program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "5.33.5"}, {"coremlc-version", "1877.0.48"}})]
{
    func main<ios16>(tensor<fp16, [2, 2048, 1, 77]> encoder_hidden_states, tensor<fp16, [2, 4, 128, 128]> sample, tensor<fp16, [2, 1280]> text_embeds, tensor<fp16, [12]> time_ids, tensor<fp16, [2]> timestep) {
            tensor<int32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<int32, []>(-1)];
            tensor<int32, [1]> var_41_axes_0 = const()[name = tensor<string, []>("op_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1]> var_41_cast = expand_dims(axes = var_41_axes_0, x = timestep)[name = tensor<string, []>("op_41_cast")];
            tensor<fp16, [1, 160]> var_43_to_fp16 = const()[name = tensor<string, []>("op_43_to_fp16"), val = tensor<fp16, [1, 160]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [2, 160]> emb_3_cast = mul(x = var_41_cast, y = var_43_to_fp16)[name = tensor<string, []>("emb_3_cast")];
            tensor<fp16, [2, 160]> var_48_cast = sin(x = emb_3_cast)[name = tensor<string, []>("op_48_cast")];
            tensor<fp16, [2, 160]> var_49_cast = cos(x = emb_3_cast)[name = tensor<string, []>("op_49_cast")];
            tensor<bool, []> emb_7_interleave_0 = const()[name = tensor<string, []>("emb_7_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> emb_7_cast = concat(axis = var_24, interleave = emb_7_interleave_0, values = (var_48_cast, var_49_cast))[name = tensor<string, []>("emb_7_cast")];
            tensor<int32, [2]> var_53_begin_0 = const()[name = tensor<string, []>("op_53_begin_0"), val = tensor<int32, [2]>([0, 160])];
            tensor<int32, [2]> var_53_end_0 = const()[name = tensor<string, []>("op_53_end_0"), val = tensor<int32, [2]>([2, 320])];
            tensor<bool, [2]> var_53_end_mask_0 = const()[name = tensor<string, []>("op_53_end_mask_0"), val = tensor<bool, [2]>([true, true])];
            tensor<fp16, [2, 160]> var_53_cast = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = emb_7_cast)[name = tensor<string, []>("op_53_cast")];
            tensor<int32, [2]> var_55_begin_0 = const()[name = tensor<string, []>("op_55_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> var_55_end_0 = const()[name = tensor<string, []>("op_55_end_0"), val = tensor<int32, [2]>([2, 160])];
            tensor<bool, [2]> var_55_end_mask_0 = const()[name = tensor<string, []>("op_55_end_mask_0"), val = tensor<bool, [2]>([true, false])];
            tensor<fp16, [2, 160]> var_55_cast = slice_by_index(begin = var_55_begin_0, end = var_55_end_0, end_mask = var_55_end_mask_0, x = emb_7_cast)[name = tensor<string, []>("op_55_cast")];
            tensor<bool, []> sample_3_interleave_0 = const()[name = tensor<string, []>("sample_3_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> sample_3_cast = concat(axis = var_24, interleave = sample_3_interleave_0, values = (var_53_cast, var_55_cast))[name = tensor<string, []>("sample_3_cast")];
            tensor<int32, []> var_58 = const()[name = tensor<string, []>("op_58"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_65_axes_0 = const()[name = tensor<string, []>("op_65_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [2, 320, 1]> var_65_cast = expand_dims(axes = var_65_axes_0, x = sample_3_cast)[name = tensor<string, []>("op_65_cast")];
            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [2, 320, 1, 1]> input_1_cast = expand_dims(axes = input_1_axes_0, x = var_65_cast)[name = tensor<string, []>("input_1_cast")];
            tensor<int32, [2]> var_69 = const()[name = tensor<string, []>("op_69"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_71 = const()[name = tensor<string, []>("op_71"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 320, 1, 1]> time_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410112))), name = tensor<string, []>("time_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 320, 1, 1])];
            tensor<fp16, [1280]> time_embedding_linear_1_bias_to_fp16 = const()[name = tensor<string, []>("time_embedding_linear_1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410688)))];
            tensor<fp16, [2, 1280, 1, 1]> input_3_cast = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_71, groups = var_58, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_69, weight = time_embedding_linear_1_weight_to_fp16_palettized, x = input_1_cast)[name = tensor<string, []>("input_3_cast")];
            tensor<fp16, [2, 1280, 1, 1]> input_5_cast = silu(x = input_3_cast)[name = tensor<string, []>("input_5_cast")];
            tensor<int32, [2]> var_77 = const()[name = tensor<string, []>("op_77"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_79 = const()[name = tensor<string, []>("op_79"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> emb_pad_type_0 = const()[name = tensor<string, []>("emb_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> emb_pad_0 = const()[name = tensor<string, []>("emb_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> time_embedding_linear_2_weight_to_fp16 = const()[name = tensor<string, []>("time_embedding_linear_2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(413312)))];
            tensor<fp16, [1280]> time_embedding_linear_2_bias_to_fp16 = const()[name = tensor<string, []>("time_embedding_linear_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3690176)))];
            tensor<fp16, [2, 1280, 1, 1]> emb_cast = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_79, groups = var_58, pad = emb_pad_0, pad_type = emb_pad_type_0, strides = var_77, weight = time_embedding_linear_2_weight_to_fp16, x = input_5_cast)[name = tensor<string, []>("emb_cast")];
            tensor<int32, []> var_85 = const()[name = tensor<string, []>("op_85"), val = tensor<int32, []>(-1)];
            tensor<int32, [1]> var_102_axes_0 = const()[name = tensor<string, []>("op_102_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [12, 1]> var_102_cast = expand_dims(axes = var_102_axes_0, x = time_ids)[name = tensor<string, []>("op_102_cast")];
            tensor<fp16, [1, 128]> var_104_to_fp16 = const()[name = tensor<string, []>("op_104_to_fp16"), val = tensor<fp16, [1, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3692800)))];
            tensor<fp16, [12, 128]> emb_11_cast = mul(x = var_102_cast, y = var_104_to_fp16)[name = tensor<string, []>("emb_11_cast")];
            tensor<fp16, [12, 128]> var_109_cast = sin(x = emb_11_cast)[name = tensor<string, []>("op_109_cast")];
            tensor<fp16, [12, 128]> var_110_cast = cos(x = emb_11_cast)[name = tensor<string, []>("op_110_cast")];
            tensor<bool, []> emb_15_interleave_0 = const()[name = tensor<string, []>("emb_15_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [12, 256]> emb_15_cast = concat(axis = var_85, interleave = emb_15_interleave_0, values = (var_109_cast, var_110_cast))[name = tensor<string, []>("emb_15_cast")];
            tensor<int32, [2]> var_114_begin_0 = const()[name = tensor<string, []>("op_114_begin_0"), val = tensor<int32, [2]>([0, 128])];
            tensor<int32, [2]> var_114_end_0 = const()[name = tensor<string, []>("op_114_end_0"), val = tensor<int32, [2]>([12, 256])];
            tensor<bool, [2]> var_114_end_mask_0 = const()[name = tensor<string, []>("op_114_end_mask_0"), val = tensor<bool, [2]>([true, true])];
            tensor<fp16, [12, 128]> var_114_cast = slice_by_index(begin = var_114_begin_0, end = var_114_end_0, end_mask = var_114_end_mask_0, x = emb_15_cast)[name = tensor<string, []>("op_114_cast")];
            tensor<int32, [2]> var_116_begin_0 = const()[name = tensor<string, []>("op_116_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> var_116_end_0 = const()[name = tensor<string, []>("op_116_end_0"), val = tensor<int32, [2]>([12, 128])];
            tensor<bool, [2]> var_116_end_mask_0 = const()[name = tensor<string, []>("op_116_end_mask_0"), val = tensor<bool, [2]>([true, false])];
            tensor<fp16, [12, 128]> var_116_cast = slice_by_index(begin = var_116_begin_0, end = var_116_end_0, end_mask = var_116_end_mask_0, x = emb_15_cast)[name = tensor<string, []>("op_116_cast")];
            tensor<bool, []> time_embeds_1_interleave_0 = const()[name = tensor<string, []>("time_embeds_1_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [12, 256]> time_embeds_1_cast = concat(axis = var_85, interleave = time_embeds_1_interleave_0, values = (var_114_cast, var_116_cast))[name = tensor<string, []>("time_embeds_1_cast")];
            tensor<int32, [2]> var_124 = const()[name = tensor<string, []>("op_124"), val = tensor<int32, [2]>([2, -1])];
            tensor<fp16, [2, 1536]> time_embeds_cast = reshape(shape = var_124, x = time_embeds_1_cast)[name = tensor<string, []>("time_embeds_cast")];
            tensor<int32, []> var_127 = const()[name = tensor<string, []>("op_127"), val = tensor<int32, []>(-1)];
            tensor<bool, []> sample_interleave_0 = const()[name = tensor<string, []>("sample_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2816]> sample_cast = concat(axis = var_127, interleave = sample_interleave_0, values = (text_embeds, time_embeds_cast))[name = tensor<string, []>("sample_cast")];
            tensor<int32, []> var_129 = const()[name = tensor<string, []>("op_129"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_136_axes_0 = const()[name = tensor<string, []>("op_136_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [2, 2816, 1]> var_136_cast = expand_dims(axes = var_136_axes_0, x = sample_cast)[name = tensor<string, []>("op_136_cast")];
            tensor<int32, [1]> input_7_axes_0 = const()[name = tensor<string, []>("input_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [2, 2816, 1, 1]> input_7_cast = expand_dims(axes = input_7_axes_0, x = var_136_cast)[name = tensor<string, []>("input_7_cast")];
            tensor<int32, [2]> var_140 = const()[name = tensor<string, []>("op_140"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_142 = const()[name = tensor<string, []>("op_142"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2816, 1, 1]> add_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3604480]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3693120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7297664))), name = tensor<string, []>("add_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2816, 1, 1])];
            tensor<fp16, [1280]> add_embedding_linear_1_bias_to_fp16 = const()[name = tensor<string, []>("add_embedding_linear_1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7298240)))];
            tensor<fp16, [2, 1280, 1, 1]> input_9_cast = conv(bias = add_embedding_linear_1_bias_to_fp16, dilations = var_142, groups = var_129, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_140, weight = add_embedding_linear_1_weight_to_fp16_palettized, x = input_7_cast)[name = tensor<string, []>("input_9_cast")];
            tensor<fp16, [2, 1280, 1, 1]> input_11_cast = silu(x = input_9_cast)[name = tensor<string, []>("input_11_cast")];
            tensor<int32, [2]> var_148 = const()[name = tensor<string, []>("op_148"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_150 = const()[name = tensor<string, []>("op_150"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> aug_emb_pad_type_0 = const()[name = tensor<string, []>("aug_emb_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> aug_emb_pad_0 = const()[name = tensor<string, []>("aug_emb_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> add_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7300864))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8939328))), name = tensor<string, []>("add_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> add_embedding_linear_2_bias_to_fp16 = const()[name = tensor<string, []>("add_embedding_linear_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8939904)))];
            tensor<fp16, [2, 1280, 1, 1]> aug_emb_cast = conv(bias = add_embedding_linear_2_bias_to_fp16, dilations = var_150, groups = var_129, pad = aug_emb_pad_0, pad_type = aug_emb_pad_type_0, strides = var_148, weight = add_embedding_linear_2_weight_to_fp16_palettized, x = input_11_cast)[name = tensor<string, []>("aug_emb_cast")];
            tensor<fp16, [2, 1280, 1, 1]> input_19_cast = add(x = emb_cast, y = aug_emb_cast)[name = tensor<string, []>("input_19_cast")];
            tensor<int32, []> var_158 = const()[name = tensor<string, []>("op_158"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_161 = const()[name = tensor<string, []>("op_161"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_163 = const()[name = tensor<string, []>("op_163"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 4, 3, 3]> conv_in_weight_to_fp16 = const()[name = tensor<string, []>("conv_in_weight_to_fp16"), val = tensor<fp16, [320, 4, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8942528)))];
            tensor<fp16, [320]> conv_in_bias_to_fp16 = const()[name = tensor<string, []>("conv_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8965632)))];
            tensor<fp16, [2, 320, 128, 128]> input_13_cast = conv(bias = conv_in_bias_to_fp16, dilations = var_163, groups = var_158, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_161, weight = conv_in_weight_to_fp16, x = sample)[name = tensor<string, []>("input_13_cast")];
            tensor<int32, []> var_172 = const()[name = tensor<string, []>("op_172"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_13_cast)[name = tensor<string, []>("reshape_0_cast")];
            tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_0_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor<string, []>("reduce_mean_0_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor<string, []>("sub_0_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_0_cast = square(x = sub_0_cast)[name = tensor<string, []>("square_0_cast")];
            tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_2_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor<string, []>("reduce_mean_2_cast")];
            tensor<fp16, []> add_0_y_0_to_fp16 = const()[name = tensor<string, []>("add_0_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor<string, []>("add_0_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor<string, []>("sqrt_0_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor<string, []>("real_div_0_cast")];
            tensor<int32, [4]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor<string, []>("reshape_1_cast")];
            tensor<fp16, [320]> add_1_mean_0_to_fp16 = const()[name = tensor<string, []>("add_1_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8966336)))];
            tensor<fp16, [320]> add_1_variance_0_to_fp16 = const()[name = tensor<string, []>("add_1_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8967040)))];
            tensor<fp16, [320]> add_1_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_1_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8967744)))];
            tensor<fp16, [320]> add_1_beta_0_to_fp16 = const()[name = tensor<string, []>("add_1_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8968448)))];
            tensor<fp16, []> add_1_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_1_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_1_cast = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast)[name = tensor<string, []>("add_1_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_17_cast = silu(x = add_1_cast)[name = tensor<string, []>("input_17_cast")];
            tensor<int32, [2]> var_190 = const()[name = tensor<string, []>("op_190"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_192 = const()[name = tensor<string, []>("op_192"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_1_pad_type_0 = const()[name = tensor<string, []>("hidden_states_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = tensor<string, []>("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8969152)))];
            tensor<fp16, [320]> down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10812416)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_1_cast = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_192, groups = var_172, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_190, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_17_cast)[name = tensor<string, []>("hidden_states_1_cast")];
            tensor<fp16, [2, 1280, 1, 1]> input_21_cast = silu(x = input_19_cast)[name = tensor<string, []>("input_21_cast")];
            tensor<int32, [2]> var_198 = const()[name = tensor<string, []>("op_198"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_200 = const()[name = tensor<string, []>("op_200"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_1_pad_type_0 = const()[name = tensor<string, []>("temb_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_1_pad_0 = const()[name = tensor<string, []>("temb_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [320, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10813120)))];
            tensor<fp16, [320]> down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11632384)))];
            tensor<fp16, [2, 320, 1, 1]> temb_1_cast = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_200, groups = var_172, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_198, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor<string, []>("temb_1_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_23_cast = add(x = hidden_states_1_cast, y = temb_1_cast)[name = tensor<string, []>("input_23_cast")];
            tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_23_cast)[name = tensor<string, []>("reshape_4_cast")];
            tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_3_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor<string, []>("reduce_mean_3_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor<string, []>("sub_2_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_1_cast = square(x = sub_2_cast)[name = tensor<string, []>("square_1_cast")];
            tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_5_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor<string, []>("reduce_mean_5_cast")];
            tensor<fp16, []> add_2_y_0_to_fp16 = const()[name = tensor<string, []>("add_2_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor<string, []>("add_2_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor<string, []>("sqrt_1_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor<string, []>("real_div_1_cast")];
            tensor<int32, [4]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor<string, []>("reshape_5_cast")];
            tensor<fp16, [320]> add_3_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_3_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11633088)))];
            tensor<fp16, [320]> add_3_beta_0_to_fp16 = const()[name = tensor<string, []>("add_3_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11633792)))];
            tensor<fp16, []> add_3_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_3_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_3_cast = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast)[name = tensor<string, []>("add_3_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_27_cast = silu(x = add_3_cast)[name = tensor<string, []>("input_27_cast")];
            tensor<int32, [2]> var_210 = const()[name = tensor<string, []>("op_210"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_212 = const()[name = tensor<string, []>("op_212"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_3_pad_type_0 = const()[name = tensor<string, []>("hidden_states_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = tensor<string, []>("hidden_states_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11634496)))];
            tensor<fp16, [320]> down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13477760)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_3_cast = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_212, groups = var_172, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_210, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_27_cast)[name = tensor<string, []>("hidden_states_3_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_29_cast = add(x = input_13_cast, y = hidden_states_3_cast)[name = tensor<string, []>("input_29_cast")];
            tensor<int32, [5]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_8_cast = reshape(shape = reshape_8_shape_0, x = input_29_cast)[name = tensor<string, []>("reshape_8_cast")];
            tensor<int32, [3]> reduce_mean_6_axes_0 = const()[name = tensor<string, []>("reduce_mean_6_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_6_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_6_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor<string, []>("reduce_mean_6_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor<string, []>("sub_4_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_2_cast = square(x = sub_4_cast)[name = tensor<string, []>("square_2_cast")];
            tensor<int32, [3]> reduce_mean_8_axes_0 = const()[name = tensor<string, []>("reduce_mean_8_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_8_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_8_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor<string, []>("reduce_mean_8_cast")];
            tensor<fp16, []> add_4_y_0_to_fp16 = const()[name = tensor<string, []>("add_4_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor<string, []>("add_4_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor<string, []>("sqrt_2_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor<string, []>("real_div_2_cast")];
            tensor<int32, [4]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor<string, []>("reshape_9_cast")];
            tensor<fp16, [320]> add_5_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_5_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13478464)))];
            tensor<fp16, [320]> add_5_beta_0_to_fp16 = const()[name = tensor<string, []>("add_5_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13479168)))];
            tensor<fp16, []> add_5_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_5_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor<string, []>("add_5_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_33_cast = silu(x = add_5_cast)[name = tensor<string, []>("input_33_cast")];
            tensor<int32, [2]> var_227 = const()[name = tensor<string, []>("op_227"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_5_pad_type_0 = const()[name = tensor<string, []>("hidden_states_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_5_pad_0 = const()[name = tensor<string, []>("hidden_states_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13479872)))];
            tensor<fp16, [320]> down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15323136)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_5_cast = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_229, groups = var_172, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_227, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_33_cast)[name = tensor<string, []>("hidden_states_5_cast")];
            tensor<int32, [2]> var_235 = const()[name = tensor<string, []>("op_235"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_237 = const()[name = tensor<string, []>("op_237"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_3_pad_type_0 = const()[name = tensor<string, []>("temb_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_3_pad_0 = const()[name = tensor<string, []>("temb_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15323840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15733504))), name = tensor<string, []>("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15734080)))];
            tensor<fp16, [2, 320, 1, 1]> temb_3_cast = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_237, groups = var_172, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_235, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_3_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_37_cast = add(x = hidden_states_5_cast, y = temb_3_cast)[name = tensor<string, []>("input_37_cast")];
            tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_37_cast)[name = tensor<string, []>("reshape_12_cast")];
            tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_9_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor<string, []>("reduce_mean_9_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor<string, []>("sub_6_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_3_cast = square(x = sub_6_cast)[name = tensor<string, []>("square_3_cast")];
            tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_11_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor<string, []>("reduce_mean_11_cast")];
            tensor<fp16, []> add_6_y_0_to_fp16 = const()[name = tensor<string, []>("add_6_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor<string, []>("add_6_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor<string, []>("sqrt_3_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor<string, []>("real_div_3_cast")];
            tensor<int32, [4]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor<string, []>("reshape_13_cast")];
            tensor<fp16, [320]> add_7_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_7_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15734784)))];
            tensor<fp16, [320]> add_7_beta_0_to_fp16 = const()[name = tensor<string, []>("add_7_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15735488)))];
            tensor<fp16, []> add_7_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_7_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_7_cast = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast)[name = tensor<string, []>("add_7_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_41_cast = silu(x = add_7_cast)[name = tensor<string, []>("input_41_cast")];
            tensor<int32, [2]> var_247 = const()[name = tensor<string, []>("op_247"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_249 = const()[name = tensor<string, []>("op_249"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_7_pad_type_0 = const()[name = tensor<string, []>("hidden_states_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = tensor<string, []>("hidden_states_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15736192)))];
            tensor<fp16, [320]> down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17579456)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_7_cast = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_249, groups = var_172, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_247, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_41_cast)[name = tensor<string, []>("hidden_states_7_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_43_cast = add(x = input_29_cast, y = hidden_states_7_cast)[name = tensor<string, []>("input_43_cast")];
            tensor<int32, [2]> var_256 = const()[name = tensor<string, []>("op_256"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_258 = const()[name = tensor<string, []>("op_258"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [921600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17580160))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18501824))), name = tensor<string, []>("down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            tensor<fp16, [320]> down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18502400)))];
            tensor<fp16, [2, 320, 64, 64]> input_45_cast = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_258, groups = var_172, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_256, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized, x = input_43_cast)[name = tensor<string, []>("input_45_cast")];
            tensor<int32, []> var_266 = const()[name = tensor<string, []>("op_266"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_277 = const()[name = tensor<string, []>("op_277"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_282 = const()[name = tensor<string, []>("op_282"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_45_cast)[name = tensor<string, []>("reshape_16_cast")];
            tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = tensor<string, []>("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_12_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_12_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_12_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor<string, []>("reduce_mean_12_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor<string, []>("sub_8_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_4_cast = square(x = sub_8_cast)[name = tensor<string, []>("square_4_cast")];
            tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = tensor<string, []>("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_14_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_14_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_14_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor<string, []>("reduce_mean_14_cast")];
            tensor<fp16, []> add_8_y_0_to_fp16 = const()[name = tensor<string, []>("add_8_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor<string, []>("add_8_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor<string, []>("sqrt_4_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor<string, []>("real_div_4_cast")];
            tensor<int32, [4]> reshape_17_shape_0 = const()[name = tensor<string, []>("reshape_17_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor<string, []>("reshape_17_cast")];
            tensor<fp16, [320]> add_9_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_9_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18503104)))];
            tensor<fp16, [320]> add_9_beta_0_to_fp16 = const()[name = tensor<string, []>("add_9_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18503808)))];
            tensor<fp16, []> add_9_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_9_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_9_cast = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast)[name = tensor<string, []>("add_9_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_49_cast = silu(x = add_9_cast)[name = tensor<string, []>("input_49_cast")];
            tensor<int32, [2]> var_305 = const()[name = tensor<string, []>("op_305"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_307 = const()[name = tensor<string, []>("op_307"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_9_pad_type_0 = const()[name = tensor<string, []>("hidden_states_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_9_pad_0 = const()[name = tensor<string, []>("hidden_states_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 320, 3, 3]> down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1843200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18504512))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20347776))), name = tensor<string, []>("down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 320, 3, 3])];
            tensor<fp16, [640]> down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20348352)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_9_cast = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_307, groups = var_282, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_305, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_49_cast)[name = tensor<string, []>("hidden_states_9_cast")];
            tensor<int32, [2]> var_313 = const()[name = tensor<string, []>("op_313"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_315 = const()[name = tensor<string, []>("op_315"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_5_pad_type_0 = const()[name = tensor<string, []>("temb_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_5_pad_0 = const()[name = tensor<string, []>("temb_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [614400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20349696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20964160))), name = tensor<string, []>("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20964352)))];
            tensor<fp16, [2, 640, 1, 1]> temb_5_cast = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_315, groups = var_282, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_313, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_5_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_53_cast = add(x = hidden_states_9_cast, y = temb_5_cast)[name = tensor<string, []>("input_53_cast")];
            tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_20_cast = reshape(shape = reshape_20_shape_0, x = input_53_cast)[name = tensor<string, []>("reshape_20_cast")];
            tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = tensor<string, []>("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_15_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_15_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor<string, []>("reduce_mean_15_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor<string, []>("sub_10_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_5_cast = square(x = sub_10_cast)[name = tensor<string, []>("square_5_cast")];
            tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = tensor<string, []>("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_17_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_17_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor<string, []>("reduce_mean_17_cast")];
            tensor<fp16, []> add_10_y_0_to_fp16 = const()[name = tensor<string, []>("add_10_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor<string, []>("add_10_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor<string, []>("sqrt_5_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor<string, []>("real_div_5_cast")];
            tensor<int32, [4]> reshape_21_shape_0 = const()[name = tensor<string, []>("reshape_21_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor<string, []>("reshape_21_cast")];
            tensor<fp16, [640]> add_11_mean_0_to_fp16 = const()[name = tensor<string, []>("add_11_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20965696)))];
            tensor<fp16, [640]> add_11_variance_0_to_fp16 = const()[name = tensor<string, []>("add_11_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20967040)))];
            tensor<fp16, [640]> add_11_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_11_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20968384)))];
            tensor<fp16, [640]> add_11_beta_0_to_fp16 = const()[name = tensor<string, []>("add_11_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20969728)))];
            tensor<fp16, []> add_11_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_11_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast)[name = tensor<string, []>("add_11_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_57_cast = silu(x = add_11_cast)[name = tensor<string, []>("input_57_cast")];
            tensor<int32, [2]> var_325 = const()[name = tensor<string, []>("op_325"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_327 = const()[name = tensor<string, []>("op_327"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_11_pad_type_0 = const()[name = tensor<string, []>("hidden_states_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_11_pad_0 = const()[name = tensor<string, []>("hidden_states_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3686400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20971072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24657536))), name = tensor<string, []>("down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24658112)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_11_cast = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_327, groups = var_282, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_325, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_57_cast)[name = tensor<string, []>("hidden_states_11_cast")];
            tensor<int32, [2]> var_332 = const()[name = tensor<string, []>("op_332"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_334 = const()[name = tensor<string, []>("op_334"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_1_pad_type_0 = const()[name = tensor<string, []>("x_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_1_pad_0 = const()[name = tensor<string, []>("x_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 320, 1, 1]> down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24659456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24864320))), name = tensor<string, []>("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 320, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24864896)))];
            tensor<fp16, [2, 640, 64, 64]> x_1_cast = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_334, groups = var_282, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_332, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_45_cast)[name = tensor<string, []>("x_1_cast")];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_13_cast = add(x = x_1_cast, y = hidden_states_11_cast)[name = tensor<string, []>("hidden_states_13_cast")];
            tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_24_cast = reshape(shape = reshape_24_shape_0, x = hidden_states_13_cast)[name = tensor<string, []>("reshape_24_cast")];
            tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = tensor<string, []>("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_18_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_18_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_18_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor<string, []>("reduce_mean_18_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor<string, []>("sub_12_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_6_cast = square(x = sub_12_cast)[name = tensor<string, []>("square_6_cast")];
            tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = tensor<string, []>("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_20_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_20_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_20_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor<string, []>("reduce_mean_20_cast")];
            tensor<fp16, []> add_12_y_0_to_fp16 = const()[name = tensor<string, []>("add_12_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor<string, []>("add_12_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor<string, []>("sqrt_6_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor<string, []>("real_div_6_cast")];
            tensor<int32, [4]> reshape_25_shape_0 = const()[name = tensor<string, []>("reshape_25_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor<string, []>("reshape_25_cast")];
            tensor<fp16, [640]> add_13_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_13_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24866240)))];
            tensor<fp16, [640]> add_13_beta_0_to_fp16 = const()[name = tensor<string, []>("add_13_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24867584)))];
            tensor<fp16, []> add_13_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_13_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_13_cast = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast)[name = tensor<string, []>("add_13_cast")];
            tensor<int32, [2]> var_356 = const()[name = tensor<string, []>("op_356"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_358 = const()[name = tensor<string, []>("op_358"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_15_pad_type_0 = const()[name = tensor<string, []>("hidden_states_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_15_pad_0 = const()[name = tensor<string, []>("hidden_states_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24868928))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25278592))), name = tensor<string, []>("down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25279168)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_15_cast = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_358, groups = var_282, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_356, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_13_cast)[name = tensor<string, []>("hidden_states_15_cast")];
            tensor<int32, [4]> var_363 = const()[name = tensor<string, []>("op_363"), val = tensor<int32, [4]>([2, 640, 1, 4096])];
            tensor<fp16, [2, 640, 1, 4096]> inputs_1_cast = reshape(shape = var_363, x = hidden_states_15_cast)[name = tensor<string, []>("inputs_1_cast")];
            tensor<int32, [1]> var_373 = const()[name = tensor<string, []>("op_373"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_1_cast = reduce_mean(axes = var_373, keep_dims = var_277, x = inputs_1_cast)[name = tensor<string, []>("channels_mean_1_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast)[name = tensor<string, []>("zero_mean_1_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast)[name = tensor<string, []>("zero_mean_sq_1_cast")];
            tensor<int32, [1]> var_377 = const()[name = tensor<string, []>("op_377"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_378_cast = reduce_mean(axes = var_377, keep_dims = var_277, x = zero_mean_sq_1_cast)[name = tensor<string, []>("op_378_cast")];
            tensor<fp16, []> var_379_to_fp16 = const()[name = tensor<string, []>("op_379_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_380_cast = add(x = var_378_cast, y = var_379_to_fp16)[name = tensor<string, []>("op_380_cast")];
            tensor<fp16, []> denom_1_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_1_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_1_cast = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_380_cast)[name = tensor<string, []>("denom_1_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast)[name = tensor<string, []>("out_1_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_384_to_fp16 = const()[name = tensor<string, []>("op_384_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25280512)))];
            tensor<fp16, [2, 640, 1, 4096]> var_385_cast = add(x = out_1_cast, y = var_384_to_fp16)[name = tensor<string, []>("op_385_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_387_to_fp16 = const()[name = tensor<string, []>("op_387_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25281856)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_17_cast = mul(x = var_385_cast, y = var_387_to_fp16)[name = tensor<string, []>("hidden_states_17_cast")];
            tensor<int32, [2]> var_394 = const()[name = tensor<string, []>("op_394"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_396 = const()[name = tensor<string, []>("op_396"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_1_pad_type_0 = const()[name = tensor<string, []>("q_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_1_pad_0 = const()[name = tensor<string, []>("q_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25283200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25590464))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_1_cast = conv(dilations = var_396, groups = var_282, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_394, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_17_cast)[name = tensor<string, []>("q_1_cast")];
            tensor<int32, [2]> var_400 = const()[name = tensor<string, []>("op_400"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_402 = const()[name = tensor<string, []>("op_402"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_1_pad_type_0 = const()[name = tensor<string, []>("k_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_1_pad_0 = const()[name = tensor<string, []>("k_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25590656))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25897920))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_1_cast = conv(dilations = var_402, groups = var_282, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_400, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_17_cast)[name = tensor<string, []>("k_1_cast")];
            tensor<int32, [2]> var_406 = const()[name = tensor<string, []>("op_406"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_408 = const()[name = tensor<string, []>("op_408"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_1_pad_type_0 = const()[name = tensor<string, []>("v_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_1_pad_0 = const()[name = tensor<string, []>("v_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25898112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26307776))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_1_cast = conv(dilations = var_408, groups = var_282, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_406, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_17_cast)[name = tensor<string, []>("v_1_cast")];
            tensor<int32, [4]> var_412 = const()[name = tensor<string, []>("op_412"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_413_cast = reshape(shape = var_412, x = q_1_cast)[name = tensor<string, []>("op_413_cast")];
            tensor<int32, [4]> var_414 = const()[name = tensor<string, []>("op_414"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_415_cast = reshape(shape = var_414, x = k_1_cast)[name = tensor<string, []>("op_415_cast")];
            tensor<int32, [4]> var_416 = const()[name = tensor<string, []>("op_416"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_417_cast = reshape(shape = var_416, x = v_1_cast)[name = tensor<string, []>("op_417_cast")];
            tensor<bool, []> attn_weights_1_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_1_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_413_cast, y = var_415_cast)[name = tensor<string, []>("attn_weights_1_cast")];
            tensor<fp16, []> var_273_to_fp16 = const()[name = tensor<string, []>("op_273_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_3_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_421_cast = softmax(axis = var_266, x = attn_weights_3_cast)[name = tensor<string, []>("op_421_cast")];
            tensor<bool, []> attn_1_transpose_x_0 = const()[name = tensor<string, []>("attn_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_1_transpose_y_0 = const()[name = tensor<string, []>("attn_1_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_1_cast = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_417_cast, y = var_421_cast)[name = tensor<string, []>("attn_1_cast")];
            tensor<int32, [4]> var_425 = const()[name = tensor<string, []>("op_425"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_61_cast = reshape(shape = var_425, x = attn_1_cast)[name = tensor<string, []>("input_61_cast")];
            tensor<int32, [2]> var_430 = const()[name = tensor<string, []>("op_430"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_432 = const()[name = tensor<string, []>("op_432"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_434_pad_type_0 = const()[name = tensor<string, []>("op_434_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_434_pad_0 = const()[name = tensor<string, []>("op_434_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26308352))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26718016))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26718592)))];
            tensor<fp16, [2, 640, 1, 4096]> var_434_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_432, groups = var_282, pad = var_434_pad_0, pad_type = var_434_pad_type_0, strides = var_430, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_61_cast)[name = tensor<string, []>("op_434_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_3_cast = add(x = var_434_cast, y = inputs_1_cast)[name = tensor<string, []>("inputs_3_cast")];
            tensor<int32, [1]> var_438 = const()[name = tensor<string, []>("op_438"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_3_cast = reduce_mean(axes = var_438, keep_dims = var_277, x = inputs_3_cast)[name = tensor<string, []>("channels_mean_3_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast)[name = tensor<string, []>("zero_mean_3_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast)[name = tensor<string, []>("zero_mean_sq_3_cast")];
            tensor<int32, [1]> var_442 = const()[name = tensor<string, []>("op_442"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_443_cast = reduce_mean(axes = var_442, keep_dims = var_277, x = zero_mean_sq_3_cast)[name = tensor<string, []>("op_443_cast")];
            tensor<fp16, []> var_444_to_fp16 = const()[name = tensor<string, []>("op_444_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_445_cast = add(x = var_443_cast, y = var_444_to_fp16)[name = tensor<string, []>("op_445_cast")];
            tensor<fp16, []> denom_3_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_3_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_3_cast = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_445_cast)[name = tensor<string, []>("denom_3_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast)[name = tensor<string, []>("out_3_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_449_to_fp16 = const()[name = tensor<string, []>("op_449_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26719936)))];
            tensor<fp16, [2, 640, 1, 4096]> var_450_cast = add(x = out_3_cast, y = var_449_to_fp16)[name = tensor<string, []>("op_450_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_452_to_fp16 = const()[name = tensor<string, []>("op_452_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26721280)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_19_cast = mul(x = var_450_cast, y = var_452_to_fp16)[name = tensor<string, []>("hidden_states_19_cast")];
            tensor<int32, [2]> var_459 = const()[name = tensor<string, []>("op_459"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_461 = const()[name = tensor<string, []>("op_461"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_3_pad_type_0 = const()[name = tensor<string, []>("q_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_3_pad_0 = const()[name = tensor<string, []>("q_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26722624))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26927488))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_3_cast = conv(dilations = var_461, groups = var_282, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_459, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("q_3_cast")];
            tensor<int32, [2]> var_465 = const()[name = tensor<string, []>("op_465"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_467 = const()[name = tensor<string, []>("op_467"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_3_pad_type_0 = const()[name = tensor<string, []>("k_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_3_pad_0 = const()[name = tensor<string, []>("k_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26927616))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27910720))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_3_cast = conv(dilations = var_467, groups = var_282, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_465, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_3_cast")];
            tensor<int32, [2]> var_471 = const()[name = tensor<string, []>("op_471"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_473 = const()[name = tensor<string, []>("op_473"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_3_pad_type_0 = const()[name = tensor<string, []>("v_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_3_pad_0 = const()[name = tensor<string, []>("v_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27910912))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28894016))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_3_cast = conv(dilations = var_473, groups = var_282, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_471, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_3_cast")];
            tensor<int32, [4]> var_477 = const()[name = tensor<string, []>("op_477"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_478_cast = reshape(shape = var_477, x = q_3_cast)[name = tensor<string, []>("op_478_cast")];
            tensor<int32, [4]> var_479 = const()[name = tensor<string, []>("op_479"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_480_cast = reshape(shape = var_479, x = k_3_cast)[name = tensor<string, []>("op_480_cast")];
            tensor<int32, [4]> var_481 = const()[name = tensor<string, []>("op_481"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_482_cast = reshape(shape = var_481, x = v_3_cast)[name = tensor<string, []>("op_482_cast")];
            tensor<bool, []> attn_weights_5_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_5_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_5_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_5_cast = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_478_cast, y = var_480_cast)[name = tensor<string, []>("attn_weights_5_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_7_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_486_cast = softmax(axis = var_266, x = attn_weights_7_cast)[name = tensor<string, []>("op_486_cast")];
            tensor<bool, []> attn_3_transpose_x_0 = const()[name = tensor<string, []>("attn_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_3_transpose_y_0 = const()[name = tensor<string, []>("attn_3_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_3_cast = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_482_cast, y = var_486_cast)[name = tensor<string, []>("attn_3_cast")];
            tensor<int32, [4]> var_490 = const()[name = tensor<string, []>("op_490"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_63_cast = reshape(shape = var_490, x = attn_3_cast)[name = tensor<string, []>("input_63_cast")];
            tensor<int32, [2]> var_495 = const()[name = tensor<string, []>("op_495"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_497 = const()[name = tensor<string, []>("op_497"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_499_pad_type_0 = const()[name = tensor<string, []>("op_499_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_499_pad_0 = const()[name = tensor<string, []>("op_499_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28894208))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29201472))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29201664)))];
            tensor<fp16, [2, 640, 1, 4096]> var_499_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_497, groups = var_282, pad = var_499_pad_0, pad_type = var_499_pad_type_0, strides = var_495, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_63_cast)[name = tensor<string, []>("op_499_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_5_cast = add(x = var_499_cast, y = inputs_3_cast)[name = tensor<string, []>("inputs_5_cast")];
            tensor<int32, [1]> var_503 = const()[name = tensor<string, []>("op_503"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_5_cast = reduce_mean(axes = var_503, keep_dims = var_277, x = inputs_5_cast)[name = tensor<string, []>("channels_mean_5_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast)[name = tensor<string, []>("zero_mean_5_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast)[name = tensor<string, []>("zero_mean_sq_5_cast")];
            tensor<int32, [1]> var_507 = const()[name = tensor<string, []>("op_507"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_508_cast = reduce_mean(axes = var_507, keep_dims = var_277, x = zero_mean_sq_5_cast)[name = tensor<string, []>("op_508_cast")];
            tensor<fp16, []> var_509_to_fp16 = const()[name = tensor<string, []>("op_509_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_510_cast = add(x = var_508_cast, y = var_509_to_fp16)[name = tensor<string, []>("op_510_cast")];
            tensor<fp16, []> denom_5_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_5_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_5_cast = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_510_cast)[name = tensor<string, []>("denom_5_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast)[name = tensor<string, []>("out_5_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_514_to_fp16 = const()[name = tensor<string, []>("op_514_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29203008)))];
            tensor<fp16, [2, 640, 1, 4096]> var_515_cast = add(x = out_5_cast, y = var_514_to_fp16)[name = tensor<string, []>("op_515_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_517_to_fp16 = const()[name = tensor<string, []>("op_517_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29204352)))];
            tensor<fp16, [2, 640, 1, 4096]> input_65_cast = mul(x = var_515_cast, y = var_517_to_fp16)[name = tensor<string, []>("input_65_cast")];
            tensor<int32, [2]> var_525 = const()[name = tensor<string, []>("op_525"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_527 = const()[name = tensor<string, []>("op_527"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_529_pad_type_0 = const()[name = tensor<string, []>("op_529_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_529_pad_0 = const()[name = tensor<string, []>("op_529_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29205696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32482560))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32483136)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_529_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_527, groups = var_282, pad = var_529_pad_0, pad_type = var_529_pad_type_0, strides = var_525, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_65_cast)[name = tensor<string, []>("op_529_cast")];
            tensor<int32, [2]> var_530_split_sizes_0 = const()[name = tensor<string, []>("op_530_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_530_axis_0 = const()[name = tensor<string, []>("op_530_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_530_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_530_cast_1 = split(axis = var_530_axis_0, split_sizes = var_530_split_sizes_0, x = var_529_cast)[name = tensor<string, []>("op_530_cast")];
            tensor<string, []> var_532_mode_0 = const()[name = tensor<string, []>("op_532_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_532_cast = gelu(mode = var_532_mode_0, x = var_530_cast_1)[name = tensor<string, []>("op_532_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_67_cast = mul(x = var_530_cast_0, y = var_532_cast)[name = tensor<string, []>("input_67_cast")];
            tensor<int32, [2]> var_536 = const()[name = tensor<string, []>("op_536"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_538 = const()[name = tensor<string, []>("op_538"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_540_pad_type_0 = const()[name = tensor<string, []>("op_540_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_540_pad_0 = const()[name = tensor<string, []>("op_540_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32493440))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34131904))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34132480)))];
            tensor<fp16, [2, 640, 1, 4096]> var_540_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_538, groups = var_282, pad = var_540_pad_0, pad_type = var_540_pad_type_0, strides = var_536, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_67_cast)[name = tensor<string, []>("op_540_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_7_cast = add(x = var_540_cast, y = inputs_5_cast)[name = tensor<string, []>("inputs_7_cast")];
            tensor<int32, [1]> var_550 = const()[name = tensor<string, []>("op_550"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_7_cast = reduce_mean(axes = var_550, keep_dims = var_277, x = inputs_7_cast)[name = tensor<string, []>("channels_mean_7_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast)[name = tensor<string, []>("zero_mean_7_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast)[name = tensor<string, []>("zero_mean_sq_7_cast")];
            tensor<int32, [1]> var_554 = const()[name = tensor<string, []>("op_554"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_555_cast = reduce_mean(axes = var_554, keep_dims = var_277, x = zero_mean_sq_7_cast)[name = tensor<string, []>("op_555_cast")];
            tensor<fp16, []> var_556_to_fp16 = const()[name = tensor<string, []>("op_556_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_557_cast = add(x = var_555_cast, y = var_556_to_fp16)[name = tensor<string, []>("op_557_cast")];
            tensor<fp16, []> denom_7_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_7_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_7_cast = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_557_cast)[name = tensor<string, []>("denom_7_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast)[name = tensor<string, []>("out_7_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_561_to_fp16 = const()[name = tensor<string, []>("op_561_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34133824)))];
            tensor<fp16, [2, 640, 1, 4096]> var_562_cast = add(x = out_7_cast, y = var_561_to_fp16)[name = tensor<string, []>("op_562_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_564_to_fp16 = const()[name = tensor<string, []>("op_564_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34135168)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_23_cast = mul(x = var_562_cast, y = var_564_to_fp16)[name = tensor<string, []>("hidden_states_23_cast")];
            tensor<int32, [2]> var_571 = const()[name = tensor<string, []>("op_571"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_573 = const()[name = tensor<string, []>("op_573"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_5_pad_type_0 = const()[name = tensor<string, []>("q_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_5_pad_0 = const()[name = tensor<string, []>("q_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34136512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34443776))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_5_cast = conv(dilations = var_573, groups = var_282, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_571, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_23_cast)[name = tensor<string, []>("q_5_cast")];
            tensor<int32, [2]> var_577 = const()[name = tensor<string, []>("op_577"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_579 = const()[name = tensor<string, []>("op_579"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_5_pad_type_0 = const()[name = tensor<string, []>("k_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_5_pad_0 = const()[name = tensor<string, []>("k_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34443968))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34751232))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_5_cast = conv(dilations = var_579, groups = var_282, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_577, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_23_cast)[name = tensor<string, []>("k_5_cast")];
            tensor<int32, [2]> var_583 = const()[name = tensor<string, []>("op_583"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_585 = const()[name = tensor<string, []>("op_585"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_5_pad_type_0 = const()[name = tensor<string, []>("v_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_5_pad_0 = const()[name = tensor<string, []>("v_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34751424))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35161088))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_5_cast = conv(dilations = var_585, groups = var_282, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_583, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_23_cast)[name = tensor<string, []>("v_5_cast")];
            tensor<int32, [4]> var_589 = const()[name = tensor<string, []>("op_589"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_590_cast = reshape(shape = var_589, x = q_5_cast)[name = tensor<string, []>("op_590_cast")];
            tensor<int32, [4]> var_591 = const()[name = tensor<string, []>("op_591"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_592_cast = reshape(shape = var_591, x = k_5_cast)[name = tensor<string, []>("op_592_cast")];
            tensor<int32, [4]> var_593 = const()[name = tensor<string, []>("op_593"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_594_cast = reshape(shape = var_593, x = v_5_cast)[name = tensor<string, []>("op_594_cast")];
            tensor<bool, []> attn_weights_9_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_9_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_9_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_9_cast = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_590_cast, y = var_592_cast)[name = tensor<string, []>("attn_weights_9_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_11_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_598_cast = softmax(axis = var_266, x = attn_weights_11_cast)[name = tensor<string, []>("op_598_cast")];
            tensor<bool, []> attn_5_transpose_x_0 = const()[name = tensor<string, []>("attn_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_5_transpose_y_0 = const()[name = tensor<string, []>("attn_5_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_5_cast = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_594_cast, y = var_598_cast)[name = tensor<string, []>("attn_5_cast")];
            tensor<int32, [4]> var_602 = const()[name = tensor<string, []>("op_602"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_69_cast = reshape(shape = var_602, x = attn_5_cast)[name = tensor<string, []>("input_69_cast")];
            tensor<int32, [2]> var_607 = const()[name = tensor<string, []>("op_607"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_609 = const()[name = tensor<string, []>("op_609"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_611_pad_type_0 = const()[name = tensor<string, []>("op_611_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_611_pad_0 = const()[name = tensor<string, []>("op_611_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35161664))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35571328))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35571904)))];
            tensor<fp16, [2, 640, 1, 4096]> var_611_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_609, groups = var_282, pad = var_611_pad_0, pad_type = var_611_pad_type_0, strides = var_607, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_69_cast)[name = tensor<string, []>("op_611_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_9_cast = add(x = var_611_cast, y = inputs_7_cast)[name = tensor<string, []>("inputs_9_cast")];
            tensor<int32, [1]> var_615 = const()[name = tensor<string, []>("op_615"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_9_cast = reduce_mean(axes = var_615, keep_dims = var_277, x = inputs_9_cast)[name = tensor<string, []>("channels_mean_9_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast)[name = tensor<string, []>("zero_mean_9_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast)[name = tensor<string, []>("zero_mean_sq_9_cast")];
            tensor<int32, [1]> var_619 = const()[name = tensor<string, []>("op_619"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_620_cast = reduce_mean(axes = var_619, keep_dims = var_277, x = zero_mean_sq_9_cast)[name = tensor<string, []>("op_620_cast")];
            tensor<fp16, []> var_621_to_fp16 = const()[name = tensor<string, []>("op_621_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_622_cast = add(x = var_620_cast, y = var_621_to_fp16)[name = tensor<string, []>("op_622_cast")];
            tensor<fp16, []> denom_9_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_9_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_9_cast = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_622_cast)[name = tensor<string, []>("denom_9_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast)[name = tensor<string, []>("out_9_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_626_to_fp16 = const()[name = tensor<string, []>("op_626_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35573248)))];
            tensor<fp16, [2, 640, 1, 4096]> var_627_cast = add(x = out_9_cast, y = var_626_to_fp16)[name = tensor<string, []>("op_627_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_629_to_fp16 = const()[name = tensor<string, []>("op_629_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35574592)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_25_cast = mul(x = var_627_cast, y = var_629_to_fp16)[name = tensor<string, []>("hidden_states_25_cast")];
            tensor<int32, [2]> var_636 = const()[name = tensor<string, []>("op_636"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_638 = const()[name = tensor<string, []>("op_638"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_7_pad_type_0 = const()[name = tensor<string, []>("q_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_7_pad_0 = const()[name = tensor<string, []>("q_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35575936))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35883200))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_7_cast = conv(dilations = var_638, groups = var_282, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_636, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("q_7_cast")];
            tensor<int32, [2]> var_642 = const()[name = tensor<string, []>("op_642"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_644 = const()[name = tensor<string, []>("op_644"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_7_pad_type_0 = const()[name = tensor<string, []>("k_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_7_pad_0 = const()[name = tensor<string, []>("k_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35883392))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36866496))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_7_cast = conv(dilations = var_644, groups = var_282, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_642, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_7_cast")];
            tensor<int32, [2]> var_648 = const()[name = tensor<string, []>("op_648"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_650 = const()[name = tensor<string, []>("op_650"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_7_pad_type_0 = const()[name = tensor<string, []>("v_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_7_pad_0 = const()[name = tensor<string, []>("v_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36866688))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37849792))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_7_cast = conv(dilations = var_650, groups = var_282, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_648, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_7_cast")];
            tensor<int32, [4]> var_654 = const()[name = tensor<string, []>("op_654"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_655_cast = reshape(shape = var_654, x = q_7_cast)[name = tensor<string, []>("op_655_cast")];
            tensor<int32, [4]> var_656 = const()[name = tensor<string, []>("op_656"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_657_cast = reshape(shape = var_656, x = k_7_cast)[name = tensor<string, []>("op_657_cast")];
            tensor<int32, [4]> var_658 = const()[name = tensor<string, []>("op_658"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_659_cast = reshape(shape = var_658, x = v_7_cast)[name = tensor<string, []>("op_659_cast")];
            tensor<bool, []> attn_weights_13_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_13_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_655_cast, y = var_657_cast)[name = tensor<string, []>("attn_weights_13_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_15_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_663_cast = softmax(axis = var_266, x = attn_weights_15_cast)[name = tensor<string, []>("op_663_cast")];
            tensor<bool, []> attn_7_transpose_x_0 = const()[name = tensor<string, []>("attn_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_7_transpose_y_0 = const()[name = tensor<string, []>("attn_7_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_7_cast = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_659_cast, y = var_663_cast)[name = tensor<string, []>("attn_7_cast")];
            tensor<int32, [4]> var_667 = const()[name = tensor<string, []>("op_667"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_71_cast = reshape(shape = var_667, x = attn_7_cast)[name = tensor<string, []>("input_71_cast")];
            tensor<int32, [2]> var_672 = const()[name = tensor<string, []>("op_672"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_674 = const()[name = tensor<string, []>("op_674"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_676_pad_type_0 = const()[name = tensor<string, []>("op_676_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_676_pad_0 = const()[name = tensor<string, []>("op_676_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37849984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38157248))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38157440)))];
            tensor<fp16, [2, 640, 1, 4096]> var_676_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_674, groups = var_282, pad = var_676_pad_0, pad_type = var_676_pad_type_0, strides = var_672, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_71_cast)[name = tensor<string, []>("op_676_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_11_cast = add(x = var_676_cast, y = inputs_9_cast)[name = tensor<string, []>("inputs_11_cast")];
            tensor<int32, [1]> var_680 = const()[name = tensor<string, []>("op_680"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_11_cast = reduce_mean(axes = var_680, keep_dims = var_277, x = inputs_11_cast)[name = tensor<string, []>("channels_mean_11_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast)[name = tensor<string, []>("zero_mean_11_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast)[name = tensor<string, []>("zero_mean_sq_11_cast")];
            tensor<int32, [1]> var_684 = const()[name = tensor<string, []>("op_684"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_685_cast = reduce_mean(axes = var_684, keep_dims = var_277, x = zero_mean_sq_11_cast)[name = tensor<string, []>("op_685_cast")];
            tensor<fp16, []> var_686_to_fp16 = const()[name = tensor<string, []>("op_686_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_687_cast = add(x = var_685_cast, y = var_686_to_fp16)[name = tensor<string, []>("op_687_cast")];
            tensor<fp16, []> denom_11_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_11_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_11_cast = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_687_cast)[name = tensor<string, []>("denom_11_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast)[name = tensor<string, []>("out_11_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_691_to_fp16 = const()[name = tensor<string, []>("op_691_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38158784)))];
            tensor<fp16, [2, 640, 1, 4096]> var_692_cast = add(x = out_11_cast, y = var_691_to_fp16)[name = tensor<string, []>("op_692_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_694_to_fp16 = const()[name = tensor<string, []>("op_694_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38160128)))];
            tensor<fp16, [2, 640, 1, 4096]> input_73_cast = mul(x = var_692_cast, y = var_694_to_fp16)[name = tensor<string, []>("input_73_cast")];
            tensor<int32, [2]> var_702 = const()[name = tensor<string, []>("op_702"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_704 = const()[name = tensor<string, []>("op_704"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_706_pad_type_0 = const()[name = tensor<string, []>("op_706_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_706_pad_0 = const()[name = tensor<string, []>("op_706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38161472))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41438336))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41438912)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_706_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_704, groups = var_282, pad = var_706_pad_0, pad_type = var_706_pad_type_0, strides = var_702, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_73_cast)[name = tensor<string, []>("op_706_cast")];
            tensor<int32, [2]> var_707_split_sizes_0 = const()[name = tensor<string, []>("op_707_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_707_axis_0 = const()[name = tensor<string, []>("op_707_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_707_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_707_cast_1 = split(axis = var_707_axis_0, split_sizes = var_707_split_sizes_0, x = var_706_cast)[name = tensor<string, []>("op_707_cast")];
            tensor<string, []> var_709_mode_0 = const()[name = tensor<string, []>("op_709_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_709_cast = gelu(mode = var_709_mode_0, x = var_707_cast_1)[name = tensor<string, []>("op_709_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_75_cast = mul(x = var_707_cast_0, y = var_709_cast)[name = tensor<string, []>("input_75_cast")];
            tensor<int32, [2]> var_713 = const()[name = tensor<string, []>("op_713"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_715 = const()[name = tensor<string, []>("op_715"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_717_pad_type_0 = const()[name = tensor<string, []>("op_717_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_717_pad_0 = const()[name = tensor<string, []>("op_717_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41449216))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43087680))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43088256)))];
            tensor<fp16, [2, 640, 1, 4096]> var_717_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_715, groups = var_282, pad = var_717_pad_0, pad_type = var_717_pad_type_0, strides = var_713, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_75_cast)[name = tensor<string, []>("op_717_cast")];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_29_cast = add(x = var_717_cast, y = inputs_11_cast)[name = tensor<string, []>("hidden_states_29_cast")];
            tensor<int32, [4]> var_719 = const()[name = tensor<string, []>("op_719"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> input_77_cast = reshape(shape = var_719, x = hidden_states_29_cast)[name = tensor<string, []>("input_77_cast")];
            tensor<int32, [2]> var_723 = const()[name = tensor<string, []>("op_723"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_725 = const()[name = tensor<string, []>("op_725"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_31_pad_type_0 = const()[name = tensor<string, []>("hidden_states_31_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = tensor<string, []>("hidden_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43089600))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43499264))), name = tensor<string, []>("down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43499840)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_31_cast = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_725, groups = var_282, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_723, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_77_cast)[name = tensor<string, []>("hidden_states_31_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_79_cast = add(x = hidden_states_31_cast, y = hidden_states_13_cast)[name = tensor<string, []>("input_79_cast")];
            tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_79_cast)[name = tensor<string, []>("reshape_28_cast")];
            tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = tensor<string, []>("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_21_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_21_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_21_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor<string, []>("reduce_mean_21_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor<string, []>("sub_14_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_7_cast = square(x = sub_14_cast)[name = tensor<string, []>("square_7_cast")];
            tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = tensor<string, []>("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_23_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_23_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_23_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor<string, []>("reduce_mean_23_cast")];
            tensor<fp16, []> add_14_y_0_to_fp16 = const()[name = tensor<string, []>("add_14_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor<string, []>("add_14_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor<string, []>("sqrt_7_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor<string, []>("real_div_7_cast")];
            tensor<int32, [4]> reshape_29_shape_0 = const()[name = tensor<string, []>("reshape_29_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor<string, []>("reshape_29_cast")];
            tensor<fp16, [640]> add_15_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_15_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43501184)))];
            tensor<fp16, [640]> add_15_beta_0_to_fp16 = const()[name = tensor<string, []>("add_15_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43502528)))];
            tensor<fp16, []> add_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_15_cast = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast)[name = tensor<string, []>("add_15_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_83_cast = silu(x = add_15_cast)[name = tensor<string, []>("input_83_cast")];
            tensor<int32, [2]> var_740 = const()[name = tensor<string, []>("op_740"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_742 = const()[name = tensor<string, []>("op_742"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_33_pad_type_0 = const()[name = tensor<string, []>("hidden_states_33_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = tensor<string, []>("hidden_states_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3686400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43503872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47190336))), name = tensor<string, []>("down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47190912)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_33_cast = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_742, groups = var_282, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_740, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_83_cast)[name = tensor<string, []>("hidden_states_33_cast")];
            tensor<int32, [2]> var_748 = const()[name = tensor<string, []>("op_748"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_750 = const()[name = tensor<string, []>("op_750"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_7_pad_type_0 = const()[name = tensor<string, []>("temb_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_7_pad_0 = const()[name = tensor<string, []>("temb_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [614400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47192256))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47806720))), name = tensor<string, []>("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47806912)))];
            tensor<fp16, [2, 640, 1, 1]> temb_7_cast = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_750, groups = var_282, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_748, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_7_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_87_cast = add(x = hidden_states_33_cast, y = temb_7_cast)[name = tensor<string, []>("input_87_cast")];
            tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_87_cast)[name = tensor<string, []>("reshape_32_cast")];
            tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = tensor<string, []>("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_24_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_24_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_24_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor<string, []>("reduce_mean_24_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor<string, []>("sub_16_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_8_cast = square(x = sub_16_cast)[name = tensor<string, []>("square_8_cast")];
            tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = tensor<string, []>("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_26_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_26_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_26_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor<string, []>("reduce_mean_26_cast")];
            tensor<fp16, []> add_16_y_0_to_fp16 = const()[name = tensor<string, []>("add_16_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor<string, []>("add_16_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor<string, []>("sqrt_8_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor<string, []>("real_div_8_cast")];
            tensor<int32, [4]> reshape_33_shape_0 = const()[name = tensor<string, []>("reshape_33_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor<string, []>("reshape_33_cast")];
            tensor<fp16, [640]> add_17_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_17_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47808256)))];
            tensor<fp16, [640]> add_17_beta_0_to_fp16 = const()[name = tensor<string, []>("add_17_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47809600)))];
            tensor<fp16, []> add_17_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_17_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_17_cast = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast)[name = tensor<string, []>("add_17_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_91_cast = silu(x = add_17_cast)[name = tensor<string, []>("input_91_cast")];
            tensor<int32, [2]> var_760 = const()[name = tensor<string, []>("op_760"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_762 = const()[name = tensor<string, []>("op_762"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_35_pad_type_0 = const()[name = tensor<string, []>("hidden_states_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = tensor<string, []>("hidden_states_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3686400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47810944))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51497408))), name = tensor<string, []>("down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51497984)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_35_cast = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_762, groups = var_282, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_760, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_91_cast)[name = tensor<string, []>("hidden_states_35_cast")];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_37_cast = add(x = input_79_cast, y = hidden_states_35_cast)[name = tensor<string, []>("hidden_states_37_cast")];
            tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_36_cast = reshape(shape = reshape_36_shape_0, x = hidden_states_37_cast)[name = tensor<string, []>("reshape_36_cast")];
            tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = tensor<string, []>("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_27_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_27_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_27_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor<string, []>("reduce_mean_27_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor<string, []>("sub_18_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_9_cast = square(x = sub_18_cast)[name = tensor<string, []>("square_9_cast")];
            tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = tensor<string, []>("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_29_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_29_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_29_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor<string, []>("reduce_mean_29_cast")];
            tensor<fp16, []> add_18_y_0_to_fp16 = const()[name = tensor<string, []>("add_18_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor<string, []>("add_18_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor<string, []>("sqrt_9_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor<string, []>("real_div_9_cast")];
            tensor<int32, [4]> reshape_37_shape_0 = const()[name = tensor<string, []>("reshape_37_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor<string, []>("reshape_37_cast")];
            tensor<fp16, [640]> add_19_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_19_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51499328)))];
            tensor<fp16, [640]> add_19_beta_0_to_fp16 = const()[name = tensor<string, []>("add_19_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51500672)))];
            tensor<fp16, []> add_19_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_19_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_37_cast)[name = tensor<string, []>("add_19_cast")];
            tensor<int32, [2]> var_784 = const()[name = tensor<string, []>("op_784"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_786 = const()[name = tensor<string, []>("op_786"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_39_pad_type_0 = const()[name = tensor<string, []>("hidden_states_39_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = tensor<string, []>("hidden_states_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51502016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51911680))), name = tensor<string, []>("down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51912256)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_39_cast = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_786, groups = var_282, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_784, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_19_cast)[name = tensor<string, []>("hidden_states_39_cast")];
            tensor<int32, [4]> var_791 = const()[name = tensor<string, []>("op_791"), val = tensor<int32, [4]>([2, 640, 1, 4096])];
            tensor<fp16, [2, 640, 1, 4096]> inputs_13_cast = reshape(shape = var_791, x = hidden_states_39_cast)[name = tensor<string, []>("inputs_13_cast")];
            tensor<int32, [1]> var_801 = const()[name = tensor<string, []>("op_801"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_13_cast = reduce_mean(axes = var_801, keep_dims = var_277, x = inputs_13_cast)[name = tensor<string, []>("channels_mean_13_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast)[name = tensor<string, []>("zero_mean_13_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast)[name = tensor<string, []>("zero_mean_sq_13_cast")];
            tensor<int32, [1]> var_805 = const()[name = tensor<string, []>("op_805"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_806_cast = reduce_mean(axes = var_805, keep_dims = var_277, x = zero_mean_sq_13_cast)[name = tensor<string, []>("op_806_cast")];
            tensor<fp16, []> var_807_to_fp16 = const()[name = tensor<string, []>("op_807_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_808_cast = add(x = var_806_cast, y = var_807_to_fp16)[name = tensor<string, []>("op_808_cast")];
            tensor<fp16, []> denom_13_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_13_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_808_cast)[name = tensor<string, []>("denom_13_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_cast)[name = tensor<string, []>("out_13_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_812_to_fp16 = const()[name = tensor<string, []>("op_812_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51913600)))];
            tensor<fp16, [2, 640, 1, 4096]> var_813_cast = add(x = out_13_cast, y = var_812_to_fp16)[name = tensor<string, []>("op_813_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_815_to_fp16 = const()[name = tensor<string, []>("op_815_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51914944)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_41_cast = mul(x = var_813_cast, y = var_815_to_fp16)[name = tensor<string, []>("hidden_states_41_cast")];
            tensor<int32, [2]> var_822 = const()[name = tensor<string, []>("op_822"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_824 = const()[name = tensor<string, []>("op_824"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_9_pad_type_0 = const()[name = tensor<string, []>("q_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_9_pad_0 = const()[name = tensor<string, []>("q_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51916288))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52223552))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_9_cast = conv(dilations = var_824, groups = var_282, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_822, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_41_cast)[name = tensor<string, []>("q_9_cast")];
            tensor<int32, [2]> var_828 = const()[name = tensor<string, []>("op_828"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_830 = const()[name = tensor<string, []>("op_830"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_9_pad_type_0 = const()[name = tensor<string, []>("k_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_9_pad_0 = const()[name = tensor<string, []>("k_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52223744))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52531008))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_9_cast = conv(dilations = var_830, groups = var_282, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_828, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_41_cast)[name = tensor<string, []>("k_9_cast")];
            tensor<int32, [2]> var_834 = const()[name = tensor<string, []>("op_834"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_836 = const()[name = tensor<string, []>("op_836"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_9_pad_type_0 = const()[name = tensor<string, []>("v_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_9_pad_0 = const()[name = tensor<string, []>("v_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52531200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52838464))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_9_cast = conv(dilations = var_836, groups = var_282, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_834, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_41_cast)[name = tensor<string, []>("v_9_cast")];
            tensor<int32, [4]> var_840 = const()[name = tensor<string, []>("op_840"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_841_cast = reshape(shape = var_840, x = q_9_cast)[name = tensor<string, []>("op_841_cast")];
            tensor<int32, [4]> var_842 = const()[name = tensor<string, []>("op_842"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_843_cast = reshape(shape = var_842, x = k_9_cast)[name = tensor<string, []>("op_843_cast")];
            tensor<int32, [4]> var_844 = const()[name = tensor<string, []>("op_844"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_845_cast = reshape(shape = var_844, x = v_9_cast)[name = tensor<string, []>("op_845_cast")];
            tensor<bool, []> attn_weights_17_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_17_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_17_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_17_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_17_cast = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_841_cast, y = var_843_cast)[name = tensor<string, []>("attn_weights_17_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_19_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_849_cast = softmax(axis = var_266, x = attn_weights_19_cast)[name = tensor<string, []>("op_849_cast")];
            tensor<bool, []> attn_9_transpose_x_0 = const()[name = tensor<string, []>("attn_9_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_9_transpose_y_0 = const()[name = tensor<string, []>("attn_9_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_9_cast = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_845_cast, y = var_849_cast)[name = tensor<string, []>("attn_9_cast")];
            tensor<int32, [4]> var_853 = const()[name = tensor<string, []>("op_853"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_95_cast = reshape(shape = var_853, x = attn_9_cast)[name = tensor<string, []>("input_95_cast")];
            tensor<int32, [2]> var_858 = const()[name = tensor<string, []>("op_858"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_860 = const()[name = tensor<string, []>("op_860"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_862_pad_type_0 = const()[name = tensor<string, []>("op_862_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_862_pad_0 = const()[name = tensor<string, []>("op_862_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52838656))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53248320))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53248896)))];
            tensor<fp16, [2, 640, 1, 4096]> var_862_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_860, groups = var_282, pad = var_862_pad_0, pad_type = var_862_pad_type_0, strides = var_858, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_95_cast)[name = tensor<string, []>("op_862_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_15_cast = add(x = var_862_cast, y = inputs_13_cast)[name = tensor<string, []>("inputs_15_cast")];
            tensor<int32, [1]> var_866 = const()[name = tensor<string, []>("op_866"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_15_cast = reduce_mean(axes = var_866, keep_dims = var_277, x = inputs_15_cast)[name = tensor<string, []>("channels_mean_15_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast)[name = tensor<string, []>("zero_mean_15_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast)[name = tensor<string, []>("zero_mean_sq_15_cast")];
            tensor<int32, [1]> var_870 = const()[name = tensor<string, []>("op_870"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_871_cast = reduce_mean(axes = var_870, keep_dims = var_277, x = zero_mean_sq_15_cast)[name = tensor<string, []>("op_871_cast")];
            tensor<fp16, []> var_872_to_fp16 = const()[name = tensor<string, []>("op_872_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_873_cast = add(x = var_871_cast, y = var_872_to_fp16)[name = tensor<string, []>("op_873_cast")];
            tensor<fp16, []> denom_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_873_cast)[name = tensor<string, []>("denom_15_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_cast)[name = tensor<string, []>("out_15_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_877_to_fp16 = const()[name = tensor<string, []>("op_877_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53250240)))];
            tensor<fp16, [2, 640, 1, 4096]> var_878_cast = add(x = out_15_cast, y = var_877_to_fp16)[name = tensor<string, []>("op_878_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_880_to_fp16 = const()[name = tensor<string, []>("op_880_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53251584)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_43_cast = mul(x = var_878_cast, y = var_880_to_fp16)[name = tensor<string, []>("hidden_states_43_cast")];
            tensor<int32, [2]> var_887 = const()[name = tensor<string, []>("op_887"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_889 = const()[name = tensor<string, []>("op_889"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_11_pad_type_0 = const()[name = tensor<string, []>("q_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_11_pad_0 = const()[name = tensor<string, []>("q_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53252928))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53457792))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_11_cast = conv(dilations = var_889, groups = var_282, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_887, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("q_11_cast")];
            tensor<int32, [2]> var_893 = const()[name = tensor<string, []>("op_893"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_895 = const()[name = tensor<string, []>("op_895"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_11_pad_type_0 = const()[name = tensor<string, []>("k_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_11_pad_0 = const()[name = tensor<string, []>("k_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53457920))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54113344))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_11_cast = conv(dilations = var_895, groups = var_282, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_893, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_11_cast")];
            tensor<int32, [2]> var_899 = const()[name = tensor<string, []>("op_899"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_901 = const()[name = tensor<string, []>("op_901"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_11_pad_type_0 = const()[name = tensor<string, []>("v_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_11_pad_0 = const()[name = tensor<string, []>("v_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54113472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55096576))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_11_cast = conv(dilations = var_901, groups = var_282, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_899, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_11_cast")];
            tensor<int32, [4]> var_905 = const()[name = tensor<string, []>("op_905"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_906_cast = reshape(shape = var_905, x = q_11_cast)[name = tensor<string, []>("op_906_cast")];
            tensor<int32, [4]> var_907 = const()[name = tensor<string, []>("op_907"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_908_cast = reshape(shape = var_907, x = k_11_cast)[name = tensor<string, []>("op_908_cast")];
            tensor<int32, [4]> var_909 = const()[name = tensor<string, []>("op_909"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_910_cast = reshape(shape = var_909, x = v_11_cast)[name = tensor<string, []>("op_910_cast")];
            tensor<bool, []> attn_weights_21_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_21_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_21_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_21_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_21_cast = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_906_cast, y = var_908_cast)[name = tensor<string, []>("attn_weights_21_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_23_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_914_cast = softmax(axis = var_266, x = attn_weights_23_cast)[name = tensor<string, []>("op_914_cast")];
            tensor<bool, []> attn_11_transpose_x_0 = const()[name = tensor<string, []>("attn_11_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_11_transpose_y_0 = const()[name = tensor<string, []>("attn_11_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_11_cast = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_910_cast, y = var_914_cast)[name = tensor<string, []>("attn_11_cast")];
            tensor<int32, [4]> var_918 = const()[name = tensor<string, []>("op_918"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_97_cast = reshape(shape = var_918, x = attn_11_cast)[name = tensor<string, []>("input_97_cast")];
            tensor<int32, [2]> var_923 = const()[name = tensor<string, []>("op_923"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_925 = const()[name = tensor<string, []>("op_925"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_927_pad_type_0 = const()[name = tensor<string, []>("op_927_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_927_pad_0 = const()[name = tensor<string, []>("op_927_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55096768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55404032))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55404224)))];
            tensor<fp16, [2, 640, 1, 4096]> var_927_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_925, groups = var_282, pad = var_927_pad_0, pad_type = var_927_pad_type_0, strides = var_923, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_97_cast)[name = tensor<string, []>("op_927_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_17_cast = add(x = var_927_cast, y = inputs_15_cast)[name = tensor<string, []>("inputs_17_cast")];
            tensor<int32, [1]> var_931 = const()[name = tensor<string, []>("op_931"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_17_cast = reduce_mean(axes = var_931, keep_dims = var_277, x = inputs_17_cast)[name = tensor<string, []>("channels_mean_17_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast)[name = tensor<string, []>("zero_mean_17_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast)[name = tensor<string, []>("zero_mean_sq_17_cast")];
            tensor<int32, [1]> var_935 = const()[name = tensor<string, []>("op_935"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_936_cast = reduce_mean(axes = var_935, keep_dims = var_277, x = zero_mean_sq_17_cast)[name = tensor<string, []>("op_936_cast")];
            tensor<fp16, []> var_937_to_fp16 = const()[name = tensor<string, []>("op_937_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_938_cast = add(x = var_936_cast, y = var_937_to_fp16)[name = tensor<string, []>("op_938_cast")];
            tensor<fp16, []> denom_17_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_17_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_938_cast)[name = tensor<string, []>("denom_17_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_cast)[name = tensor<string, []>("out_17_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_942_to_fp16 = const()[name = tensor<string, []>("op_942_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55405568)))];
            tensor<fp16, [2, 640, 1, 4096]> var_943_cast = add(x = out_17_cast, y = var_942_to_fp16)[name = tensor<string, []>("op_943_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_945_to_fp16 = const()[name = tensor<string, []>("op_945_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55406912)))];
            tensor<fp16, [2, 640, 1, 4096]> input_99_cast = mul(x = var_943_cast, y = var_945_to_fp16)[name = tensor<string, []>("input_99_cast")];
            tensor<int32, [2]> var_953 = const()[name = tensor<string, []>("op_953"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_955 = const()[name = tensor<string, []>("op_955"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_957_pad_type_0 = const()[name = tensor<string, []>("op_957_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_957_pad_0 = const()[name = tensor<string, []>("op_957_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55408256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58685120))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58685696)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_957_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_955, groups = var_282, pad = var_957_pad_0, pad_type = var_957_pad_type_0, strides = var_953, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_99_cast)[name = tensor<string, []>("op_957_cast")];
            tensor<int32, [2]> var_958_split_sizes_0 = const()[name = tensor<string, []>("op_958_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_958_axis_0 = const()[name = tensor<string, []>("op_958_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_958_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_958_cast_1 = split(axis = var_958_axis_0, split_sizes = var_958_split_sizes_0, x = var_957_cast)[name = tensor<string, []>("op_958_cast")];
            tensor<string, []> var_960_mode_0 = const()[name = tensor<string, []>("op_960_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_960_cast = gelu(mode = var_960_mode_0, x = var_958_cast_1)[name = tensor<string, []>("op_960_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_101_cast = mul(x = var_958_cast_0, y = var_960_cast)[name = tensor<string, []>("input_101_cast")];
            tensor<int32, [2]> var_964 = const()[name = tensor<string, []>("op_964"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_966 = const()[name = tensor<string, []>("op_966"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_968_pad_type_0 = const()[name = tensor<string, []>("op_968_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_968_pad_0 = const()[name = tensor<string, []>("op_968_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58696000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60334464))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60335040)))];
            tensor<fp16, [2, 640, 1, 4096]> var_968_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_966, groups = var_282, pad = var_968_pad_0, pad_type = var_968_pad_type_0, strides = var_964, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_101_cast)[name = tensor<string, []>("op_968_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_19_cast = add(x = var_968_cast, y = inputs_17_cast)[name = tensor<string, []>("inputs_19_cast")];
            tensor<int32, [1]> var_978 = const()[name = tensor<string, []>("op_978"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_19_cast = reduce_mean(axes = var_978, keep_dims = var_277, x = inputs_19_cast)[name = tensor<string, []>("channels_mean_19_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast)[name = tensor<string, []>("zero_mean_19_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast)[name = tensor<string, []>("zero_mean_sq_19_cast")];
            tensor<int32, [1]> var_982 = const()[name = tensor<string, []>("op_982"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_983_cast = reduce_mean(axes = var_982, keep_dims = var_277, x = zero_mean_sq_19_cast)[name = tensor<string, []>("op_983_cast")];
            tensor<fp16, []> var_984_to_fp16 = const()[name = tensor<string, []>("op_984_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_985_cast = add(x = var_983_cast, y = var_984_to_fp16)[name = tensor<string, []>("op_985_cast")];
            tensor<fp16, []> denom_19_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_19_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_985_cast)[name = tensor<string, []>("denom_19_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast)[name = tensor<string, []>("out_19_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_989_to_fp16 = const()[name = tensor<string, []>("op_989_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60336384)))];
            tensor<fp16, [2, 640, 1, 4096]> var_990_cast = add(x = out_19_cast, y = var_989_to_fp16)[name = tensor<string, []>("op_990_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_992_to_fp16 = const()[name = tensor<string, []>("op_992_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60337728)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_47_cast = mul(x = var_990_cast, y = var_992_to_fp16)[name = tensor<string, []>("hidden_states_47_cast")];
            tensor<int32, [2]> var_999 = const()[name = tensor<string, []>("op_999"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1001 = const()[name = tensor<string, []>("op_1001"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_13_pad_type_0 = const()[name = tensor<string, []>("q_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_13_pad_0 = const()[name = tensor<string, []>("q_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60339072))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60646336))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_13_cast = conv(dilations = var_1001, groups = var_282, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_999, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_47_cast)[name = tensor<string, []>("q_13_cast")];
            tensor<int32, [2]> var_1005 = const()[name = tensor<string, []>("op_1005"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1007 = const()[name = tensor<string, []>("op_1007"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_13_pad_type_0 = const()[name = tensor<string, []>("k_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_13_pad_0 = const()[name = tensor<string, []>("k_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60646528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60953792))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_13_cast = conv(dilations = var_1007, groups = var_282, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1005, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_47_cast)[name = tensor<string, []>("k_13_cast")];
            tensor<int32, [2]> var_1011 = const()[name = tensor<string, []>("op_1011"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1013 = const()[name = tensor<string, []>("op_1013"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_13_pad_type_0 = const()[name = tensor<string, []>("v_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_13_pad_0 = const()[name = tensor<string, []>("v_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60953984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61261248))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_13_cast = conv(dilations = var_1013, groups = var_282, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1011, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_47_cast)[name = tensor<string, []>("v_13_cast")];
            tensor<int32, [4]> var_1017 = const()[name = tensor<string, []>("op_1017"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_1018_cast = reshape(shape = var_1017, x = q_13_cast)[name = tensor<string, []>("op_1018_cast")];
            tensor<int32, [4]> var_1019 = const()[name = tensor<string, []>("op_1019"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_1020_cast = reshape(shape = var_1019, x = k_13_cast)[name = tensor<string, []>("op_1020_cast")];
            tensor<int32, [4]> var_1021 = const()[name = tensor<string, []>("op_1021"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_1022_cast = reshape(shape = var_1021, x = v_13_cast)[name = tensor<string, []>("op_1022_cast")];
            tensor<bool, []> attn_weights_25_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_25_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1018_cast, y = var_1020_cast)[name = tensor<string, []>("attn_weights_25_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_27_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_1026_cast = softmax(axis = var_266, x = attn_weights_27_cast)[name = tensor<string, []>("op_1026_cast")];
            tensor<bool, []> attn_13_transpose_x_0 = const()[name = tensor<string, []>("attn_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_13_transpose_y_0 = const()[name = tensor<string, []>("attn_13_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_13_cast = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1022_cast, y = var_1026_cast)[name = tensor<string, []>("attn_13_cast")];
            tensor<int32, [4]> var_1030 = const()[name = tensor<string, []>("op_1030"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_103_cast = reshape(shape = var_1030, x = attn_13_cast)[name = tensor<string, []>("input_103_cast")];
            tensor<int32, [2]> var_1035 = const()[name = tensor<string, []>("op_1035"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1037 = const()[name = tensor<string, []>("op_1037"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1039_pad_type_0 = const()[name = tensor<string, []>("op_1039_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1039_pad_0 = const()[name = tensor<string, []>("op_1039_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61261440))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61568704))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61568896)))];
            tensor<fp16, [2, 640, 1, 4096]> var_1039_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1037, groups = var_282, pad = var_1039_pad_0, pad_type = var_1039_pad_type_0, strides = var_1035, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_103_cast)[name = tensor<string, []>("op_1039_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_21_cast = add(x = var_1039_cast, y = inputs_19_cast)[name = tensor<string, []>("inputs_21_cast")];
            tensor<int32, [1]> var_1043 = const()[name = tensor<string, []>("op_1043"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_21_cast = reduce_mean(axes = var_1043, keep_dims = var_277, x = inputs_21_cast)[name = tensor<string, []>("channels_mean_21_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast)[name = tensor<string, []>("zero_mean_21_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast)[name = tensor<string, []>("zero_mean_sq_21_cast")];
            tensor<int32, [1]> var_1047 = const()[name = tensor<string, []>("op_1047"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_1048_cast = reduce_mean(axes = var_1047, keep_dims = var_277, x = zero_mean_sq_21_cast)[name = tensor<string, []>("op_1048_cast")];
            tensor<fp16, []> var_1049_to_fp16 = const()[name = tensor<string, []>("op_1049_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_1050_cast = add(x = var_1048_cast, y = var_1049_to_fp16)[name = tensor<string, []>("op_1050_cast")];
            tensor<fp16, []> denom_21_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_21_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1050_cast)[name = tensor<string, []>("denom_21_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast)[name = tensor<string, []>("out_21_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_1054_to_fp16 = const()[name = tensor<string, []>("op_1054_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61570240)))];
            tensor<fp16, [2, 640, 1, 4096]> var_1055_cast = add(x = out_21_cast, y = var_1054_to_fp16)[name = tensor<string, []>("op_1055_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_1057_to_fp16 = const()[name = tensor<string, []>("op_1057_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61571584)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_49_cast = mul(x = var_1055_cast, y = var_1057_to_fp16)[name = tensor<string, []>("hidden_states_49_cast")];
            tensor<int32, [2]> var_1064 = const()[name = tensor<string, []>("op_1064"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1066 = const()[name = tensor<string, []>("op_1066"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_15_pad_type_0 = const()[name = tensor<string, []>("q_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_15_pad_0 = const()[name = tensor<string, []>("q_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61572928))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61777792))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_15_cast = conv(dilations = var_1066, groups = var_282, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1064, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("q_15_cast")];
            tensor<int32, [2]> var_1070 = const()[name = tensor<string, []>("op_1070"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1072 = const()[name = tensor<string, []>("op_1072"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_15_pad_type_0 = const()[name = tensor<string, []>("k_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_15_pad_0 = const()[name = tensor<string, []>("k_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61777920))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62433344))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_15_cast = conv(dilations = var_1072, groups = var_282, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1070, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_15_cast")];
            tensor<int32, [2]> var_1076 = const()[name = tensor<string, []>("op_1076"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1078 = const()[name = tensor<string, []>("op_1078"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_15_pad_type_0 = const()[name = tensor<string, []>("v_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_15_pad_0 = const()[name = tensor<string, []>("v_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62433472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63416576))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_15_cast = conv(dilations = var_1078, groups = var_282, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1076, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_15_cast")];
            tensor<int32, [4]> var_1082 = const()[name = tensor<string, []>("op_1082"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_1083_cast = reshape(shape = var_1082, x = q_15_cast)[name = tensor<string, []>("op_1083_cast")];
            tensor<int32, [4]> var_1084 = const()[name = tensor<string, []>("op_1084"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_1085_cast = reshape(shape = var_1084, x = k_15_cast)[name = tensor<string, []>("op_1085_cast")];
            tensor<int32, [4]> var_1086 = const()[name = tensor<string, []>("op_1086"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_1087_cast = reshape(shape = var_1086, x = v_15_cast)[name = tensor<string, []>("op_1087_cast")];
            tensor<bool, []> attn_weights_29_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_29_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_29_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_29_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_29_cast = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1083_cast, y = var_1085_cast)[name = tensor<string, []>("attn_weights_29_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_273_to_fp16)[name = tensor<string, []>("attn_weights_31_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_1091_cast = softmax(axis = var_266, x = attn_weights_31_cast)[name = tensor<string, []>("op_1091_cast")];
            tensor<bool, []> attn_15_transpose_x_0 = const()[name = tensor<string, []>("attn_15_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_15_transpose_y_0 = const()[name = tensor<string, []>("attn_15_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_15_cast = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1087_cast, y = var_1091_cast)[name = tensor<string, []>("attn_15_cast")];
            tensor<int32, [4]> var_1095 = const()[name = tensor<string, []>("op_1095"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_105_cast = reshape(shape = var_1095, x = attn_15_cast)[name = tensor<string, []>("input_105_cast")];
            tensor<int32, [2]> var_1100 = const()[name = tensor<string, []>("op_1100"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1102 = const()[name = tensor<string, []>("op_1102"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1104_pad_type_0 = const()[name = tensor<string, []>("op_1104_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1104_pad_0 = const()[name = tensor<string, []>("op_1104_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63416768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63724032))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63724224)))];
            tensor<fp16, [2, 640, 1, 4096]> var_1104_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1102, groups = var_282, pad = var_1104_pad_0, pad_type = var_1104_pad_type_0, strides = var_1100, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_105_cast)[name = tensor<string, []>("op_1104_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_23_cast = add(x = var_1104_cast, y = inputs_21_cast)[name = tensor<string, []>("inputs_23_cast")];
            tensor<int32, [1]> var_1108 = const()[name = tensor<string, []>("op_1108"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_23_cast = reduce_mean(axes = var_1108, keep_dims = var_277, x = inputs_23_cast)[name = tensor<string, []>("channels_mean_23_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast)[name = tensor<string, []>("zero_mean_23_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast)[name = tensor<string, []>("zero_mean_sq_23_cast")];
            tensor<int32, [1]> var_1112 = const()[name = tensor<string, []>("op_1112"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_1113_cast = reduce_mean(axes = var_1112, keep_dims = var_277, x = zero_mean_sq_23_cast)[name = tensor<string, []>("op_1113_cast")];
            tensor<fp16, []> var_1114_to_fp16 = const()[name = tensor<string, []>("op_1114_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_1115_cast = add(x = var_1113_cast, y = var_1114_to_fp16)[name = tensor<string, []>("op_1115_cast")];
            tensor<fp16, []> denom_23_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_23_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1115_cast)[name = tensor<string, []>("denom_23_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast)[name = tensor<string, []>("out_23_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_1119_to_fp16 = const()[name = tensor<string, []>("op_1119_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63725568)))];
            tensor<fp16, [2, 640, 1, 4096]> var_1120_cast = add(x = out_23_cast, y = var_1119_to_fp16)[name = tensor<string, []>("op_1120_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_1122_to_fp16 = const()[name = tensor<string, []>("op_1122_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63726912)))];
            tensor<fp16, [2, 640, 1, 4096]> input_107_cast = mul(x = var_1120_cast, y = var_1122_to_fp16)[name = tensor<string, []>("input_107_cast")];
            tensor<int32, [2]> var_1130 = const()[name = tensor<string, []>("op_1130"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1132 = const()[name = tensor<string, []>("op_1132"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1134_pad_type_0 = const()[name = tensor<string, []>("op_1134_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1134_pad_0 = const()[name = tensor<string, []>("op_1134_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63728256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67005120))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67005696)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_1134_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_1132, groups = var_282, pad = var_1134_pad_0, pad_type = var_1134_pad_type_0, strides = var_1130, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_107_cast)[name = tensor<string, []>("op_1134_cast")];
            tensor<int32, [2]> var_1135_split_sizes_0 = const()[name = tensor<string, []>("op_1135_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_1135_axis_0 = const()[name = tensor<string, []>("op_1135_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_1135_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_1135_cast_1 = split(axis = var_1135_axis_0, split_sizes = var_1135_split_sizes_0, x = var_1134_cast)[name = tensor<string, []>("op_1135_cast")];
            tensor<string, []> var_1137_mode_0 = const()[name = tensor<string, []>("op_1137_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_1137_cast = gelu(mode = var_1137_mode_0, x = var_1135_cast_1)[name = tensor<string, []>("op_1137_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_109_cast = mul(x = var_1135_cast_0, y = var_1137_cast)[name = tensor<string, []>("input_109_cast")];
            tensor<int32, [2]> var_1141 = const()[name = tensor<string, []>("op_1141"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1143 = const()[name = tensor<string, []>("op_1143"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1145_pad_type_0 = const()[name = tensor<string, []>("op_1145_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1145_pad_0 = const()[name = tensor<string, []>("op_1145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67016000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68654464))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68655040)))];
            tensor<fp16, [2, 640, 1, 4096]> var_1145_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1143, groups = var_282, pad = var_1145_pad_0, pad_type = var_1145_pad_type_0, strides = var_1141, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_109_cast)[name = tensor<string, []>("op_1145_cast")];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_53_cast = add(x = var_1145_cast, y = inputs_23_cast)[name = tensor<string, []>("hidden_states_53_cast")];
            tensor<int32, [4]> var_1147 = const()[name = tensor<string, []>("op_1147"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> input_111_cast = reshape(shape = var_1147, x = hidden_states_53_cast)[name = tensor<string, []>("input_111_cast")];
            tensor<int32, [2]> var_1151 = const()[name = tensor<string, []>("op_1151"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1153 = const()[name = tensor<string, []>("op_1153"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_55_pad_type_0 = const()[name = tensor<string, []>("hidden_states_55_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_55_pad_0 = const()[name = tensor<string, []>("hidden_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68656384))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69066048))), name = tensor<string, []>("down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69066624)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_55_cast = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1153, groups = var_282, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_1151, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_111_cast)[name = tensor<string, []>("hidden_states_55_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_113_cast = add(x = hidden_states_55_cast, y = hidden_states_37_cast)[name = tensor<string, []>("input_113_cast")];
            tensor<int32, [2]> var_1160 = const()[name = tensor<string, []>("op_1160"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_1162 = const()[name = tensor<string, []>("op_1162"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_115_pad_type_0 = const()[name = tensor<string, []>("input_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_115_pad_0 = const()[name = tensor<string, []>("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3686400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69067968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72754432))), name = tensor<string, []>("down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72755008)))];
            tensor<fp16, [2, 640, 32, 32]> input_115_cast = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1162, groups = var_282, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_1160, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized, x = input_113_cast)[name = tensor<string, []>("input_115_cast")];
            tensor<int32, []> var_1170 = const()[name = tensor<string, []>("op_1170"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_1181 = const()[name = tensor<string, []>("op_1181"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_1186 = const()[name = tensor<string, []>("op_1186"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_115_cast)[name = tensor<string, []>("reshape_40_cast")];
            tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = tensor<string, []>("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_30_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_30_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_30_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor<string, []>("reduce_mean_30_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor<string, []>("sub_20_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> square_10_cast = square(x = sub_20_cast)[name = tensor<string, []>("square_10_cast")];
            tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = tensor<string, []>("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_32_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_32_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_32_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor<string, []>("reduce_mean_32_cast")];
            tensor<fp16, []> add_20_y_0_to_fp16 = const()[name = tensor<string, []>("add_20_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor<string, []>("add_20_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor<string, []>("sqrt_10_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor<string, []>("real_div_10_cast")];
            tensor<int32, [4]> reshape_41_shape_0 = const()[name = tensor<string, []>("reshape_41_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor<string, []>("reshape_41_cast")];
            tensor<fp16, [640]> add_21_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_21_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72756352)))];
            tensor<fp16, [640]> add_21_beta_0_to_fp16 = const()[name = tensor<string, []>("add_21_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72757696)))];
            tensor<fp16, []> add_21_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_21_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 32, 32]> add_21_cast = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_41_cast)[name = tensor<string, []>("add_21_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_119_cast = silu(x = add_21_cast)[name = tensor<string, []>("input_119_cast")];
            tensor<int32, [2]> var_1207 = const()[name = tensor<string, []>("op_1207"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1209 = const()[name = tensor<string, []>("op_1209"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_57_pad_type_0 = const()[name = tensor<string, []>("hidden_states_57_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = tensor<string, []>("hidden_states_57_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 640, 3, 3]> down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5529600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72759040))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78288704))), name = tensor<string, []>("down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 640, 3, 3])];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78288896)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_57_cast = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1209, groups = var_1186, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_1207, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_119_cast)[name = tensor<string, []>("hidden_states_57_cast")];
            tensor<int32, [2]> var_1215 = const()[name = tensor<string, []>("op_1215"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1217 = const()[name = tensor<string, []>("op_1217"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_9_pad_type_0 = const()[name = tensor<string, []>("temb_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_9_pad_0 = const()[name = tensor<string, []>("temb_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78291520))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79520384))), name = tensor<string, []>("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79520576)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_9_cast = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1217, groups = var_1186, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1215, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_9_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_123_cast = add(x = hidden_states_57_cast, y = temb_9_cast)[name = tensor<string, []>("input_123_cast")];
            tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_123_cast)[name = tensor<string, []>("reshape_44_cast")];
            tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = tensor<string, []>("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_33_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_33_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_33_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor<string, []>("reduce_mean_33_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor<string, []>("sub_22_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_11_cast = square(x = sub_22_cast)[name = tensor<string, []>("square_11_cast")];
            tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = tensor<string, []>("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_35_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_35_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_35_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor<string, []>("reduce_mean_35_cast")];
            tensor<fp16, []> add_22_y_0_to_fp16 = const()[name = tensor<string, []>("add_22_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor<string, []>("add_22_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor<string, []>("sqrt_11_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor<string, []>("real_div_11_cast")];
            tensor<int32, [4]> reshape_45_shape_0 = const()[name = tensor<string, []>("reshape_45_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor<string, []>("reshape_45_cast")];
            tensor<fp16, [1280]> add_23_mean_0_to_fp16 = const()[name = tensor<string, []>("add_23_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79523200)))];
            tensor<fp16, [1280]> add_23_variance_0_to_fp16 = const()[name = tensor<string, []>("add_23_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79525824)))];
            tensor<fp16, [1280]> add_23_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_23_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79528448)))];
            tensor<fp16, [1280]> add_23_beta_0_to_fp16 = const()[name = tensor<string, []>("add_23_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79531072)))];
            tensor<fp16, []> add_23_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_23_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_23_cast = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast)[name = tensor<string, []>("add_23_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_127_cast = silu(x = add_23_cast)[name = tensor<string, []>("input_127_cast")];
            tensor<int32, [2]> var_1227 = const()[name = tensor<string, []>("op_1227"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1229 = const()[name = tensor<string, []>("op_1229"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_59_pad_type_0 = const()[name = tensor<string, []>("hidden_states_59_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_59_pad_0 = const()[name = tensor<string, []>("hidden_states_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79533696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90592960))), name = tensor<string, []>("down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90593152)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_59_cast = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1229, groups = var_1186, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_1227, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_127_cast)[name = tensor<string, []>("hidden_states_59_cast")];
            tensor<int32, [2]> var_1234 = const()[name = tensor<string, []>("op_1234"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1236 = const()[name = tensor<string, []>("op_1236"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_3_pad_type_0 = const()[name = tensor<string, []>("x_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_3_pad_0 = const()[name = tensor<string, []>("x_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 640, 1, 1]> down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90595776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91415040))), name = tensor<string, []>("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 640, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91415616)))];
            tensor<fp16, [2, 1280, 32, 32]> x_3_cast = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1236, groups = var_1186, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1234, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_115_cast)[name = tensor<string, []>("x_3_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_61_cast = add(x = x_3_cast, y = hidden_states_59_cast)[name = tensor<string, []>("hidden_states_61_cast")];
            tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_48_cast = reshape(shape = reshape_48_shape_0, x = hidden_states_61_cast)[name = tensor<string, []>("reshape_48_cast")];
            tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = tensor<string, []>("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_36_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_36_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_36_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor<string, []>("reduce_mean_36_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor<string, []>("sub_24_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_12_cast = square(x = sub_24_cast)[name = tensor<string, []>("square_12_cast")];
            tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = tensor<string, []>("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_38_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_38_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_38_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor<string, []>("reduce_mean_38_cast")];
            tensor<fp16, []> add_24_y_0_to_fp16 = const()[name = tensor<string, []>("add_24_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor<string, []>("add_24_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor<string, []>("sqrt_12_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor<string, []>("real_div_12_cast")];
            tensor<int32, [4]> reshape_49_shape_0 = const()[name = tensor<string, []>("reshape_49_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor<string, []>("reshape_49_cast")];
            tensor<fp16, [1280]> add_25_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_25_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91418240)))];
            tensor<fp16, [1280]> add_25_beta_0_to_fp16 = const()[name = tensor<string, []>("add_25_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91420864)))];
            tensor<fp16, []> add_25_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_25_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_25_cast = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_49_cast)[name = tensor<string, []>("add_25_cast")];
            tensor<int32, [2]> var_1274 = const()[name = tensor<string, []>("op_1274"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1276 = const()[name = tensor<string, []>("op_1276"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_63_pad_type_0 = const()[name = tensor<string, []>("hidden_states_63_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = tensor<string, []>("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91423488))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92652352))), name = tensor<string, []>("down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92652544)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_63_cast = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1276, groups = var_1186, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = var_1274, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_25_cast)[name = tensor<string, []>("hidden_states_63_cast")];
            tensor<int32, [4]> var_1281 = const()[name = tensor<string, []>("op_1281"), val = tensor<int32, [4]>([2, 1280, 1, 1024])];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_25_cast = reshape(shape = var_1281, x = hidden_states_63_cast)[name = tensor<string, []>("inputs_25_cast")];
            tensor<int32, [1]> var_1291 = const()[name = tensor<string, []>("op_1291"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_25_cast = reduce_mean(axes = var_1291, keep_dims = var_1181, x = inputs_25_cast)[name = tensor<string, []>("channels_mean_25_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast)[name = tensor<string, []>("zero_mean_25_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast)[name = tensor<string, []>("zero_mean_sq_25_cast")];
            tensor<int32, [1]> var_1295 = const()[name = tensor<string, []>("op_1295"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1296_cast = reduce_mean(axes = var_1295, keep_dims = var_1181, x = zero_mean_sq_25_cast)[name = tensor<string, []>("op_1296_cast")];
            tensor<fp16, []> var_1297_to_fp16 = const()[name = tensor<string, []>("op_1297_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1298_cast = add(x = var_1296_cast, y = var_1297_to_fp16)[name = tensor<string, []>("op_1298_cast")];
            tensor<fp16, []> denom_25_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_25_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1298_cast)[name = tensor<string, []>("denom_25_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast)[name = tensor<string, []>("out_25_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1302_to_fp16 = const()[name = tensor<string, []>("op_1302_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92655168)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1303_cast = add(x = out_25_cast, y = var_1302_to_fp16)[name = tensor<string, []>("op_1303_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1305_to_fp16 = const()[name = tensor<string, []>("op_1305_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92657792)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_65_cast = mul(x = var_1303_cast, y = var_1305_to_fp16)[name = tensor<string, []>("hidden_states_65_cast")];
            tensor<int32, [2]> var_1312 = const()[name = tensor<string, []>("op_1312"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1314 = const()[name = tensor<string, []>("op_1314"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_17_pad_type_0 = const()[name = tensor<string, []>("q_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_17_pad_0 = const()[name = tensor<string, []>("q_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92660416))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93479680))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_17_cast = conv(dilations = var_1314, groups = var_1186, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1312, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_65_cast)[name = tensor<string, []>("q_17_cast")];
            tensor<int32, [2]> var_1318 = const()[name = tensor<string, []>("op_1318"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1320 = const()[name = tensor<string, []>("op_1320"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_17_pad_type_0 = const()[name = tensor<string, []>("k_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_17_pad_0 = const()[name = tensor<string, []>("k_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93479808))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94299072))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_17_cast = conv(dilations = var_1320, groups = var_1186, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1318, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_65_cast)[name = tensor<string, []>("k_17_cast")];
            tensor<int32, [2]> var_1324 = const()[name = tensor<string, []>("op_1324"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1326 = const()[name = tensor<string, []>("op_1326"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_17_pad_type_0 = const()[name = tensor<string, []>("v_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_17_pad_0 = const()[name = tensor<string, []>("v_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94299200))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95118464))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_17_cast = conv(dilations = var_1326, groups = var_1186, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1324, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_65_cast)[name = tensor<string, []>("v_17_cast")];
            tensor<int32, [4]> var_1330 = const()[name = tensor<string, []>("op_1330"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1331_cast = reshape(shape = var_1330, x = q_17_cast)[name = tensor<string, []>("op_1331_cast")];
            tensor<int32, [4]> var_1332 = const()[name = tensor<string, []>("op_1332"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1333_cast = reshape(shape = var_1332, x = k_17_cast)[name = tensor<string, []>("op_1333_cast")];
            tensor<int32, [4]> var_1334 = const()[name = tensor<string, []>("op_1334"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1335_cast = reshape(shape = var_1334, x = v_17_cast)[name = tensor<string, []>("op_1335_cast")];
            tensor<bool, []> attn_weights_33_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_33_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_33_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_33_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_33_cast = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1331_cast, y = var_1333_cast)[name = tensor<string, []>("attn_weights_33_cast")];
            tensor<fp16, []> var_1177_to_fp16 = const()[name = tensor<string, []>("op_1177_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_35_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_1339_cast = softmax(axis = var_1170, x = attn_weights_35_cast)[name = tensor<string, []>("op_1339_cast")];
            tensor<bool, []> attn_17_transpose_x_0 = const()[name = tensor<string, []>("attn_17_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_17_transpose_y_0 = const()[name = tensor<string, []>("attn_17_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_17_cast = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1335_cast, y = var_1339_cast)[name = tensor<string, []>("attn_17_cast")];
            tensor<int32, [4]> var_1343 = const()[name = tensor<string, []>("op_1343"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_131_cast = reshape(shape = var_1343, x = attn_17_cast)[name = tensor<string, []>("input_131_cast")];
            tensor<int32, [2]> var_1348 = const()[name = tensor<string, []>("op_1348"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1350 = const()[name = tensor<string, []>("op_1350"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1352_pad_type_0 = const()[name = tensor<string, []>("op_1352_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1352_pad_0 = const()[name = tensor<string, []>("op_1352_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95118592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96347456))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96347648)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1352_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1350, groups = var_1186, pad = var_1352_pad_0, pad_type = var_1352_pad_type_0, strides = var_1348, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_131_cast)[name = tensor<string, []>("op_1352_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_27_cast = add(x = var_1352_cast, y = inputs_25_cast)[name = tensor<string, []>("inputs_27_cast")];
            tensor<int32, [1]> var_1356 = const()[name = tensor<string, []>("op_1356"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_27_cast = reduce_mean(axes = var_1356, keep_dims = var_1181, x = inputs_27_cast)[name = tensor<string, []>("channels_mean_27_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast)[name = tensor<string, []>("zero_mean_27_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast)[name = tensor<string, []>("zero_mean_sq_27_cast")];
            tensor<int32, [1]> var_1360 = const()[name = tensor<string, []>("op_1360"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1361_cast = reduce_mean(axes = var_1360, keep_dims = var_1181, x = zero_mean_sq_27_cast)[name = tensor<string, []>("op_1361_cast")];
            tensor<fp16, []> var_1362_to_fp16 = const()[name = tensor<string, []>("op_1362_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1363_cast = add(x = var_1361_cast, y = var_1362_to_fp16)[name = tensor<string, []>("op_1363_cast")];
            tensor<fp16, []> denom_27_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_27_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1363_cast)[name = tensor<string, []>("denom_27_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast)[name = tensor<string, []>("out_27_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1367_to_fp16 = const()[name = tensor<string, []>("op_1367_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96350272)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1368_cast = add(x = out_27_cast, y = var_1367_to_fp16)[name = tensor<string, []>("op_1368_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1370_to_fp16 = const()[name = tensor<string, []>("op_1370_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96352896)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_67_cast = mul(x = var_1368_cast, y = var_1370_to_fp16)[name = tensor<string, []>("hidden_states_67_cast")];
            tensor<int32, [2]> var_1377 = const()[name = tensor<string, []>("op_1377"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1379 = const()[name = tensor<string, []>("op_1379"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_19_pad_type_0 = const()[name = tensor<string, []>("q_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_19_pad_0 = const()[name = tensor<string, []>("q_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96355520))), lut = tensor<fp16, [2]>([-0x1.adp-7, 0x1.ad8p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_19_cast = conv(dilations = var_1379, groups = var_1186, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1377, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("q_19_cast")];
            tensor<int32, [2]> var_1383 = const()[name = tensor<string, []>("op_1383"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1385 = const()[name = tensor<string, []>("op_1385"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_19_pad_type_0 = const()[name = tensor<string, []>("k_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_19_pad_0 = const()[name = tensor<string, []>("k_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96560384))), lut = tensor<fp16, [4]>([-0x1.7fcp-6, -0x1.bdcp-8, 0x1.c44p-8, 0x1.818p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_19_cast = conv(dilations = var_1385, groups = var_1186, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1383, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_19_cast")];
            tensor<int32, [2]> var_1389 = const()[name = tensor<string, []>("op_1389"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1391 = const()[name = tensor<string, []>("op_1391"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_19_pad_type_0 = const()[name = tensor<string, []>("v_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_19_pad_0 = const()[name = tensor<string, []>("v_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97215808))), lut = tensor<fp16, [4]>([-0x1.8e8p-6, -0x1.cbp-8, 0x1.cccp-8, 0x1.9p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_19_cast = conv(dilations = var_1391, groups = var_1186, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1389, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_19_cast")];
            tensor<int32, [4]> var_1395 = const()[name = tensor<string, []>("op_1395"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1396_cast = reshape(shape = var_1395, x = q_19_cast)[name = tensor<string, []>("op_1396_cast")];
            tensor<int32, [4]> var_1397 = const()[name = tensor<string, []>("op_1397"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1398_cast = reshape(shape = var_1397, x = k_19_cast)[name = tensor<string, []>("op_1398_cast")];
            tensor<int32, [4]> var_1399 = const()[name = tensor<string, []>("op_1399"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1400_cast = reshape(shape = var_1399, x = v_19_cast)[name = tensor<string, []>("op_1400_cast")];
            tensor<bool, []> attn_weights_37_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_37_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1396_cast, y = var_1398_cast)[name = tensor<string, []>("attn_weights_37_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_39_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_1404_cast = softmax(axis = var_1170, x = attn_weights_39_cast)[name = tensor<string, []>("op_1404_cast")];
            tensor<bool, []> attn_19_transpose_x_0 = const()[name = tensor<string, []>("attn_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_19_transpose_y_0 = const()[name = tensor<string, []>("attn_19_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_19_cast = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1400_cast, y = var_1404_cast)[name = tensor<string, []>("attn_19_cast")];
            tensor<int32, [4]> var_1408 = const()[name = tensor<string, []>("op_1408"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_133_cast = reshape(shape = var_1408, x = attn_19_cast)[name = tensor<string, []>("input_133_cast")];
            tensor<int32, [2]> var_1413 = const()[name = tensor<string, []>("op_1413"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1415 = const()[name = tensor<string, []>("op_1415"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1417_pad_type_0 = const()[name = tensor<string, []>("op_1417_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1417_pad_0 = const()[name = tensor<string, []>("op_1417_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97871232))), lut = tensor<fp16, [4]>([-0x1.aep-7, -0x1.f5p-9, 0x1.fa8p-9, 0x1.afcp-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98280896)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1417_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1415, groups = var_1186, pad = var_1417_pad_0, pad_type = var_1417_pad_type_0, strides = var_1413, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_133_cast)[name = tensor<string, []>("op_1417_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_29_cast = add(x = var_1417_cast, y = inputs_27_cast)[name = tensor<string, []>("inputs_29_cast")];
            tensor<int32, [1]> var_1421 = const()[name = tensor<string, []>("op_1421"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_29_cast = reduce_mean(axes = var_1421, keep_dims = var_1181, x = inputs_29_cast)[name = tensor<string, []>("channels_mean_29_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast)[name = tensor<string, []>("zero_mean_29_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast)[name = tensor<string, []>("zero_mean_sq_29_cast")];
            tensor<int32, [1]> var_1425 = const()[name = tensor<string, []>("op_1425"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1426_cast = reduce_mean(axes = var_1425, keep_dims = var_1181, x = zero_mean_sq_29_cast)[name = tensor<string, []>("op_1426_cast")];
            tensor<fp16, []> var_1427_to_fp16 = const()[name = tensor<string, []>("op_1427_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1428_cast = add(x = var_1426_cast, y = var_1427_to_fp16)[name = tensor<string, []>("op_1428_cast")];
            tensor<fp16, []> denom_29_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_29_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1428_cast)[name = tensor<string, []>("denom_29_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast)[name = tensor<string, []>("out_29_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1432_to_fp16 = const()[name = tensor<string, []>("op_1432_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98283520)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1433_cast = add(x = out_29_cast, y = var_1432_to_fp16)[name = tensor<string, []>("op_1433_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1435_to_fp16 = const()[name = tensor<string, []>("op_1435_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98286144)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_135_cast = mul(x = var_1433_cast, y = var_1435_to_fp16)[name = tensor<string, []>("input_135_cast")];
            tensor<int32, [2]> var_1443 = const()[name = tensor<string, []>("op_1443"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1445 = const()[name = tensor<string, []>("op_1445"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1447_pad_type_0 = const()[name = tensor<string, []>("op_1447_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1447_pad_0 = const()[name = tensor<string, []>("op_1447_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98288768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108119232))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108119424)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_1447_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1445, groups = var_1186, pad = var_1447_pad_0, pad_type = var_1447_pad_type_0, strides = var_1443, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_135_cast)[name = tensor<string, []>("op_1447_cast")];
            tensor<int32, [2]> var_1448_split_sizes_0 = const()[name = tensor<string, []>("op_1448_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1448_axis_0 = const()[name = tensor<string, []>("op_1448_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_1448_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_1448_cast_1 = split(axis = var_1448_axis_0, split_sizes = var_1448_split_sizes_0, x = var_1447_cast)[name = tensor<string, []>("op_1448_cast")];
            tensor<string, []> var_1450_mode_0 = const()[name = tensor<string, []>("op_1450_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_1450_cast = gelu(mode = var_1450_mode_0, x = var_1448_cast_1)[name = tensor<string, []>("op_1450_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_137_cast = mul(x = var_1448_cast_0, y = var_1450_cast)[name = tensor<string, []>("input_137_cast")];
            tensor<int32, [2]> var_1454 = const()[name = tensor<string, []>("op_1454"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1456 = const()[name = tensor<string, []>("op_1456"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1458_pad_type_0 = const()[name = tensor<string, []>("op_1458_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1458_pad_0 = const()[name = tensor<string, []>("op_1458_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108139968))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111416832))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111416960)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1458_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1456, groups = var_1186, pad = var_1458_pad_0, pad_type = var_1458_pad_type_0, strides = var_1454, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_137_cast)[name = tensor<string, []>("op_1458_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_31_cast = add(x = var_1458_cast, y = inputs_29_cast)[name = tensor<string, []>("inputs_31_cast")];
            tensor<int32, [1]> var_1468 = const()[name = tensor<string, []>("op_1468"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_31_cast = reduce_mean(axes = var_1468, keep_dims = var_1181, x = inputs_31_cast)[name = tensor<string, []>("channels_mean_31_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast)[name = tensor<string, []>("zero_mean_31_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast)[name = tensor<string, []>("zero_mean_sq_31_cast")];
            tensor<int32, [1]> var_1472 = const()[name = tensor<string, []>("op_1472"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1473_cast = reduce_mean(axes = var_1472, keep_dims = var_1181, x = zero_mean_sq_31_cast)[name = tensor<string, []>("op_1473_cast")];
            tensor<fp16, []> var_1474_to_fp16 = const()[name = tensor<string, []>("op_1474_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1475_cast = add(x = var_1473_cast, y = var_1474_to_fp16)[name = tensor<string, []>("op_1475_cast")];
            tensor<fp16, []> denom_31_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_31_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1475_cast)[name = tensor<string, []>("denom_31_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast)[name = tensor<string, []>("out_31_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1479_to_fp16 = const()[name = tensor<string, []>("op_1479_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111419584)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1480_cast = add(x = out_31_cast, y = var_1479_to_fp16)[name = tensor<string, []>("op_1480_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1482_to_fp16 = const()[name = tensor<string, []>("op_1482_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111422208)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_71_cast = mul(x = var_1480_cast, y = var_1482_to_fp16)[name = tensor<string, []>("hidden_states_71_cast")];
            tensor<int32, [2]> var_1489 = const()[name = tensor<string, []>("op_1489"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1491 = const()[name = tensor<string, []>("op_1491"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_21_pad_type_0 = const()[name = tensor<string, []>("q_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_21_pad_0 = const()[name = tensor<string, []>("q_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111424832))), lut = tensor<fp16, [4]>([-0x1.17p-5, -0x1.518p-7, 0x1.4d4p-7, 0x1.16p-5]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_21_cast = conv(dilations = var_1491, groups = var_1186, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1489, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_71_cast)[name = tensor<string, []>("q_21_cast")];
            tensor<int32, [2]> var_1495 = const()[name = tensor<string, []>("op_1495"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1497 = const()[name = tensor<string, []>("op_1497"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_21_pad_type_0 = const()[name = tensor<string, []>("k_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_21_pad_0 = const()[name = tensor<string, []>("k_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111834496))), lut = tensor<fp16, [4]>([-0x1.16p-5, -0x1.4fp-7, 0x1.4e8p-7, 0x1.16p-5]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_21_cast = conv(dilations = var_1497, groups = var_1186, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1495, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_71_cast)[name = tensor<string, []>("k_21_cast")];
            tensor<int32, [2]> var_1501 = const()[name = tensor<string, []>("op_1501"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1503 = const()[name = tensor<string, []>("op_1503"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_21_pad_type_0 = const()[name = tensor<string, []>("v_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_21_pad_0 = const()[name = tensor<string, []>("v_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112244160))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113063424))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_21_cast = conv(dilations = var_1503, groups = var_1186, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1501, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_71_cast)[name = tensor<string, []>("v_21_cast")];
            tensor<int32, [4]> var_1507 = const()[name = tensor<string, []>("op_1507"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1508_cast = reshape(shape = var_1507, x = q_21_cast)[name = tensor<string, []>("op_1508_cast")];
            tensor<int32, [4]> var_1509 = const()[name = tensor<string, []>("op_1509"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1510_cast = reshape(shape = var_1509, x = k_21_cast)[name = tensor<string, []>("op_1510_cast")];
            tensor<int32, [4]> var_1511 = const()[name = tensor<string, []>("op_1511"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1512_cast = reshape(shape = var_1511, x = v_21_cast)[name = tensor<string, []>("op_1512_cast")];
            tensor<bool, []> attn_weights_41_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_41_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_41_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_41_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_41_cast = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1508_cast, y = var_1510_cast)[name = tensor<string, []>("attn_weights_41_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_43_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_1516_cast = softmax(axis = var_1170, x = attn_weights_43_cast)[name = tensor<string, []>("op_1516_cast")];
            tensor<bool, []> attn_21_transpose_x_0 = const()[name = tensor<string, []>("attn_21_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_21_transpose_y_0 = const()[name = tensor<string, []>("attn_21_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_21_cast = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1512_cast, y = var_1516_cast)[name = tensor<string, []>("attn_21_cast")];
            tensor<int32, [4]> var_1520 = const()[name = tensor<string, []>("op_1520"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_139_cast = reshape(shape = var_1520, x = attn_21_cast)[name = tensor<string, []>("input_139_cast")];
            tensor<int32, [2]> var_1525 = const()[name = tensor<string, []>("op_1525"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1527 = const()[name = tensor<string, []>("op_1527"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1529_pad_type_0 = const()[name = tensor<string, []>("op_1529_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1529_pad_0 = const()[name = tensor<string, []>("op_1529_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113063552))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113882816))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113882944)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1529_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1527, groups = var_1186, pad = var_1529_pad_0, pad_type = var_1529_pad_type_0, strides = var_1525, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_139_cast)[name = tensor<string, []>("op_1529_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_33_cast = add(x = var_1529_cast, y = inputs_31_cast)[name = tensor<string, []>("inputs_33_cast")];
            tensor<int32, [1]> var_1533 = const()[name = tensor<string, []>("op_1533"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_33_cast = reduce_mean(axes = var_1533, keep_dims = var_1181, x = inputs_33_cast)[name = tensor<string, []>("channels_mean_33_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast)[name = tensor<string, []>("zero_mean_33_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast)[name = tensor<string, []>("zero_mean_sq_33_cast")];
            tensor<int32, [1]> var_1537 = const()[name = tensor<string, []>("op_1537"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1538_cast = reduce_mean(axes = var_1537, keep_dims = var_1181, x = zero_mean_sq_33_cast)[name = tensor<string, []>("op_1538_cast")];
            tensor<fp16, []> var_1539_to_fp16 = const()[name = tensor<string, []>("op_1539_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1540_cast = add(x = var_1538_cast, y = var_1539_to_fp16)[name = tensor<string, []>("op_1540_cast")];
            tensor<fp16, []> denom_33_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_33_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1540_cast)[name = tensor<string, []>("denom_33_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast)[name = tensor<string, []>("out_33_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1544_to_fp16 = const()[name = tensor<string, []>("op_1544_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113885568)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1545_cast = add(x = out_33_cast, y = var_1544_to_fp16)[name = tensor<string, []>("op_1545_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1547_to_fp16 = const()[name = tensor<string, []>("op_1547_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113888192)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_73_cast = mul(x = var_1545_cast, y = var_1547_to_fp16)[name = tensor<string, []>("hidden_states_73_cast")];
            tensor<int32, [2]> var_1554 = const()[name = tensor<string, []>("op_1554"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1556 = const()[name = tensor<string, []>("op_1556"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_23_pad_type_0 = const()[name = tensor<string, []>("q_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_23_pad_0 = const()[name = tensor<string, []>("q_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113890816))), lut = tensor<fp16, [4]>([-0x1.aap-6, -0x1.058p-7, 0x1.068p-7, 0x1.aa8p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_23_cast = conv(dilations = var_1556, groups = var_1186, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("q_23_cast")];
            tensor<int32, [2]> var_1560 = const()[name = tensor<string, []>("op_1560"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1562 = const()[name = tensor<string, []>("op_1562"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_23_pad_type_0 = const()[name = tensor<string, []>("k_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_23_pad_0 = const()[name = tensor<string, []>("k_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114300480))), lut = tensor<fp16, [4]>([-0x1.85cp-6, -0x1.cbp-8, 0x1.c18p-8, 0x1.82cp-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_23_cast = conv(dilations = var_1562, groups = var_1186, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_23_cast")];
            tensor<int32, [2]> var_1566 = const()[name = tensor<string, []>("op_1566"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1568 = const()[name = tensor<string, []>("op_1568"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_23_pad_type_0 = const()[name = tensor<string, []>("v_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_23_pad_0 = const()[name = tensor<string, []>("v_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114955904))), lut = tensor<fp16, [4]>([-0x1.a5p-6, -0x1.e3p-8, 0x1.e54p-8, 0x1.a5p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_23_cast = conv(dilations = var_1568, groups = var_1186, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_23_cast")];
            tensor<int32, [4]> var_1572 = const()[name = tensor<string, []>("op_1572"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1573_cast = reshape(shape = var_1572, x = q_23_cast)[name = tensor<string, []>("op_1573_cast")];
            tensor<int32, [4]> var_1574 = const()[name = tensor<string, []>("op_1574"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1575_cast = reshape(shape = var_1574, x = k_23_cast)[name = tensor<string, []>("op_1575_cast")];
            tensor<int32, [4]> var_1576 = const()[name = tensor<string, []>("op_1576"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1577_cast = reshape(shape = var_1576, x = v_23_cast)[name = tensor<string, []>("op_1577_cast")];
            tensor<bool, []> attn_weights_45_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_45_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_45_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_45_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_45_cast = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1573_cast, y = var_1575_cast)[name = tensor<string, []>("attn_weights_45_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_47_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_1581_cast = softmax(axis = var_1170, x = attn_weights_47_cast)[name = tensor<string, []>("op_1581_cast")];
            tensor<bool, []> attn_23_transpose_x_0 = const()[name = tensor<string, []>("attn_23_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_23_transpose_y_0 = const()[name = tensor<string, []>("attn_23_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_23_cast = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1577_cast, y = var_1581_cast)[name = tensor<string, []>("attn_23_cast")];
            tensor<int32, [4]> var_1585 = const()[name = tensor<string, []>("op_1585"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_141_cast = reshape(shape = var_1585, x = attn_23_cast)[name = tensor<string, []>("input_141_cast")];
            tensor<int32, [2]> var_1590 = const()[name = tensor<string, []>("op_1590"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1592 = const()[name = tensor<string, []>("op_1592"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1594_pad_type_0 = const()[name = tensor<string, []>("op_1594_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1594_pad_0 = const()[name = tensor<string, []>("op_1594_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115611328))), lut = tensor<fp16, [2]>([-0x1.ca8p-8, 0x1.ccp-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115816192)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1594_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1186, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_141_cast)[name = tensor<string, []>("op_1594_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_35_cast = add(x = var_1594_cast, y = inputs_33_cast)[name = tensor<string, []>("inputs_35_cast")];
            tensor<int32, [1]> var_1598 = const()[name = tensor<string, []>("op_1598"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_35_cast = reduce_mean(axes = var_1598, keep_dims = var_1181, x = inputs_35_cast)[name = tensor<string, []>("channels_mean_35_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast)[name = tensor<string, []>("zero_mean_35_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast)[name = tensor<string, []>("zero_mean_sq_35_cast")];
            tensor<int32, [1]> var_1602 = const()[name = tensor<string, []>("op_1602"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1603_cast = reduce_mean(axes = var_1602, keep_dims = var_1181, x = zero_mean_sq_35_cast)[name = tensor<string, []>("op_1603_cast")];
            tensor<fp16, []> var_1604_to_fp16 = const()[name = tensor<string, []>("op_1604_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1605_cast = add(x = var_1603_cast, y = var_1604_to_fp16)[name = tensor<string, []>("op_1605_cast")];
            tensor<fp16, []> denom_35_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_35_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1605_cast)[name = tensor<string, []>("denom_35_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast)[name = tensor<string, []>("out_35_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1609_to_fp16 = const()[name = tensor<string, []>("op_1609_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115818816)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1610_cast = add(x = out_35_cast, y = var_1609_to_fp16)[name = tensor<string, []>("op_1610_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1612_to_fp16 = const()[name = tensor<string, []>("op_1612_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115821440)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_143_cast = mul(x = var_1610_cast, y = var_1612_to_fp16)[name = tensor<string, []>("input_143_cast")];
            tensor<int32, [2]> var_1620 = const()[name = tensor<string, []>("op_1620"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1622 = const()[name = tensor<string, []>("op_1622"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1624_pad_type_0 = const()[name = tensor<string, []>("op_1624_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1624_pad_0 = const()[name = tensor<string, []>("op_1624_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115824064))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122377728))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122377856)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_1624_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_1622, groups = var_1186, pad = var_1624_pad_0, pad_type = var_1624_pad_type_0, strides = var_1620, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_143_cast)[name = tensor<string, []>("op_1624_cast")];
            tensor<int32, [2]> var_1625_split_sizes_0 = const()[name = tensor<string, []>("op_1625_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1625_axis_0 = const()[name = tensor<string, []>("op_1625_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_1625_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_1625_cast_1 = split(axis = var_1625_axis_0, split_sizes = var_1625_split_sizes_0, x = var_1624_cast)[name = tensor<string, []>("op_1625_cast")];
            tensor<string, []> var_1627_mode_0 = const()[name = tensor<string, []>("op_1627_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_1627_cast = gelu(mode = var_1627_mode_0, x = var_1625_cast_1)[name = tensor<string, []>("op_1627_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_145_cast = mul(x = var_1625_cast_0, y = var_1627_cast)[name = tensor<string, []>("input_145_cast")];
            tensor<int32, [2]> var_1631 = const()[name = tensor<string, []>("op_1631"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1633 = const()[name = tensor<string, []>("op_1633"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1635_pad_type_0 = const()[name = tensor<string, []>("op_1635_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1635_pad_0 = const()[name = tensor<string, []>("op_1635_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122398400))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125675264))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125675392)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1635_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1633, groups = var_1186, pad = var_1635_pad_0, pad_type = var_1635_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_145_cast)[name = tensor<string, []>("op_1635_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_37_cast = add(x = var_1635_cast, y = inputs_35_cast)[name = tensor<string, []>("inputs_37_cast")];
            tensor<int32, [1]> var_1645 = const()[name = tensor<string, []>("op_1645"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_37_cast = reduce_mean(axes = var_1645, keep_dims = var_1181, x = inputs_37_cast)[name = tensor<string, []>("channels_mean_37_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast)[name = tensor<string, []>("zero_mean_37_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast)[name = tensor<string, []>("zero_mean_sq_37_cast")];
            tensor<int32, [1]> var_1649 = const()[name = tensor<string, []>("op_1649"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1650_cast = reduce_mean(axes = var_1649, keep_dims = var_1181, x = zero_mean_sq_37_cast)[name = tensor<string, []>("op_1650_cast")];
            tensor<fp16, []> var_1651_to_fp16 = const()[name = tensor<string, []>("op_1651_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1652_cast = add(x = var_1650_cast, y = var_1651_to_fp16)[name = tensor<string, []>("op_1652_cast")];
            tensor<fp16, []> denom_37_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_37_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1652_cast)[name = tensor<string, []>("denom_37_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast)[name = tensor<string, []>("out_37_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1656_to_fp16 = const()[name = tensor<string, []>("op_1656_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125678016)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1657_cast = add(x = out_37_cast, y = var_1656_to_fp16)[name = tensor<string, []>("op_1657_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1659_to_fp16 = const()[name = tensor<string, []>("op_1659_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125680640)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_77_cast = mul(x = var_1657_cast, y = var_1659_to_fp16)[name = tensor<string, []>("hidden_states_77_cast")];
            tensor<int32, [2]> var_1666 = const()[name = tensor<string, []>("op_1666"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1668 = const()[name = tensor<string, []>("op_1668"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_25_pad_type_0 = const()[name = tensor<string, []>("q_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_25_pad_0 = const()[name = tensor<string, []>("q_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125683264))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126502528))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_25_cast = conv(dilations = var_1668, groups = var_1186, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1666, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_77_cast)[name = tensor<string, []>("q_25_cast")];
            tensor<int32, [2]> var_1672 = const()[name = tensor<string, []>("op_1672"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1674 = const()[name = tensor<string, []>("op_1674"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_25_pad_type_0 = const()[name = tensor<string, []>("k_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_25_pad_0 = const()[name = tensor<string, []>("k_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126502656))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127321920))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_25_cast = conv(dilations = var_1674, groups = var_1186, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1672, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_77_cast)[name = tensor<string, []>("k_25_cast")];
            tensor<int32, [2]> var_1678 = const()[name = tensor<string, []>("op_1678"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1680 = const()[name = tensor<string, []>("op_1680"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_25_pad_type_0 = const()[name = tensor<string, []>("v_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_25_pad_0 = const()[name = tensor<string, []>("v_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127322048))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128141312))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_25_cast = conv(dilations = var_1680, groups = var_1186, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1678, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_77_cast)[name = tensor<string, []>("v_25_cast")];
            tensor<int32, [4]> var_1684 = const()[name = tensor<string, []>("op_1684"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1685_cast = reshape(shape = var_1684, x = q_25_cast)[name = tensor<string, []>("op_1685_cast")];
            tensor<int32, [4]> var_1686 = const()[name = tensor<string, []>("op_1686"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1687_cast = reshape(shape = var_1686, x = k_25_cast)[name = tensor<string, []>("op_1687_cast")];
            tensor<int32, [4]> var_1688 = const()[name = tensor<string, []>("op_1688"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1689_cast = reshape(shape = var_1688, x = v_25_cast)[name = tensor<string, []>("op_1689_cast")];
            tensor<bool, []> attn_weights_49_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_49_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1685_cast, y = var_1687_cast)[name = tensor<string, []>("attn_weights_49_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_51_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_1693_cast = softmax(axis = var_1170, x = attn_weights_51_cast)[name = tensor<string, []>("op_1693_cast")];
            tensor<bool, []> attn_25_transpose_x_0 = const()[name = tensor<string, []>("attn_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_25_transpose_y_0 = const()[name = tensor<string, []>("attn_25_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_25_cast = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1689_cast, y = var_1693_cast)[name = tensor<string, []>("attn_25_cast")];
            tensor<int32, [4]> var_1697 = const()[name = tensor<string, []>("op_1697"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_147_cast = reshape(shape = var_1697, x = attn_25_cast)[name = tensor<string, []>("input_147_cast")];
            tensor<int32, [2]> var_1702 = const()[name = tensor<string, []>("op_1702"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1704 = const()[name = tensor<string, []>("op_1704"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1706_pad_type_0 = const()[name = tensor<string, []>("op_1706_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1706_pad_0 = const()[name = tensor<string, []>("op_1706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128141440))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128960704))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128960832)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1706_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_1704, groups = var_1186, pad = var_1706_pad_0, pad_type = var_1706_pad_type_0, strides = var_1702, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_147_cast)[name = tensor<string, []>("op_1706_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_39_cast = add(x = var_1706_cast, y = inputs_37_cast)[name = tensor<string, []>("inputs_39_cast")];
            tensor<int32, [1]> var_1710 = const()[name = tensor<string, []>("op_1710"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_39_cast = reduce_mean(axes = var_1710, keep_dims = var_1181, x = inputs_39_cast)[name = tensor<string, []>("channels_mean_39_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast)[name = tensor<string, []>("zero_mean_39_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast)[name = tensor<string, []>("zero_mean_sq_39_cast")];
            tensor<int32, [1]> var_1714 = const()[name = tensor<string, []>("op_1714"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1715_cast = reduce_mean(axes = var_1714, keep_dims = var_1181, x = zero_mean_sq_39_cast)[name = tensor<string, []>("op_1715_cast")];
            tensor<fp16, []> var_1716_to_fp16 = const()[name = tensor<string, []>("op_1716_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1717_cast = add(x = var_1715_cast, y = var_1716_to_fp16)[name = tensor<string, []>("op_1717_cast")];
            tensor<fp16, []> denom_39_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_39_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1717_cast)[name = tensor<string, []>("denom_39_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast)[name = tensor<string, []>("out_39_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1721_to_fp16 = const()[name = tensor<string, []>("op_1721_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128963456)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1722_cast = add(x = out_39_cast, y = var_1721_to_fp16)[name = tensor<string, []>("op_1722_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1724_to_fp16 = const()[name = tensor<string, []>("op_1724_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128966080)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_79_cast = mul(x = var_1722_cast, y = var_1724_to_fp16)[name = tensor<string, []>("hidden_states_79_cast")];
            tensor<int32, [2]> var_1731 = const()[name = tensor<string, []>("op_1731"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1733 = const()[name = tensor<string, []>("op_1733"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_27_pad_type_0 = const()[name = tensor<string, []>("q_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_27_pad_0 = const()[name = tensor<string, []>("q_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128968704))), lut = tensor<fp16, [4]>([-0x1.72cp-6, -0x1.d2cp-8, 0x1.d5p-8, 0x1.73cp-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_27_cast = conv(dilations = var_1733, groups = var_1186, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1731, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("q_27_cast")];
            tensor<int32, [2]> var_1737 = const()[name = tensor<string, []>("op_1737"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1739 = const()[name = tensor<string, []>("op_1739"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_27_pad_type_0 = const()[name = tensor<string, []>("k_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_27_pad_0 = const()[name = tensor<string, []>("k_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129378368))), lut = tensor<fp16, [4]>([-0x1.394p-6, -0x1.768p-8, 0x1.704p-8, 0x1.378p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_27_cast = conv(dilations = var_1739, groups = var_1186, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1737, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_27_cast")];
            tensor<int32, [2]> var_1743 = const()[name = tensor<string, []>("op_1743"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1745 = const()[name = tensor<string, []>("op_1745"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_27_pad_type_0 = const()[name = tensor<string, []>("v_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_27_pad_0 = const()[name = tensor<string, []>("v_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130033792))), lut = tensor<fp16, [4]>([-0x1.74cp-6, -0x1.ab8p-8, 0x1.a44p-8, 0x1.72p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_27_cast = conv(dilations = var_1745, groups = var_1186, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_1743, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_27_cast")];
            tensor<int32, [4]> var_1749 = const()[name = tensor<string, []>("op_1749"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1750_cast = reshape(shape = var_1749, x = q_27_cast)[name = tensor<string, []>("op_1750_cast")];
            tensor<int32, [4]> var_1751 = const()[name = tensor<string, []>("op_1751"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1752_cast = reshape(shape = var_1751, x = k_27_cast)[name = tensor<string, []>("op_1752_cast")];
            tensor<int32, [4]> var_1753 = const()[name = tensor<string, []>("op_1753"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1754_cast = reshape(shape = var_1753, x = v_27_cast)[name = tensor<string, []>("op_1754_cast")];
            tensor<bool, []> attn_weights_53_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_53_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_53_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_53_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_53_cast = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_1750_cast, y = var_1752_cast)[name = tensor<string, []>("attn_weights_53_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_55_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_1758_cast = softmax(axis = var_1170, x = attn_weights_55_cast)[name = tensor<string, []>("op_1758_cast")];
            tensor<bool, []> attn_27_transpose_x_0 = const()[name = tensor<string, []>("attn_27_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_27_transpose_y_0 = const()[name = tensor<string, []>("attn_27_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_27_cast = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1754_cast, y = var_1758_cast)[name = tensor<string, []>("attn_27_cast")];
            tensor<int32, [4]> var_1762 = const()[name = tensor<string, []>("op_1762"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_149_cast = reshape(shape = var_1762, x = attn_27_cast)[name = tensor<string, []>("input_149_cast")];
            tensor<int32, [2]> var_1767 = const()[name = tensor<string, []>("op_1767"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1769 = const()[name = tensor<string, []>("op_1769"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1771_pad_type_0 = const()[name = tensor<string, []>("op_1771_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1771_pad_0 = const()[name = tensor<string, []>("op_1771_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130689216))), lut = tensor<fp16, [2]>([-0x1.8bp-8, 0x1.8bp-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130894080)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1771_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_1769, groups = var_1186, pad = var_1771_pad_0, pad_type = var_1771_pad_type_0, strides = var_1767, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_149_cast)[name = tensor<string, []>("op_1771_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_41_cast = add(x = var_1771_cast, y = inputs_39_cast)[name = tensor<string, []>("inputs_41_cast")];
            tensor<int32, [1]> var_1775 = const()[name = tensor<string, []>("op_1775"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_41_cast = reduce_mean(axes = var_1775, keep_dims = var_1181, x = inputs_41_cast)[name = tensor<string, []>("channels_mean_41_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast)[name = tensor<string, []>("zero_mean_41_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast)[name = tensor<string, []>("zero_mean_sq_41_cast")];
            tensor<int32, [1]> var_1779 = const()[name = tensor<string, []>("op_1779"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1780_cast = reduce_mean(axes = var_1779, keep_dims = var_1181, x = zero_mean_sq_41_cast)[name = tensor<string, []>("op_1780_cast")];
            tensor<fp16, []> var_1781_to_fp16 = const()[name = tensor<string, []>("op_1781_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1782_cast = add(x = var_1780_cast, y = var_1781_to_fp16)[name = tensor<string, []>("op_1782_cast")];
            tensor<fp16, []> denom_41_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_41_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_1782_cast)[name = tensor<string, []>("denom_41_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast)[name = tensor<string, []>("out_41_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1786_to_fp16 = const()[name = tensor<string, []>("op_1786_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130896704)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1787_cast = add(x = out_41_cast, y = var_1786_to_fp16)[name = tensor<string, []>("op_1787_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1789_to_fp16 = const()[name = tensor<string, []>("op_1789_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130899328)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_151_cast = mul(x = var_1787_cast, y = var_1789_to_fp16)[name = tensor<string, []>("input_151_cast")];
            tensor<int32, [2]> var_1797 = const()[name = tensor<string, []>("op_1797"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1799 = const()[name = tensor<string, []>("op_1799"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1801_pad_type_0 = const()[name = tensor<string, []>("op_1801_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1801_pad_0 = const()[name = tensor<string, []>("op_1801_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130901952))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140732416))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140732608)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_1801_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_1799, groups = var_1186, pad = var_1801_pad_0, pad_type = var_1801_pad_type_0, strides = var_1797, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_151_cast)[name = tensor<string, []>("op_1801_cast")];
            tensor<int32, [2]> var_1802_split_sizes_0 = const()[name = tensor<string, []>("op_1802_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1802_axis_0 = const()[name = tensor<string, []>("op_1802_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_1802_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_1802_cast_1 = split(axis = var_1802_axis_0, split_sizes = var_1802_split_sizes_0, x = var_1801_cast)[name = tensor<string, []>("op_1802_cast")];
            tensor<string, []> var_1804_mode_0 = const()[name = tensor<string, []>("op_1804_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_1804_cast = gelu(mode = var_1804_mode_0, x = var_1802_cast_1)[name = tensor<string, []>("op_1804_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_153_cast = mul(x = var_1802_cast_0, y = var_1804_cast)[name = tensor<string, []>("input_153_cast")];
            tensor<int32, [2]> var_1808 = const()[name = tensor<string, []>("op_1808"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1810 = const()[name = tensor<string, []>("op_1810"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1812_pad_type_0 = const()[name = tensor<string, []>("op_1812_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1812_pad_0 = const()[name = tensor<string, []>("op_1812_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140753152))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144030016))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144030144)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1812_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_1810, groups = var_1186, pad = var_1812_pad_0, pad_type = var_1812_pad_type_0, strides = var_1808, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_153_cast)[name = tensor<string, []>("op_1812_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_43_cast = add(x = var_1812_cast, y = inputs_41_cast)[name = tensor<string, []>("inputs_43_cast")];
            tensor<int32, [1]> var_1822 = const()[name = tensor<string, []>("op_1822"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_43_cast = reduce_mean(axes = var_1822, keep_dims = var_1181, x = inputs_43_cast)[name = tensor<string, []>("channels_mean_43_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_43_cast = sub(x = inputs_43_cast, y = channels_mean_43_cast)[name = tensor<string, []>("zero_mean_43_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_43_cast = mul(x = zero_mean_43_cast, y = zero_mean_43_cast)[name = tensor<string, []>("zero_mean_sq_43_cast")];
            tensor<int32, [1]> var_1826 = const()[name = tensor<string, []>("op_1826"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1827_cast = reduce_mean(axes = var_1826, keep_dims = var_1181, x = zero_mean_sq_43_cast)[name = tensor<string, []>("op_1827_cast")];
            tensor<fp16, []> var_1828_to_fp16 = const()[name = tensor<string, []>("op_1828_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1829_cast = add(x = var_1827_cast, y = var_1828_to_fp16)[name = tensor<string, []>("op_1829_cast")];
            tensor<fp16, []> denom_43_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_43_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_43_cast = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_1829_cast)[name = tensor<string, []>("denom_43_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_43_cast = mul(x = zero_mean_43_cast, y = denom_43_cast)[name = tensor<string, []>("out_43_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1833_to_fp16 = const()[name = tensor<string, []>("op_1833_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144032768)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1834_cast = add(x = out_43_cast, y = var_1833_to_fp16)[name = tensor<string, []>("op_1834_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1836_to_fp16 = const()[name = tensor<string, []>("op_1836_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144035392)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_83_cast = mul(x = var_1834_cast, y = var_1836_to_fp16)[name = tensor<string, []>("hidden_states_83_cast")];
            tensor<int32, [2]> var_1843 = const()[name = tensor<string, []>("op_1843"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1845 = const()[name = tensor<string, []>("op_1845"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_29_pad_type_0 = const()[name = tensor<string, []>("q_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_29_pad_0 = const()[name = tensor<string, []>("q_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144038016))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144857280))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_29_cast = conv(dilations = var_1845, groups = var_1186, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_1843, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_83_cast)[name = tensor<string, []>("q_29_cast")];
            tensor<int32, [2]> var_1849 = const()[name = tensor<string, []>("op_1849"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1851 = const()[name = tensor<string, []>("op_1851"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_29_pad_type_0 = const()[name = tensor<string, []>("k_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_29_pad_0 = const()[name = tensor<string, []>("k_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144857408))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145676672))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_29_cast = conv(dilations = var_1851, groups = var_1186, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_1849, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_83_cast)[name = tensor<string, []>("k_29_cast")];
            tensor<int32, [2]> var_1855 = const()[name = tensor<string, []>("op_1855"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1857 = const()[name = tensor<string, []>("op_1857"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_29_pad_type_0 = const()[name = tensor<string, []>("v_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_29_pad_0 = const()[name = tensor<string, []>("v_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145676800))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146496064))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_29_cast = conv(dilations = var_1857, groups = var_1186, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_1855, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_83_cast)[name = tensor<string, []>("v_29_cast")];
            tensor<int32, [4]> var_1861 = const()[name = tensor<string, []>("op_1861"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1862_cast = reshape(shape = var_1861, x = q_29_cast)[name = tensor<string, []>("op_1862_cast")];
            tensor<int32, [4]> var_1863 = const()[name = tensor<string, []>("op_1863"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1864_cast = reshape(shape = var_1863, x = k_29_cast)[name = tensor<string, []>("op_1864_cast")];
            tensor<int32, [4]> var_1865 = const()[name = tensor<string, []>("op_1865"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1866_cast = reshape(shape = var_1865, x = v_29_cast)[name = tensor<string, []>("op_1866_cast")];
            tensor<bool, []> attn_weights_57_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_57_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_57_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_57_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_57_cast = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_1862_cast, y = var_1864_cast)[name = tensor<string, []>("attn_weights_57_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_59_cast = mul(x = attn_weights_57_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_59_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_1870_cast = softmax(axis = var_1170, x = attn_weights_59_cast)[name = tensor<string, []>("op_1870_cast")];
            tensor<bool, []> attn_29_transpose_x_0 = const()[name = tensor<string, []>("attn_29_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_29_transpose_y_0 = const()[name = tensor<string, []>("attn_29_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_29_cast = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1866_cast, y = var_1870_cast)[name = tensor<string, []>("attn_29_cast")];
            tensor<int32, [4]> var_1874 = const()[name = tensor<string, []>("op_1874"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_155_cast = reshape(shape = var_1874, x = attn_29_cast)[name = tensor<string, []>("input_155_cast")];
            tensor<int32, [2]> var_1879 = const()[name = tensor<string, []>("op_1879"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1881 = const()[name = tensor<string, []>("op_1881"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1883_pad_type_0 = const()[name = tensor<string, []>("op_1883_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1883_pad_0 = const()[name = tensor<string, []>("op_1883_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146496192))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147315456))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147315584)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1883_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_1881, groups = var_1186, pad = var_1883_pad_0, pad_type = var_1883_pad_type_0, strides = var_1879, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_155_cast)[name = tensor<string, []>("op_1883_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_45_cast = add(x = var_1883_cast, y = inputs_43_cast)[name = tensor<string, []>("inputs_45_cast")];
            tensor<int32, [1]> var_1887 = const()[name = tensor<string, []>("op_1887"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_45_cast = reduce_mean(axes = var_1887, keep_dims = var_1181, x = inputs_45_cast)[name = tensor<string, []>("channels_mean_45_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_45_cast = sub(x = inputs_45_cast, y = channels_mean_45_cast)[name = tensor<string, []>("zero_mean_45_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_45_cast = mul(x = zero_mean_45_cast, y = zero_mean_45_cast)[name = tensor<string, []>("zero_mean_sq_45_cast")];
            tensor<int32, [1]> var_1891 = const()[name = tensor<string, []>("op_1891"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1892_cast = reduce_mean(axes = var_1891, keep_dims = var_1181, x = zero_mean_sq_45_cast)[name = tensor<string, []>("op_1892_cast")];
            tensor<fp16, []> var_1893_to_fp16 = const()[name = tensor<string, []>("op_1893_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1894_cast = add(x = var_1892_cast, y = var_1893_to_fp16)[name = tensor<string, []>("op_1894_cast")];
            tensor<fp16, []> denom_45_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_45_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_45_cast = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_1894_cast)[name = tensor<string, []>("denom_45_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_45_cast = mul(x = zero_mean_45_cast, y = denom_45_cast)[name = tensor<string, []>("out_45_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1898_to_fp16 = const()[name = tensor<string, []>("op_1898_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147318208)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1899_cast = add(x = out_45_cast, y = var_1898_to_fp16)[name = tensor<string, []>("op_1899_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1901_to_fp16 = const()[name = tensor<string, []>("op_1901_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147320832)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_85_cast = mul(x = var_1899_cast, y = var_1901_to_fp16)[name = tensor<string, []>("hidden_states_85_cast")];
            tensor<int32, [2]> var_1908 = const()[name = tensor<string, []>("op_1908"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1910 = const()[name = tensor<string, []>("op_1910"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_31_pad_type_0 = const()[name = tensor<string, []>("q_31_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_31_pad_0 = const()[name = tensor<string, []>("q_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147323456))), lut = tensor<fp16, [2]>([-0x1.918p-7, 0x1.924p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_31_cast = conv(dilations = var_1910, groups = var_1186, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_1908, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("q_31_cast")];
            tensor<int32, [2]> var_1914 = const()[name = tensor<string, []>("op_1914"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1916 = const()[name = tensor<string, []>("op_1916"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_31_pad_type_0 = const()[name = tensor<string, []>("k_31_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_31_pad_0 = const()[name = tensor<string, []>("k_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147528320))), lut = tensor<fp16, [4]>([-0x1.214p-6, -0x1.5c8p-8, 0x1.5bcp-8, 0x1.218p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_31_cast = conv(dilations = var_1916, groups = var_1186, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_1914, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_31_cast")];
            tensor<int32, [2]> var_1920 = const()[name = tensor<string, []>("op_1920"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1922 = const()[name = tensor<string, []>("op_1922"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_31_pad_type_0 = const()[name = tensor<string, []>("v_31_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_31_pad_0 = const()[name = tensor<string, []>("v_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148183744))), lut = tensor<fp16, [4]>([-0x1.6d4p-6, -0x1.9d8p-8, 0x1.a04p-8, 0x1.6e4p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_31_cast = conv(dilations = var_1922, groups = var_1186, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_1920, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_31_cast")];
            tensor<int32, [4]> var_1926 = const()[name = tensor<string, []>("op_1926"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_1927_cast = reshape(shape = var_1926, x = q_31_cast)[name = tensor<string, []>("op_1927_cast")];
            tensor<int32, [4]> var_1928 = const()[name = tensor<string, []>("op_1928"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1929_cast = reshape(shape = var_1928, x = k_31_cast)[name = tensor<string, []>("op_1929_cast")];
            tensor<int32, [4]> var_1930 = const()[name = tensor<string, []>("op_1930"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1931_cast = reshape(shape = var_1930, x = v_31_cast)[name = tensor<string, []>("op_1931_cast")];
            tensor<bool, []> attn_weights_61_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_61_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_1927_cast, y = var_1929_cast)[name = tensor<string, []>("attn_weights_61_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_63_cast = mul(x = attn_weights_61_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_63_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_1935_cast = softmax(axis = var_1170, x = attn_weights_63_cast)[name = tensor<string, []>("op_1935_cast")];
            tensor<bool, []> attn_31_transpose_x_0 = const()[name = tensor<string, []>("attn_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_31_transpose_y_0 = const()[name = tensor<string, []>("attn_31_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_31_cast = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1931_cast, y = var_1935_cast)[name = tensor<string, []>("attn_31_cast")];
            tensor<int32, [4]> var_1939 = const()[name = tensor<string, []>("op_1939"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_157_cast = reshape(shape = var_1939, x = attn_31_cast)[name = tensor<string, []>("input_157_cast")];
            tensor<int32, [2]> var_1944 = const()[name = tensor<string, []>("op_1944"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1946 = const()[name = tensor<string, []>("op_1946"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1948_pad_type_0 = const()[name = tensor<string, []>("op_1948_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1948_pad_0 = const()[name = tensor<string, []>("op_1948_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148839168))), lut = tensor<fp16, [2]>([-0x1.8a8p-8, 0x1.89cp-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149044032)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1948_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_1946, groups = var_1186, pad = var_1948_pad_0, pad_type = var_1948_pad_type_0, strides = var_1944, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_157_cast)[name = tensor<string, []>("op_1948_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_47_cast = add(x = var_1948_cast, y = inputs_45_cast)[name = tensor<string, []>("inputs_47_cast")];
            tensor<int32, [1]> var_1952 = const()[name = tensor<string, []>("op_1952"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_47_cast = reduce_mean(axes = var_1952, keep_dims = var_1181, x = inputs_47_cast)[name = tensor<string, []>("channels_mean_47_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_47_cast = sub(x = inputs_47_cast, y = channels_mean_47_cast)[name = tensor<string, []>("zero_mean_47_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_47_cast = mul(x = zero_mean_47_cast, y = zero_mean_47_cast)[name = tensor<string, []>("zero_mean_sq_47_cast")];
            tensor<int32, [1]> var_1956 = const()[name = tensor<string, []>("op_1956"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1957_cast = reduce_mean(axes = var_1956, keep_dims = var_1181, x = zero_mean_sq_47_cast)[name = tensor<string, []>("op_1957_cast")];
            tensor<fp16, []> var_1958_to_fp16 = const()[name = tensor<string, []>("op_1958_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1959_cast = add(x = var_1957_cast, y = var_1958_to_fp16)[name = tensor<string, []>("op_1959_cast")];
            tensor<fp16, []> denom_47_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_47_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_47_cast = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_1959_cast)[name = tensor<string, []>("denom_47_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_47_cast = mul(x = zero_mean_47_cast, y = denom_47_cast)[name = tensor<string, []>("out_47_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1963_to_fp16 = const()[name = tensor<string, []>("op_1963_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149046656)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1964_cast = add(x = out_47_cast, y = var_1963_to_fp16)[name = tensor<string, []>("op_1964_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1966_to_fp16 = const()[name = tensor<string, []>("op_1966_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149049280)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_159_cast = mul(x = var_1964_cast, y = var_1966_to_fp16)[name = tensor<string, []>("input_159_cast")];
            tensor<int32, [2]> var_1974 = const()[name = tensor<string, []>("op_1974"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1976 = const()[name = tensor<string, []>("op_1976"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1978_pad_type_0 = const()[name = tensor<string, []>("op_1978_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1978_pad_0 = const()[name = tensor<string, []>("op_1978_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149051904))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155605568))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155605696)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_1978_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_1976, groups = var_1186, pad = var_1978_pad_0, pad_type = var_1978_pad_type_0, strides = var_1974, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_159_cast)[name = tensor<string, []>("op_1978_cast")];
            tensor<int32, [2]> var_1979_split_sizes_0 = const()[name = tensor<string, []>("op_1979_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1979_axis_0 = const()[name = tensor<string, []>("op_1979_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_1979_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_1979_cast_1 = split(axis = var_1979_axis_0, split_sizes = var_1979_split_sizes_0, x = var_1978_cast)[name = tensor<string, []>("op_1979_cast")];
            tensor<string, []> var_1981_mode_0 = const()[name = tensor<string, []>("op_1981_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_1981_cast = gelu(mode = var_1981_mode_0, x = var_1979_cast_1)[name = tensor<string, []>("op_1981_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_161_cast = mul(x = var_1979_cast_0, y = var_1981_cast)[name = tensor<string, []>("input_161_cast")];
            tensor<int32, [2]> var_1985 = const()[name = tensor<string, []>("op_1985"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1987 = const()[name = tensor<string, []>("op_1987"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1989_pad_type_0 = const()[name = tensor<string, []>("op_1989_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1989_pad_0 = const()[name = tensor<string, []>("op_1989_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155626240))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160541504))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160541696)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_1989_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_1987, groups = var_1186, pad = var_1989_pad_0, pad_type = var_1989_pad_type_0, strides = var_1985, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_161_cast)[name = tensor<string, []>("op_1989_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_49_cast = add(x = var_1989_cast, y = inputs_47_cast)[name = tensor<string, []>("inputs_49_cast")];
            tensor<int32, [1]> var_1999 = const()[name = tensor<string, []>("op_1999"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_49_cast = reduce_mean(axes = var_1999, keep_dims = var_1181, x = inputs_49_cast)[name = tensor<string, []>("channels_mean_49_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_49_cast = sub(x = inputs_49_cast, y = channels_mean_49_cast)[name = tensor<string, []>("zero_mean_49_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_49_cast = mul(x = zero_mean_49_cast, y = zero_mean_49_cast)[name = tensor<string, []>("zero_mean_sq_49_cast")];
            tensor<int32, [1]> var_2003 = const()[name = tensor<string, []>("op_2003"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2004_cast = reduce_mean(axes = var_2003, keep_dims = var_1181, x = zero_mean_sq_49_cast)[name = tensor<string, []>("op_2004_cast")];
            tensor<fp16, []> var_2005_to_fp16 = const()[name = tensor<string, []>("op_2005_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2006_cast = add(x = var_2004_cast, y = var_2005_to_fp16)[name = tensor<string, []>("op_2006_cast")];
            tensor<fp16, []> denom_49_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_49_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_49_cast = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2006_cast)[name = tensor<string, []>("denom_49_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_49_cast = mul(x = zero_mean_49_cast, y = denom_49_cast)[name = tensor<string, []>("out_49_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2010_to_fp16 = const()[name = tensor<string, []>("op_2010_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160544320)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2011_cast = add(x = out_49_cast, y = var_2010_to_fp16)[name = tensor<string, []>("op_2011_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2013_to_fp16 = const()[name = tensor<string, []>("op_2013_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160546944)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_89_cast = mul(x = var_2011_cast, y = var_2013_to_fp16)[name = tensor<string, []>("hidden_states_89_cast")];
            tensor<int32, [2]> var_2020 = const()[name = tensor<string, []>("op_2020"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2022 = const()[name = tensor<string, []>("op_2022"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_33_pad_type_0 = const()[name = tensor<string, []>("q_33_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_33_pad_0 = const()[name = tensor<string, []>("q_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160549568))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161368832))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_33_cast = conv(dilations = var_2022, groups = var_1186, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2020, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_89_cast)[name = tensor<string, []>("q_33_cast")];
            tensor<int32, [2]> var_2026 = const()[name = tensor<string, []>("op_2026"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2028 = const()[name = tensor<string, []>("op_2028"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_33_pad_type_0 = const()[name = tensor<string, []>("k_33_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_33_pad_0 = const()[name = tensor<string, []>("k_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161368960))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162188224))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_33_cast = conv(dilations = var_2028, groups = var_1186, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2026, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_89_cast)[name = tensor<string, []>("k_33_cast")];
            tensor<int32, [2]> var_2032 = const()[name = tensor<string, []>("op_2032"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2034 = const()[name = tensor<string, []>("op_2034"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_33_pad_type_0 = const()[name = tensor<string, []>("v_33_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_33_pad_0 = const()[name = tensor<string, []>("v_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162188352))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163007616))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_33_cast = conv(dilations = var_2034, groups = var_1186, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2032, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_89_cast)[name = tensor<string, []>("v_33_cast")];
            tensor<int32, [4]> var_2038 = const()[name = tensor<string, []>("op_2038"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2039_cast = reshape(shape = var_2038, x = q_33_cast)[name = tensor<string, []>("op_2039_cast")];
            tensor<int32, [4]> var_2040 = const()[name = tensor<string, []>("op_2040"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2041_cast = reshape(shape = var_2040, x = k_33_cast)[name = tensor<string, []>("op_2041_cast")];
            tensor<int32, [4]> var_2042 = const()[name = tensor<string, []>("op_2042"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2043_cast = reshape(shape = var_2042, x = v_33_cast)[name = tensor<string, []>("op_2043_cast")];
            tensor<bool, []> attn_weights_65_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_65_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_65_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_65_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_65_cast = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2039_cast, y = var_2041_cast)[name = tensor<string, []>("attn_weights_65_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_67_cast = mul(x = attn_weights_65_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_67_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_2047_cast = softmax(axis = var_1170, x = attn_weights_67_cast)[name = tensor<string, []>("op_2047_cast")];
            tensor<bool, []> attn_33_transpose_x_0 = const()[name = tensor<string, []>("attn_33_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_33_transpose_y_0 = const()[name = tensor<string, []>("attn_33_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_33_cast = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2043_cast, y = var_2047_cast)[name = tensor<string, []>("attn_33_cast")];
            tensor<int32, [4]> var_2051 = const()[name = tensor<string, []>("op_2051"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_163_cast = reshape(shape = var_2051, x = attn_33_cast)[name = tensor<string, []>("input_163_cast")];
            tensor<int32, [2]> var_2056 = const()[name = tensor<string, []>("op_2056"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2058 = const()[name = tensor<string, []>("op_2058"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2060_pad_type_0 = const()[name = tensor<string, []>("op_2060_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2060_pad_0 = const()[name = tensor<string, []>("op_2060_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163007744))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163827008))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163827136)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2060_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_2058, groups = var_1186, pad = var_2060_pad_0, pad_type = var_2060_pad_type_0, strides = var_2056, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_163_cast)[name = tensor<string, []>("op_2060_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_51_cast = add(x = var_2060_cast, y = inputs_49_cast)[name = tensor<string, []>("inputs_51_cast")];
            tensor<int32, [1]> var_2064 = const()[name = tensor<string, []>("op_2064"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_51_cast = reduce_mean(axes = var_2064, keep_dims = var_1181, x = inputs_51_cast)[name = tensor<string, []>("channels_mean_51_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_51_cast = sub(x = inputs_51_cast, y = channels_mean_51_cast)[name = tensor<string, []>("zero_mean_51_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_51_cast = mul(x = zero_mean_51_cast, y = zero_mean_51_cast)[name = tensor<string, []>("zero_mean_sq_51_cast")];
            tensor<int32, [1]> var_2068 = const()[name = tensor<string, []>("op_2068"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2069_cast = reduce_mean(axes = var_2068, keep_dims = var_1181, x = zero_mean_sq_51_cast)[name = tensor<string, []>("op_2069_cast")];
            tensor<fp16, []> var_2070_to_fp16 = const()[name = tensor<string, []>("op_2070_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2071_cast = add(x = var_2069_cast, y = var_2070_to_fp16)[name = tensor<string, []>("op_2071_cast")];
            tensor<fp16, []> denom_51_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_51_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_51_cast = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2071_cast)[name = tensor<string, []>("denom_51_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_51_cast = mul(x = zero_mean_51_cast, y = denom_51_cast)[name = tensor<string, []>("out_51_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2075_to_fp16 = const()[name = tensor<string, []>("op_2075_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163829760)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2076_cast = add(x = out_51_cast, y = var_2075_to_fp16)[name = tensor<string, []>("op_2076_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2078_to_fp16 = const()[name = tensor<string, []>("op_2078_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163832384)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_91_cast = mul(x = var_2076_cast, y = var_2078_to_fp16)[name = tensor<string, []>("hidden_states_91_cast")];
            tensor<int32, [2]> var_2085 = const()[name = tensor<string, []>("op_2085"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2087 = const()[name = tensor<string, []>("op_2087"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_35_pad_type_0 = const()[name = tensor<string, []>("q_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_35_pad_0 = const()[name = tensor<string, []>("q_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163835008))), lut = tensor<fp16, [2]>([-0x1.83p-7, 0x1.82cp-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_35_cast = conv(dilations = var_2087, groups = var_1186, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2085, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("q_35_cast")];
            tensor<int32, [2]> var_2091 = const()[name = tensor<string, []>("op_2091"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2093 = const()[name = tensor<string, []>("op_2093"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_35_pad_type_0 = const()[name = tensor<string, []>("k_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_35_pad_0 = const()[name = tensor<string, []>("k_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164039872))), lut = tensor<fp16, [4]>([-0x1.064p-6, -0x1.42p-8, 0x1.42cp-8, 0x1.064p-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_35_cast = conv(dilations = var_2093, groups = var_1186, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2091, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_35_cast")];
            tensor<int32, [2]> var_2097 = const()[name = tensor<string, []>("op_2097"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2099 = const()[name = tensor<string, []>("op_2099"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_35_pad_type_0 = const()[name = tensor<string, []>("v_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_35_pad_0 = const()[name = tensor<string, []>("v_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164695296))), lut = tensor<fp16, [2]>([-0x1.3e4p-7, 0x1.3e8p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_35_cast = conv(dilations = var_2099, groups = var_1186, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2097, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_35_cast")];
            tensor<int32, [4]> var_2103 = const()[name = tensor<string, []>("op_2103"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2104_cast = reshape(shape = var_2103, x = q_35_cast)[name = tensor<string, []>("op_2104_cast")];
            tensor<int32, [4]> var_2105 = const()[name = tensor<string, []>("op_2105"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2106_cast = reshape(shape = var_2105, x = k_35_cast)[name = tensor<string, []>("op_2106_cast")];
            tensor<int32, [4]> var_2107 = const()[name = tensor<string, []>("op_2107"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2108_cast = reshape(shape = var_2107, x = v_35_cast)[name = tensor<string, []>("op_2108_cast")];
            tensor<bool, []> attn_weights_69_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_69_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_69_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_69_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_69_cast = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2104_cast, y = var_2106_cast)[name = tensor<string, []>("attn_weights_69_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_71_cast = mul(x = attn_weights_69_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_71_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_2112_cast = softmax(axis = var_1170, x = attn_weights_71_cast)[name = tensor<string, []>("op_2112_cast")];
            tensor<bool, []> attn_35_transpose_x_0 = const()[name = tensor<string, []>("attn_35_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_35_transpose_y_0 = const()[name = tensor<string, []>("attn_35_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_35_cast = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2108_cast, y = var_2112_cast)[name = tensor<string, []>("attn_35_cast")];
            tensor<int32, [4]> var_2116 = const()[name = tensor<string, []>("op_2116"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_165_cast = reshape(shape = var_2116, x = attn_35_cast)[name = tensor<string, []>("input_165_cast")];
            tensor<int32, [2]> var_2121 = const()[name = tensor<string, []>("op_2121"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2123 = const()[name = tensor<string, []>("op_2123"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2125_pad_type_0 = const()[name = tensor<string, []>("op_2125_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2125_pad_0 = const()[name = tensor<string, []>("op_2125_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165023040))), lut = tensor<fp16, [2]>([-0x1.684p-8, 0x1.68cp-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165227904)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2125_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_2123, groups = var_1186, pad = var_2125_pad_0, pad_type = var_2125_pad_type_0, strides = var_2121, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_165_cast)[name = tensor<string, []>("op_2125_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_53_cast = add(x = var_2125_cast, y = inputs_51_cast)[name = tensor<string, []>("inputs_53_cast")];
            tensor<int32, [1]> var_2129 = const()[name = tensor<string, []>("op_2129"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_53_cast = reduce_mean(axes = var_2129, keep_dims = var_1181, x = inputs_53_cast)[name = tensor<string, []>("channels_mean_53_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_53_cast = sub(x = inputs_53_cast, y = channels_mean_53_cast)[name = tensor<string, []>("zero_mean_53_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_53_cast = mul(x = zero_mean_53_cast, y = zero_mean_53_cast)[name = tensor<string, []>("zero_mean_sq_53_cast")];
            tensor<int32, [1]> var_2133 = const()[name = tensor<string, []>("op_2133"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2134_cast = reduce_mean(axes = var_2133, keep_dims = var_1181, x = zero_mean_sq_53_cast)[name = tensor<string, []>("op_2134_cast")];
            tensor<fp16, []> var_2135_to_fp16 = const()[name = tensor<string, []>("op_2135_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2136_cast = add(x = var_2134_cast, y = var_2135_to_fp16)[name = tensor<string, []>("op_2136_cast")];
            tensor<fp16, []> denom_53_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_53_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_53_cast = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2136_cast)[name = tensor<string, []>("denom_53_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_53_cast = mul(x = zero_mean_53_cast, y = denom_53_cast)[name = tensor<string, []>("out_53_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2140_to_fp16 = const()[name = tensor<string, []>("op_2140_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165230528)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2141_cast = add(x = out_53_cast, y = var_2140_to_fp16)[name = tensor<string, []>("op_2141_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2143_to_fp16 = const()[name = tensor<string, []>("op_2143_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165233152)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_167_cast = mul(x = var_2141_cast, y = var_2143_to_fp16)[name = tensor<string, []>("input_167_cast")];
            tensor<int32, [2]> var_2151 = const()[name = tensor<string, []>("op_2151"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2153 = const()[name = tensor<string, []>("op_2153"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2155_pad_type_0 = const()[name = tensor<string, []>("op_2155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2155_pad_0 = const()[name = tensor<string, []>("op_2155_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165235776))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175066240))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175066432)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_2155_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_2153, groups = var_1186, pad = var_2155_pad_0, pad_type = var_2155_pad_type_0, strides = var_2151, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_167_cast)[name = tensor<string, []>("op_2155_cast")];
            tensor<int32, [2]> var_2156_split_sizes_0 = const()[name = tensor<string, []>("op_2156_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2156_axis_0 = const()[name = tensor<string, []>("op_2156_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_2156_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_2156_cast_1 = split(axis = var_2156_axis_0, split_sizes = var_2156_split_sizes_0, x = var_2155_cast)[name = tensor<string, []>("op_2156_cast")];
            tensor<string, []> var_2158_mode_0 = const()[name = tensor<string, []>("op_2158_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_2158_cast = gelu(mode = var_2158_mode_0, x = var_2156_cast_1)[name = tensor<string, []>("op_2158_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_169_cast = mul(x = var_2156_cast_0, y = var_2158_cast)[name = tensor<string, []>("input_169_cast")];
            tensor<int32, [2]> var_2162 = const()[name = tensor<string, []>("op_2162"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2164 = const()[name = tensor<string, []>("op_2164"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2166_pad_type_0 = const()[name = tensor<string, []>("op_2166_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2166_pad_0 = const()[name = tensor<string, []>("op_2166_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175086976))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180002240))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180002432)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2166_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_2164, groups = var_1186, pad = var_2166_pad_0, pad_type = var_2166_pad_type_0, strides = var_2162, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_169_cast)[name = tensor<string, []>("op_2166_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_55_cast = add(x = var_2166_cast, y = inputs_53_cast)[name = tensor<string, []>("inputs_55_cast")];
            tensor<int32, [1]> var_2176 = const()[name = tensor<string, []>("op_2176"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_55_cast = reduce_mean(axes = var_2176, keep_dims = var_1181, x = inputs_55_cast)[name = tensor<string, []>("channels_mean_55_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_55_cast = sub(x = inputs_55_cast, y = channels_mean_55_cast)[name = tensor<string, []>("zero_mean_55_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_55_cast = mul(x = zero_mean_55_cast, y = zero_mean_55_cast)[name = tensor<string, []>("zero_mean_sq_55_cast")];
            tensor<int32, [1]> var_2180 = const()[name = tensor<string, []>("op_2180"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2181_cast = reduce_mean(axes = var_2180, keep_dims = var_1181, x = zero_mean_sq_55_cast)[name = tensor<string, []>("op_2181_cast")];
            tensor<fp16, []> var_2182_to_fp16 = const()[name = tensor<string, []>("op_2182_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2183_cast = add(x = var_2181_cast, y = var_2182_to_fp16)[name = tensor<string, []>("op_2183_cast")];
            tensor<fp16, []> denom_55_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_55_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_55_cast = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2183_cast)[name = tensor<string, []>("denom_55_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_55_cast = mul(x = zero_mean_55_cast, y = denom_55_cast)[name = tensor<string, []>("out_55_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2187_to_fp16 = const()[name = tensor<string, []>("op_2187_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180005056)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2188_cast = add(x = out_55_cast, y = var_2187_to_fp16)[name = tensor<string, []>("op_2188_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2190_to_fp16 = const()[name = tensor<string, []>("op_2190_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180007680)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_95_cast = mul(x = var_2188_cast, y = var_2190_to_fp16)[name = tensor<string, []>("hidden_states_95_cast")];
            tensor<int32, [2]> var_2197 = const()[name = tensor<string, []>("op_2197"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2199 = const()[name = tensor<string, []>("op_2199"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_37_pad_type_0 = const()[name = tensor<string, []>("q_37_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_37_pad_0 = const()[name = tensor<string, []>("q_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180010304))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180829568))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_37_cast = conv(dilations = var_2199, groups = var_1186, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2197, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_95_cast)[name = tensor<string, []>("q_37_cast")];
            tensor<int32, [2]> var_2203 = const()[name = tensor<string, []>("op_2203"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2205 = const()[name = tensor<string, []>("op_2205"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_37_pad_type_0 = const()[name = tensor<string, []>("k_37_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_37_pad_0 = const()[name = tensor<string, []>("k_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180829696))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181648960))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_37_cast = conv(dilations = var_2205, groups = var_1186, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2203, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_95_cast)[name = tensor<string, []>("k_37_cast")];
            tensor<int32, [2]> var_2209 = const()[name = tensor<string, []>("op_2209"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2211 = const()[name = tensor<string, []>("op_2211"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_37_pad_type_0 = const()[name = tensor<string, []>("v_37_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_37_pad_0 = const()[name = tensor<string, []>("v_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181649088))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182468352))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_37_cast = conv(dilations = var_2211, groups = var_1186, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2209, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_95_cast)[name = tensor<string, []>("v_37_cast")];
            tensor<int32, [4]> var_2215 = const()[name = tensor<string, []>("op_2215"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2216_cast = reshape(shape = var_2215, x = q_37_cast)[name = tensor<string, []>("op_2216_cast")];
            tensor<int32, [4]> var_2217 = const()[name = tensor<string, []>("op_2217"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2218_cast = reshape(shape = var_2217, x = k_37_cast)[name = tensor<string, []>("op_2218_cast")];
            tensor<int32, [4]> var_2219 = const()[name = tensor<string, []>("op_2219"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2220_cast = reshape(shape = var_2219, x = v_37_cast)[name = tensor<string, []>("op_2220_cast")];
            tensor<bool, []> attn_weights_73_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_73_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2216_cast, y = var_2218_cast)[name = tensor<string, []>("attn_weights_73_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_75_cast = mul(x = attn_weights_73_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_75_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_2224_cast = softmax(axis = var_1170, x = attn_weights_75_cast)[name = tensor<string, []>("op_2224_cast")];
            tensor<bool, []> attn_37_transpose_x_0 = const()[name = tensor<string, []>("attn_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_37_transpose_y_0 = const()[name = tensor<string, []>("attn_37_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_37_cast = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2220_cast, y = var_2224_cast)[name = tensor<string, []>("attn_37_cast")];
            tensor<int32, [4]> var_2228 = const()[name = tensor<string, []>("op_2228"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_171_cast = reshape(shape = var_2228, x = attn_37_cast)[name = tensor<string, []>("input_171_cast")];
            tensor<int32, [2]> var_2233 = const()[name = tensor<string, []>("op_2233"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2235 = const()[name = tensor<string, []>("op_2235"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2237_pad_type_0 = const()[name = tensor<string, []>("op_2237_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2237_pad_0 = const()[name = tensor<string, []>("op_2237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182468480))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183287744))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183287872)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2237_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_2235, groups = var_1186, pad = var_2237_pad_0, pad_type = var_2237_pad_type_0, strides = var_2233, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_171_cast)[name = tensor<string, []>("op_2237_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_57_cast = add(x = var_2237_cast, y = inputs_55_cast)[name = tensor<string, []>("inputs_57_cast")];
            tensor<int32, [1]> var_2241 = const()[name = tensor<string, []>("op_2241"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_57_cast = reduce_mean(axes = var_2241, keep_dims = var_1181, x = inputs_57_cast)[name = tensor<string, []>("channels_mean_57_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_57_cast = sub(x = inputs_57_cast, y = channels_mean_57_cast)[name = tensor<string, []>("zero_mean_57_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_57_cast = mul(x = zero_mean_57_cast, y = zero_mean_57_cast)[name = tensor<string, []>("zero_mean_sq_57_cast")];
            tensor<int32, [1]> var_2245 = const()[name = tensor<string, []>("op_2245"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2246_cast = reduce_mean(axes = var_2245, keep_dims = var_1181, x = zero_mean_sq_57_cast)[name = tensor<string, []>("op_2246_cast")];
            tensor<fp16, []> var_2247_to_fp16 = const()[name = tensor<string, []>("op_2247_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2248_cast = add(x = var_2246_cast, y = var_2247_to_fp16)[name = tensor<string, []>("op_2248_cast")];
            tensor<fp16, []> denom_57_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_57_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_57_cast = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2248_cast)[name = tensor<string, []>("denom_57_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_57_cast = mul(x = zero_mean_57_cast, y = denom_57_cast)[name = tensor<string, []>("out_57_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2252_to_fp16 = const()[name = tensor<string, []>("op_2252_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183290496)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2253_cast = add(x = out_57_cast, y = var_2252_to_fp16)[name = tensor<string, []>("op_2253_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2255_to_fp16 = const()[name = tensor<string, []>("op_2255_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183293120)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_97_cast = mul(x = var_2253_cast, y = var_2255_to_fp16)[name = tensor<string, []>("hidden_states_97_cast")];
            tensor<int32, [2]> var_2262 = const()[name = tensor<string, []>("op_2262"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2264 = const()[name = tensor<string, []>("op_2264"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_39_pad_type_0 = const()[name = tensor<string, []>("q_39_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_39_pad_0 = const()[name = tensor<string, []>("q_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183295744))), lut = tensor<fp16, [2]>([-0x1.7p-7, 0x1.6fcp-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_39_cast = conv(dilations = var_2264, groups = var_1186, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_2262, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("q_39_cast")];
            tensor<int32, [2]> var_2268 = const()[name = tensor<string, []>("op_2268"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2270 = const()[name = tensor<string, []>("op_2270"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_39_pad_type_0 = const()[name = tensor<string, []>("k_39_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_39_pad_0 = const()[name = tensor<string, []>("k_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183500608))), lut = tensor<fp16, [4]>([-0x1.cbcp-7, -0x1.22p-8, 0x1.214p-8, 0x1.cbcp-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_39_cast = conv(dilations = var_2270, groups = var_1186, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_2268, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_39_cast")];
            tensor<int32, [2]> var_2274 = const()[name = tensor<string, []>("op_2274"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2276 = const()[name = tensor<string, []>("op_2276"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_39_pad_type_0 = const()[name = tensor<string, []>("v_39_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_39_pad_0 = const()[name = tensor<string, []>("v_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184156032))), lut = tensor<fp16, [2]>([-0x1.1e8p-7, 0x1.1e8p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_39_cast = conv(dilations = var_2276, groups = var_1186, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_2274, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_39_cast")];
            tensor<int32, [4]> var_2280 = const()[name = tensor<string, []>("op_2280"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2281_cast = reshape(shape = var_2280, x = q_39_cast)[name = tensor<string, []>("op_2281_cast")];
            tensor<int32, [4]> var_2282 = const()[name = tensor<string, []>("op_2282"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2283_cast = reshape(shape = var_2282, x = k_39_cast)[name = tensor<string, []>("op_2283_cast")];
            tensor<int32, [4]> var_2284 = const()[name = tensor<string, []>("op_2284"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2285_cast = reshape(shape = var_2284, x = v_39_cast)[name = tensor<string, []>("op_2285_cast")];
            tensor<bool, []> attn_weights_77_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_77_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_77_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_77_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_77_cast = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_2281_cast, y = var_2283_cast)[name = tensor<string, []>("attn_weights_77_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_79_cast = mul(x = attn_weights_77_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_79_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_2289_cast = softmax(axis = var_1170, x = attn_weights_79_cast)[name = tensor<string, []>("op_2289_cast")];
            tensor<bool, []> attn_39_transpose_x_0 = const()[name = tensor<string, []>("attn_39_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_39_transpose_y_0 = const()[name = tensor<string, []>("attn_39_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_39_cast = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2285_cast, y = var_2289_cast)[name = tensor<string, []>("attn_39_cast")];
            tensor<int32, [4]> var_2293 = const()[name = tensor<string, []>("op_2293"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_173_cast = reshape(shape = var_2293, x = attn_39_cast)[name = tensor<string, []>("input_173_cast")];
            tensor<int32, [2]> var_2298 = const()[name = tensor<string, []>("op_2298"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2300 = const()[name = tensor<string, []>("op_2300"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2302_pad_type_0 = const()[name = tensor<string, []>("op_2302_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2302_pad_0 = const()[name = tensor<string, []>("op_2302_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184483776))), lut = tensor<fp16, [2]>([-0x1.46cp-8, 0x1.48p-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184688640)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2302_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_2300, groups = var_1186, pad = var_2302_pad_0, pad_type = var_2302_pad_type_0, strides = var_2298, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_173_cast)[name = tensor<string, []>("op_2302_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_59_cast = add(x = var_2302_cast, y = inputs_57_cast)[name = tensor<string, []>("inputs_59_cast")];
            tensor<int32, [1]> var_2306 = const()[name = tensor<string, []>("op_2306"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_59_cast = reduce_mean(axes = var_2306, keep_dims = var_1181, x = inputs_59_cast)[name = tensor<string, []>("channels_mean_59_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_59_cast = sub(x = inputs_59_cast, y = channels_mean_59_cast)[name = tensor<string, []>("zero_mean_59_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_59_cast = mul(x = zero_mean_59_cast, y = zero_mean_59_cast)[name = tensor<string, []>("zero_mean_sq_59_cast")];
            tensor<int32, [1]> var_2310 = const()[name = tensor<string, []>("op_2310"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2311_cast = reduce_mean(axes = var_2310, keep_dims = var_1181, x = zero_mean_sq_59_cast)[name = tensor<string, []>("op_2311_cast")];
            tensor<fp16, []> var_2312_to_fp16 = const()[name = tensor<string, []>("op_2312_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2313_cast = add(x = var_2311_cast, y = var_2312_to_fp16)[name = tensor<string, []>("op_2313_cast")];
            tensor<fp16, []> denom_59_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_59_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_59_cast = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_2313_cast)[name = tensor<string, []>("denom_59_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_59_cast = mul(x = zero_mean_59_cast, y = denom_59_cast)[name = tensor<string, []>("out_59_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2317_to_fp16 = const()[name = tensor<string, []>("op_2317_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184691264)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2318_cast = add(x = out_59_cast, y = var_2317_to_fp16)[name = tensor<string, []>("op_2318_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2320_to_fp16 = const()[name = tensor<string, []>("op_2320_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184693888)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_175_cast = mul(x = var_2318_cast, y = var_2320_to_fp16)[name = tensor<string, []>("input_175_cast")];
            tensor<int32, [2]> var_2328 = const()[name = tensor<string, []>("op_2328"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2330 = const()[name = tensor<string, []>("op_2330"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2332_pad_type_0 = const()[name = tensor<string, []>("op_2332_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2332_pad_0 = const()[name = tensor<string, []>("op_2332_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184696512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194526976))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194527168)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_2332_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_2330, groups = var_1186, pad = var_2332_pad_0, pad_type = var_2332_pad_type_0, strides = var_2328, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_175_cast)[name = tensor<string, []>("op_2332_cast")];
            tensor<int32, [2]> var_2333_split_sizes_0 = const()[name = tensor<string, []>("op_2333_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2333_axis_0 = const()[name = tensor<string, []>("op_2333_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_2333_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_2333_cast_1 = split(axis = var_2333_axis_0, split_sizes = var_2333_split_sizes_0, x = var_2332_cast)[name = tensor<string, []>("op_2333_cast")];
            tensor<string, []> var_2335_mode_0 = const()[name = tensor<string, []>("op_2335_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_2335_cast = gelu(mode = var_2335_mode_0, x = var_2333_cast_1)[name = tensor<string, []>("op_2335_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_177_cast = mul(x = var_2333_cast_0, y = var_2335_cast)[name = tensor<string, []>("input_177_cast")];
            tensor<int32, [2]> var_2339 = const()[name = tensor<string, []>("op_2339"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2341 = const()[name = tensor<string, []>("op_2341"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2343_pad_type_0 = const()[name = tensor<string, []>("op_2343_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2343_pad_0 = const()[name = tensor<string, []>("op_2343_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194547712))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197824576))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197824704)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2343_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_2341, groups = var_1186, pad = var_2343_pad_0, pad_type = var_2343_pad_type_0, strides = var_2339, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_177_cast)[name = tensor<string, []>("op_2343_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_61_cast = add(x = var_2343_cast, y = inputs_59_cast)[name = tensor<string, []>("inputs_61_cast")];
            tensor<int32, [1]> var_2353 = const()[name = tensor<string, []>("op_2353"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_61_cast = reduce_mean(axes = var_2353, keep_dims = var_1181, x = inputs_61_cast)[name = tensor<string, []>("channels_mean_61_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_61_cast = sub(x = inputs_61_cast, y = channels_mean_61_cast)[name = tensor<string, []>("zero_mean_61_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_61_cast = mul(x = zero_mean_61_cast, y = zero_mean_61_cast)[name = tensor<string, []>("zero_mean_sq_61_cast")];
            tensor<int32, [1]> var_2357 = const()[name = tensor<string, []>("op_2357"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2358_cast = reduce_mean(axes = var_2357, keep_dims = var_1181, x = zero_mean_sq_61_cast)[name = tensor<string, []>("op_2358_cast")];
            tensor<fp16, []> var_2359_to_fp16 = const()[name = tensor<string, []>("op_2359_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2360_cast = add(x = var_2358_cast, y = var_2359_to_fp16)[name = tensor<string, []>("op_2360_cast")];
            tensor<fp16, []> denom_61_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_61_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_61_cast = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_2360_cast)[name = tensor<string, []>("denom_61_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_61_cast = mul(x = zero_mean_61_cast, y = denom_61_cast)[name = tensor<string, []>("out_61_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2364_to_fp16 = const()[name = tensor<string, []>("op_2364_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197827328)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2365_cast = add(x = out_61_cast, y = var_2364_to_fp16)[name = tensor<string, []>("op_2365_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2367_to_fp16 = const()[name = tensor<string, []>("op_2367_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197829952)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_101_cast = mul(x = var_2365_cast, y = var_2367_to_fp16)[name = tensor<string, []>("hidden_states_101_cast")];
            tensor<int32, [2]> var_2374 = const()[name = tensor<string, []>("op_2374"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2376 = const()[name = tensor<string, []>("op_2376"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_41_pad_type_0 = const()[name = tensor<string, []>("q_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_41_pad_0 = const()[name = tensor<string, []>("q_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197832576))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198651840))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_41_cast = conv(dilations = var_2376, groups = var_1186, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_2374, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_101_cast)[name = tensor<string, []>("q_41_cast")];
            tensor<int32, [2]> var_2380 = const()[name = tensor<string, []>("op_2380"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2382 = const()[name = tensor<string, []>("op_2382"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_41_pad_type_0 = const()[name = tensor<string, []>("k_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_41_pad_0 = const()[name = tensor<string, []>("k_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198651968))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199471232))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_41_cast = conv(dilations = var_2382, groups = var_1186, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2380, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_101_cast)[name = tensor<string, []>("k_41_cast")];
            tensor<int32, [2]> var_2386 = const()[name = tensor<string, []>("op_2386"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2388 = const()[name = tensor<string, []>("op_2388"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_41_pad_type_0 = const()[name = tensor<string, []>("v_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_41_pad_0 = const()[name = tensor<string, []>("v_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199471360))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(200290624))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_41_cast = conv(dilations = var_2388, groups = var_1186, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_2386, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_101_cast)[name = tensor<string, []>("v_41_cast")];
            tensor<int32, [4]> var_2392 = const()[name = tensor<string, []>("op_2392"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2393_cast = reshape(shape = var_2392, x = q_41_cast)[name = tensor<string, []>("op_2393_cast")];
            tensor<int32, [4]> var_2394 = const()[name = tensor<string, []>("op_2394"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2395_cast = reshape(shape = var_2394, x = k_41_cast)[name = tensor<string, []>("op_2395_cast")];
            tensor<int32, [4]> var_2396 = const()[name = tensor<string, []>("op_2396"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2397_cast = reshape(shape = var_2396, x = v_41_cast)[name = tensor<string, []>("op_2397_cast")];
            tensor<bool, []> attn_weights_81_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_81_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_81_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_81_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_81_cast = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2393_cast, y = var_2395_cast)[name = tensor<string, []>("attn_weights_81_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_83_cast = mul(x = attn_weights_81_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_83_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_2401_cast = softmax(axis = var_1170, x = attn_weights_83_cast)[name = tensor<string, []>("op_2401_cast")];
            tensor<bool, []> attn_41_transpose_x_0 = const()[name = tensor<string, []>("attn_41_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_41_transpose_y_0 = const()[name = tensor<string, []>("attn_41_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_41_cast = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2397_cast, y = var_2401_cast)[name = tensor<string, []>("attn_41_cast")];
            tensor<int32, [4]> var_2405 = const()[name = tensor<string, []>("op_2405"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_179_cast = reshape(shape = var_2405, x = attn_41_cast)[name = tensor<string, []>("input_179_cast")];
            tensor<int32, [2]> var_2410 = const()[name = tensor<string, []>("op_2410"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2412 = const()[name = tensor<string, []>("op_2412"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2414_pad_type_0 = const()[name = tensor<string, []>("op_2414_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2414_pad_0 = const()[name = tensor<string, []>("op_2414_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(200290752))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201110016))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201110144)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2414_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_2412, groups = var_1186, pad = var_2414_pad_0, pad_type = var_2414_pad_type_0, strides = var_2410, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_179_cast)[name = tensor<string, []>("op_2414_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_63_cast = add(x = var_2414_cast, y = inputs_61_cast)[name = tensor<string, []>("inputs_63_cast")];
            tensor<int32, [1]> var_2418 = const()[name = tensor<string, []>("op_2418"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_63_cast = reduce_mean(axes = var_2418, keep_dims = var_1181, x = inputs_63_cast)[name = tensor<string, []>("channels_mean_63_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_63_cast = sub(x = inputs_63_cast, y = channels_mean_63_cast)[name = tensor<string, []>("zero_mean_63_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_63_cast = mul(x = zero_mean_63_cast, y = zero_mean_63_cast)[name = tensor<string, []>("zero_mean_sq_63_cast")];
            tensor<int32, [1]> var_2422 = const()[name = tensor<string, []>("op_2422"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2423_cast = reduce_mean(axes = var_2422, keep_dims = var_1181, x = zero_mean_sq_63_cast)[name = tensor<string, []>("op_2423_cast")];
            tensor<fp16, []> var_2424_to_fp16 = const()[name = tensor<string, []>("op_2424_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2425_cast = add(x = var_2423_cast, y = var_2424_to_fp16)[name = tensor<string, []>("op_2425_cast")];
            tensor<fp16, []> denom_63_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_63_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_63_cast = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_2425_cast)[name = tensor<string, []>("denom_63_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_63_cast = mul(x = zero_mean_63_cast, y = denom_63_cast)[name = tensor<string, []>("out_63_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2429_to_fp16 = const()[name = tensor<string, []>("op_2429_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201112768)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2430_cast = add(x = out_63_cast, y = var_2429_to_fp16)[name = tensor<string, []>("op_2430_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2432_to_fp16 = const()[name = tensor<string, []>("op_2432_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201115392)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_103_cast = mul(x = var_2430_cast, y = var_2432_to_fp16)[name = tensor<string, []>("hidden_states_103_cast")];
            tensor<int32, [2]> var_2439 = const()[name = tensor<string, []>("op_2439"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2441 = const()[name = tensor<string, []>("op_2441"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_43_pad_type_0 = const()[name = tensor<string, []>("q_43_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_43_pad_0 = const()[name = tensor<string, []>("q_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201118016))), lut = tensor<fp16, [2]>([-0x1.61p-7, 0x1.608p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_43_cast = conv(dilations = var_2441, groups = var_1186, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_2439, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("q_43_cast")];
            tensor<int32, [2]> var_2445 = const()[name = tensor<string, []>("op_2445"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2447 = const()[name = tensor<string, []>("op_2447"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_43_pad_type_0 = const()[name = tensor<string, []>("k_43_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_43_pad_0 = const()[name = tensor<string, []>("k_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201322880))), lut = tensor<fp16, [2]>([-0x1.c8p-8, 0x1.c8cp-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_43_cast = conv(dilations = var_2447, groups = var_1186, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_2445, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_43_cast")];
            tensor<int32, [2]> var_2451 = const()[name = tensor<string, []>("op_2451"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2453 = const()[name = tensor<string, []>("op_2453"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_43_pad_type_0 = const()[name = tensor<string, []>("v_43_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_43_pad_0 = const()[name = tensor<string, []>("v_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201650624))), lut = tensor<fp16, [2]>([-0x1.e5cp-8, 0x1.e78p-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_43_cast = conv(dilations = var_2453, groups = var_1186, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_2451, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_43_cast")];
            tensor<int32, [4]> var_2457 = const()[name = tensor<string, []>("op_2457"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2458_cast = reshape(shape = var_2457, x = q_43_cast)[name = tensor<string, []>("op_2458_cast")];
            tensor<int32, [4]> var_2459 = const()[name = tensor<string, []>("op_2459"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2460_cast = reshape(shape = var_2459, x = k_43_cast)[name = tensor<string, []>("op_2460_cast")];
            tensor<int32, [4]> var_2461 = const()[name = tensor<string, []>("op_2461"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2462_cast = reshape(shape = var_2461, x = v_43_cast)[name = tensor<string, []>("op_2462_cast")];
            tensor<bool, []> attn_weights_85_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_85_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_2458_cast, y = var_2460_cast)[name = tensor<string, []>("attn_weights_85_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_87_cast = mul(x = attn_weights_85_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_87_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_2466_cast = softmax(axis = var_1170, x = attn_weights_87_cast)[name = tensor<string, []>("op_2466_cast")];
            tensor<bool, []> attn_43_transpose_x_0 = const()[name = tensor<string, []>("attn_43_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_43_transpose_y_0 = const()[name = tensor<string, []>("attn_43_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_43_cast = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2462_cast, y = var_2466_cast)[name = tensor<string, []>("attn_43_cast")];
            tensor<int32, [4]> var_2470 = const()[name = tensor<string, []>("op_2470"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_181_cast = reshape(shape = var_2470, x = attn_43_cast)[name = tensor<string, []>("input_181_cast")];
            tensor<int32, [2]> var_2475 = const()[name = tensor<string, []>("op_2475"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2477 = const()[name = tensor<string, []>("op_2477"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2479_pad_type_0 = const()[name = tensor<string, []>("op_2479_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2479_pad_0 = const()[name = tensor<string, []>("op_2479_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201978368))), lut = tensor<fp16, [2]>([-0x1.1a8p-8, 0x1.1a8p-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202183232)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2479_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_2477, groups = var_1186, pad = var_2479_pad_0, pad_type = var_2479_pad_type_0, strides = var_2475, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_181_cast)[name = tensor<string, []>("op_2479_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_65_cast = add(x = var_2479_cast, y = inputs_63_cast)[name = tensor<string, []>("inputs_65_cast")];
            tensor<int32, [1]> var_2483 = const()[name = tensor<string, []>("op_2483"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_65_cast = reduce_mean(axes = var_2483, keep_dims = var_1181, x = inputs_65_cast)[name = tensor<string, []>("channels_mean_65_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_65_cast = sub(x = inputs_65_cast, y = channels_mean_65_cast)[name = tensor<string, []>("zero_mean_65_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_65_cast = mul(x = zero_mean_65_cast, y = zero_mean_65_cast)[name = tensor<string, []>("zero_mean_sq_65_cast")];
            tensor<int32, [1]> var_2487 = const()[name = tensor<string, []>("op_2487"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2488_cast = reduce_mean(axes = var_2487, keep_dims = var_1181, x = zero_mean_sq_65_cast)[name = tensor<string, []>("op_2488_cast")];
            tensor<fp16, []> var_2489_to_fp16 = const()[name = tensor<string, []>("op_2489_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2490_cast = add(x = var_2488_cast, y = var_2489_to_fp16)[name = tensor<string, []>("op_2490_cast")];
            tensor<fp16, []> denom_65_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_65_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_65_cast = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_2490_cast)[name = tensor<string, []>("denom_65_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_65_cast = mul(x = zero_mean_65_cast, y = denom_65_cast)[name = tensor<string, []>("out_65_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2494_to_fp16 = const()[name = tensor<string, []>("op_2494_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202185856)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2495_cast = add(x = out_65_cast, y = var_2494_to_fp16)[name = tensor<string, []>("op_2495_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2497_to_fp16 = const()[name = tensor<string, []>("op_2497_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202188480)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_183_cast = mul(x = var_2495_cast, y = var_2497_to_fp16)[name = tensor<string, []>("input_183_cast")];
            tensor<int32, [2]> var_2505 = const()[name = tensor<string, []>("op_2505"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2507 = const()[name = tensor<string, []>("op_2507"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2509_pad_type_0 = const()[name = tensor<string, []>("op_2509_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2509_pad_0 = const()[name = tensor<string, []>("op_2509_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202191104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212021568))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212021760)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_2509_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_2507, groups = var_1186, pad = var_2509_pad_0, pad_type = var_2509_pad_type_0, strides = var_2505, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_183_cast)[name = tensor<string, []>("op_2509_cast")];
            tensor<int32, [2]> var_2510_split_sizes_0 = const()[name = tensor<string, []>("op_2510_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2510_axis_0 = const()[name = tensor<string, []>("op_2510_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_2510_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_2510_cast_1 = split(axis = var_2510_axis_0, split_sizes = var_2510_split_sizes_0, x = var_2509_cast)[name = tensor<string, []>("op_2510_cast")];
            tensor<string, []> var_2512_mode_0 = const()[name = tensor<string, []>("op_2512_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_2512_cast = gelu(mode = var_2512_mode_0, x = var_2510_cast_1)[name = tensor<string, []>("op_2512_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_185_cast = mul(x = var_2510_cast_0, y = var_2512_cast)[name = tensor<string, []>("input_185_cast")];
            tensor<int32, [2]> var_2516 = const()[name = tensor<string, []>("op_2516"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2518 = const()[name = tensor<string, []>("op_2518"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2520_pad_type_0 = const()[name = tensor<string, []>("op_2520_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2520_pad_0 = const()[name = tensor<string, []>("op_2520_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212042304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216957568))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216957760)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2520_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_2518, groups = var_1186, pad = var_2520_pad_0, pad_type = var_2520_pad_type_0, strides = var_2516, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_185_cast)[name = tensor<string, []>("op_2520_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_67_cast = add(x = var_2520_cast, y = inputs_65_cast)[name = tensor<string, []>("inputs_67_cast")];
            tensor<int32, [1]> var_2530 = const()[name = tensor<string, []>("op_2530"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_67_cast = reduce_mean(axes = var_2530, keep_dims = var_1181, x = inputs_67_cast)[name = tensor<string, []>("channels_mean_67_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_67_cast = sub(x = inputs_67_cast, y = channels_mean_67_cast)[name = tensor<string, []>("zero_mean_67_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_67_cast = mul(x = zero_mean_67_cast, y = zero_mean_67_cast)[name = tensor<string, []>("zero_mean_sq_67_cast")];
            tensor<int32, [1]> var_2534 = const()[name = tensor<string, []>("op_2534"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2535_cast = reduce_mean(axes = var_2534, keep_dims = var_1181, x = zero_mean_sq_67_cast)[name = tensor<string, []>("op_2535_cast")];
            tensor<fp16, []> var_2536_to_fp16 = const()[name = tensor<string, []>("op_2536_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2537_cast = add(x = var_2535_cast, y = var_2536_to_fp16)[name = tensor<string, []>("op_2537_cast")];
            tensor<fp16, []> denom_67_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_67_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_67_cast = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_2537_cast)[name = tensor<string, []>("denom_67_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_67_cast = mul(x = zero_mean_67_cast, y = denom_67_cast)[name = tensor<string, []>("out_67_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2541_to_fp16 = const()[name = tensor<string, []>("op_2541_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216960384)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2542_cast = add(x = out_67_cast, y = var_2541_to_fp16)[name = tensor<string, []>("op_2542_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2544_to_fp16 = const()[name = tensor<string, []>("op_2544_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216963008)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_107_cast = mul(x = var_2542_cast, y = var_2544_to_fp16)[name = tensor<string, []>("hidden_states_107_cast")];
            tensor<int32, [2]> var_2551 = const()[name = tensor<string, []>("op_2551"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2553 = const()[name = tensor<string, []>("op_2553"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_45_pad_type_0 = const()[name = tensor<string, []>("q_45_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_45_pad_0 = const()[name = tensor<string, []>("q_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216965632))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217784896))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_45_cast = conv(dilations = var_2553, groups = var_1186, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_2551, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_107_cast)[name = tensor<string, []>("q_45_cast")];
            tensor<int32, [2]> var_2557 = const()[name = tensor<string, []>("op_2557"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2559 = const()[name = tensor<string, []>("op_2559"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_45_pad_type_0 = const()[name = tensor<string, []>("k_45_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_45_pad_0 = const()[name = tensor<string, []>("k_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217785024))), lut = tensor<fp16, [4]>([-0x1.09p-5, -0x1.404p-7, 0x1.404p-7, 0x1.08cp-5]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_45_cast = conv(dilations = var_2559, groups = var_1186, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_2557, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_107_cast)[name = tensor<string, []>("k_45_cast")];
            tensor<int32, [2]> var_2563 = const()[name = tensor<string, []>("op_2563"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2565 = const()[name = tensor<string, []>("op_2565"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_45_pad_type_0 = const()[name = tensor<string, []>("v_45_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_45_pad_0 = const()[name = tensor<string, []>("v_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218194688))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219013952))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_45_cast = conv(dilations = var_2565, groups = var_1186, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_2563, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_107_cast)[name = tensor<string, []>("v_45_cast")];
            tensor<int32, [4]> var_2569 = const()[name = tensor<string, []>("op_2569"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2570_cast = reshape(shape = var_2569, x = q_45_cast)[name = tensor<string, []>("op_2570_cast")];
            tensor<int32, [4]> var_2571 = const()[name = tensor<string, []>("op_2571"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2572_cast = reshape(shape = var_2571, x = k_45_cast)[name = tensor<string, []>("op_2572_cast")];
            tensor<int32, [4]> var_2573 = const()[name = tensor<string, []>("op_2573"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2574_cast = reshape(shape = var_2573, x = v_45_cast)[name = tensor<string, []>("op_2574_cast")];
            tensor<bool, []> attn_weights_89_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_89_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_89_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_89_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_89_cast = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2570_cast, y = var_2572_cast)[name = tensor<string, []>("attn_weights_89_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_91_cast = mul(x = attn_weights_89_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_91_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_2578_cast = softmax(axis = var_1170, x = attn_weights_91_cast)[name = tensor<string, []>("op_2578_cast")];
            tensor<bool, []> attn_45_transpose_x_0 = const()[name = tensor<string, []>("attn_45_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_45_transpose_y_0 = const()[name = tensor<string, []>("attn_45_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_45_cast = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2574_cast, y = var_2578_cast)[name = tensor<string, []>("attn_45_cast")];
            tensor<int32, [4]> var_2582 = const()[name = tensor<string, []>("op_2582"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_187_cast = reshape(shape = var_2582, x = attn_45_cast)[name = tensor<string, []>("input_187_cast")];
            tensor<int32, [2]> var_2587 = const()[name = tensor<string, []>("op_2587"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2589 = const()[name = tensor<string, []>("op_2589"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2591_pad_type_0 = const()[name = tensor<string, []>("op_2591_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2591_pad_0 = const()[name = tensor<string, []>("op_2591_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219014080))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219833344))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219833472)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2591_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_2589, groups = var_1186, pad = var_2591_pad_0, pad_type = var_2591_pad_type_0, strides = var_2587, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_187_cast)[name = tensor<string, []>("op_2591_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_69_cast = add(x = var_2591_cast, y = inputs_67_cast)[name = tensor<string, []>("inputs_69_cast")];
            tensor<int32, [1]> var_2595 = const()[name = tensor<string, []>("op_2595"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_69_cast = reduce_mean(axes = var_2595, keep_dims = var_1181, x = inputs_69_cast)[name = tensor<string, []>("channels_mean_69_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_69_cast = sub(x = inputs_69_cast, y = channels_mean_69_cast)[name = tensor<string, []>("zero_mean_69_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_69_cast = mul(x = zero_mean_69_cast, y = zero_mean_69_cast)[name = tensor<string, []>("zero_mean_sq_69_cast")];
            tensor<int32, [1]> var_2599 = const()[name = tensor<string, []>("op_2599"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2600_cast = reduce_mean(axes = var_2599, keep_dims = var_1181, x = zero_mean_sq_69_cast)[name = tensor<string, []>("op_2600_cast")];
            tensor<fp16, []> var_2601_to_fp16 = const()[name = tensor<string, []>("op_2601_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2602_cast = add(x = var_2600_cast, y = var_2601_to_fp16)[name = tensor<string, []>("op_2602_cast")];
            tensor<fp16, []> denom_69_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_69_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_69_cast = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_2602_cast)[name = tensor<string, []>("denom_69_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_69_cast = mul(x = zero_mean_69_cast, y = denom_69_cast)[name = tensor<string, []>("out_69_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2606_to_fp16 = const()[name = tensor<string, []>("op_2606_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219836096)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2607_cast = add(x = out_69_cast, y = var_2606_to_fp16)[name = tensor<string, []>("op_2607_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2609_to_fp16 = const()[name = tensor<string, []>("op_2609_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219838720)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_109_cast = mul(x = var_2607_cast, y = var_2609_to_fp16)[name = tensor<string, []>("hidden_states_109_cast")];
            tensor<int32, [2]> var_2616 = const()[name = tensor<string, []>("op_2616"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2618 = const()[name = tensor<string, []>("op_2618"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_47_pad_type_0 = const()[name = tensor<string, []>("q_47_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_47_pad_0 = const()[name = tensor<string, []>("q_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219841344))), lut = tensor<fp16, [2]>([-0x1.61p-7, 0x1.614p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_47_cast = conv(dilations = var_2618, groups = var_1186, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_2616, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("q_47_cast")];
            tensor<int32, [2]> var_2622 = const()[name = tensor<string, []>("op_2622"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2624 = const()[name = tensor<string, []>("op_2624"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_47_pad_type_0 = const()[name = tensor<string, []>("k_47_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_47_pad_0 = const()[name = tensor<string, []>("k_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220046208))), lut = tensor<fp16, [2]>([-0x1.c84p-8, 0x1.c98p-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_47_cast = conv(dilations = var_2624, groups = var_1186, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_2622, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_47_cast")];
            tensor<int32, [2]> var_2628 = const()[name = tensor<string, []>("op_2628"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2630 = const()[name = tensor<string, []>("op_2630"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_47_pad_type_0 = const()[name = tensor<string, []>("v_47_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_47_pad_0 = const()[name = tensor<string, []>("v_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220373952))), lut = tensor<fp16, [2]>([-0x1.e6cp-8, 0x1.e64p-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_47_cast = conv(dilations = var_2630, groups = var_1186, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_2628, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_47_cast")];
            tensor<int32, [4]> var_2634 = const()[name = tensor<string, []>("op_2634"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2635_cast = reshape(shape = var_2634, x = q_47_cast)[name = tensor<string, []>("op_2635_cast")];
            tensor<int32, [4]> var_2636 = const()[name = tensor<string, []>("op_2636"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2637_cast = reshape(shape = var_2636, x = k_47_cast)[name = tensor<string, []>("op_2637_cast")];
            tensor<int32, [4]> var_2638 = const()[name = tensor<string, []>("op_2638"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2639_cast = reshape(shape = var_2638, x = v_47_cast)[name = tensor<string, []>("op_2639_cast")];
            tensor<bool, []> attn_weights_93_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_93_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_93_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_93_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_93_cast = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_2635_cast, y = var_2637_cast)[name = tensor<string, []>("attn_weights_93_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_95_cast = mul(x = attn_weights_93_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_95_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_2643_cast = softmax(axis = var_1170, x = attn_weights_95_cast)[name = tensor<string, []>("op_2643_cast")];
            tensor<bool, []> attn_47_transpose_x_0 = const()[name = tensor<string, []>("attn_47_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_47_transpose_y_0 = const()[name = tensor<string, []>("attn_47_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_47_cast = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2639_cast, y = var_2643_cast)[name = tensor<string, []>("attn_47_cast")];
            tensor<int32, [4]> var_2647 = const()[name = tensor<string, []>("op_2647"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_189_cast = reshape(shape = var_2647, x = attn_47_cast)[name = tensor<string, []>("input_189_cast")];
            tensor<int32, [2]> var_2652 = const()[name = tensor<string, []>("op_2652"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2654 = const()[name = tensor<string, []>("op_2654"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2656_pad_type_0 = const()[name = tensor<string, []>("op_2656_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2656_pad_0 = const()[name = tensor<string, []>("op_2656_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220701696))), lut = tensor<fp16, [2]>([-0x1.214p-8, 0x1.21p-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220906560)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2656_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_2654, groups = var_1186, pad = var_2656_pad_0, pad_type = var_2656_pad_type_0, strides = var_2652, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_189_cast)[name = tensor<string, []>("op_2656_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_71_cast = add(x = var_2656_cast, y = inputs_69_cast)[name = tensor<string, []>("inputs_71_cast")];
            tensor<int32, [1]> var_2660 = const()[name = tensor<string, []>("op_2660"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_71_cast = reduce_mean(axes = var_2660, keep_dims = var_1181, x = inputs_71_cast)[name = tensor<string, []>("channels_mean_71_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_71_cast = sub(x = inputs_71_cast, y = channels_mean_71_cast)[name = tensor<string, []>("zero_mean_71_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_71_cast = mul(x = zero_mean_71_cast, y = zero_mean_71_cast)[name = tensor<string, []>("zero_mean_sq_71_cast")];
            tensor<int32, [1]> var_2664 = const()[name = tensor<string, []>("op_2664"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2665_cast = reduce_mean(axes = var_2664, keep_dims = var_1181, x = zero_mean_sq_71_cast)[name = tensor<string, []>("op_2665_cast")];
            tensor<fp16, []> var_2666_to_fp16 = const()[name = tensor<string, []>("op_2666_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2667_cast = add(x = var_2665_cast, y = var_2666_to_fp16)[name = tensor<string, []>("op_2667_cast")];
            tensor<fp16, []> denom_71_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_71_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_71_cast = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_2667_cast)[name = tensor<string, []>("denom_71_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_71_cast = mul(x = zero_mean_71_cast, y = denom_71_cast)[name = tensor<string, []>("out_71_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2671_to_fp16 = const()[name = tensor<string, []>("op_2671_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220909184)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2672_cast = add(x = out_71_cast, y = var_2671_to_fp16)[name = tensor<string, []>("op_2672_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2674_to_fp16 = const()[name = tensor<string, []>("op_2674_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220911808)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_191_cast = mul(x = var_2672_cast, y = var_2674_to_fp16)[name = tensor<string, []>("input_191_cast")];
            tensor<int32, [2]> var_2682 = const()[name = tensor<string, []>("op_2682"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2684 = const()[name = tensor<string, []>("op_2684"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2686_pad_type_0 = const()[name = tensor<string, []>("op_2686_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2686_pad_0 = const()[name = tensor<string, []>("op_2686_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220914432))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227468096))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227468224)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_2686_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_2684, groups = var_1186, pad = var_2686_pad_0, pad_type = var_2686_pad_type_0, strides = var_2682, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_191_cast)[name = tensor<string, []>("op_2686_cast")];
            tensor<int32, [2]> var_2687_split_sizes_0 = const()[name = tensor<string, []>("op_2687_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2687_axis_0 = const()[name = tensor<string, []>("op_2687_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_2687_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_2687_cast_1 = split(axis = var_2687_axis_0, split_sizes = var_2687_split_sizes_0, x = var_2686_cast)[name = tensor<string, []>("op_2687_cast")];
            tensor<string, []> var_2689_mode_0 = const()[name = tensor<string, []>("op_2689_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_2689_cast = gelu(mode = var_2689_mode_0, x = var_2687_cast_1)[name = tensor<string, []>("op_2689_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_193_cast = mul(x = var_2687_cast_0, y = var_2689_cast)[name = tensor<string, []>("input_193_cast")];
            tensor<int32, [2]> var_2693 = const()[name = tensor<string, []>("op_2693"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2695 = const()[name = tensor<string, []>("op_2695"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2697_pad_type_0 = const()[name = tensor<string, []>("op_2697_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2697_pad_0 = const()[name = tensor<string, []>("op_2697_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227488768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232404032))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232404224)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2697_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_2695, groups = var_1186, pad = var_2697_pad_0, pad_type = var_2697_pad_type_0, strides = var_2693, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_193_cast)[name = tensor<string, []>("op_2697_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_73_cast = add(x = var_2697_cast, y = inputs_71_cast)[name = tensor<string, []>("inputs_73_cast")];
            tensor<int32, [1]> var_2707 = const()[name = tensor<string, []>("op_2707"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_73_cast = reduce_mean(axes = var_2707, keep_dims = var_1181, x = inputs_73_cast)[name = tensor<string, []>("channels_mean_73_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_73_cast = sub(x = inputs_73_cast, y = channels_mean_73_cast)[name = tensor<string, []>("zero_mean_73_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_73_cast = mul(x = zero_mean_73_cast, y = zero_mean_73_cast)[name = tensor<string, []>("zero_mean_sq_73_cast")];
            tensor<int32, [1]> var_2711 = const()[name = tensor<string, []>("op_2711"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2712_cast = reduce_mean(axes = var_2711, keep_dims = var_1181, x = zero_mean_sq_73_cast)[name = tensor<string, []>("op_2712_cast")];
            tensor<fp16, []> var_2713_to_fp16 = const()[name = tensor<string, []>("op_2713_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2714_cast = add(x = var_2712_cast, y = var_2713_to_fp16)[name = tensor<string, []>("op_2714_cast")];
            tensor<fp16, []> denom_73_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_73_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_73_cast = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_2714_cast)[name = tensor<string, []>("denom_73_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_73_cast = mul(x = zero_mean_73_cast, y = denom_73_cast)[name = tensor<string, []>("out_73_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2718_to_fp16 = const()[name = tensor<string, []>("op_2718_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232406848)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2719_cast = add(x = out_73_cast, y = var_2718_to_fp16)[name = tensor<string, []>("op_2719_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2721_to_fp16 = const()[name = tensor<string, []>("op_2721_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232409472)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_113_cast = mul(x = var_2719_cast, y = var_2721_to_fp16)[name = tensor<string, []>("hidden_states_113_cast")];
            tensor<int32, [2]> var_2728 = const()[name = tensor<string, []>("op_2728"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2730 = const()[name = tensor<string, []>("op_2730"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_49_pad_type_0 = const()[name = tensor<string, []>("q_49_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_49_pad_0 = const()[name = tensor<string, []>("q_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232412096))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233231360))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_49_cast = conv(dilations = var_2730, groups = var_1186, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_2728, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_113_cast)[name = tensor<string, []>("q_49_cast")];
            tensor<int32, [2]> var_2734 = const()[name = tensor<string, []>("op_2734"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2736 = const()[name = tensor<string, []>("op_2736"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_49_pad_type_0 = const()[name = tensor<string, []>("k_49_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_49_pad_0 = const()[name = tensor<string, []>("k_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233231488))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234050752))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_49_cast = conv(dilations = var_2736, groups = var_1186, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_2734, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_113_cast)[name = tensor<string, []>("k_49_cast")];
            tensor<int32, [2]> var_2740 = const()[name = tensor<string, []>("op_2740"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2742 = const()[name = tensor<string, []>("op_2742"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_49_pad_type_0 = const()[name = tensor<string, []>("v_49_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_49_pad_0 = const()[name = tensor<string, []>("v_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234050880))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234870144))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_49_cast = conv(dilations = var_2742, groups = var_1186, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_2740, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_113_cast)[name = tensor<string, []>("v_49_cast")];
            tensor<int32, [4]> var_2746 = const()[name = tensor<string, []>("op_2746"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2747_cast = reshape(shape = var_2746, x = q_49_cast)[name = tensor<string, []>("op_2747_cast")];
            tensor<int32, [4]> var_2748 = const()[name = tensor<string, []>("op_2748"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2749_cast = reshape(shape = var_2748, x = k_49_cast)[name = tensor<string, []>("op_2749_cast")];
            tensor<int32, [4]> var_2750 = const()[name = tensor<string, []>("op_2750"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2751_cast = reshape(shape = var_2750, x = v_49_cast)[name = tensor<string, []>("op_2751_cast")];
            tensor<bool, []> attn_weights_97_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_97_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_2747_cast, y = var_2749_cast)[name = tensor<string, []>("attn_weights_97_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_99_cast = mul(x = attn_weights_97_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_99_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_2755_cast = softmax(axis = var_1170, x = attn_weights_99_cast)[name = tensor<string, []>("op_2755_cast")];
            tensor<bool, []> attn_49_transpose_x_0 = const()[name = tensor<string, []>("attn_49_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_49_transpose_y_0 = const()[name = tensor<string, []>("attn_49_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_49_cast = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2751_cast, y = var_2755_cast)[name = tensor<string, []>("attn_49_cast")];
            tensor<int32, [4]> var_2759 = const()[name = tensor<string, []>("op_2759"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_195_cast = reshape(shape = var_2759, x = attn_49_cast)[name = tensor<string, []>("input_195_cast")];
            tensor<int32, [2]> var_2764 = const()[name = tensor<string, []>("op_2764"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2766 = const()[name = tensor<string, []>("op_2766"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2768_pad_type_0 = const()[name = tensor<string, []>("op_2768_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2768_pad_0 = const()[name = tensor<string, []>("op_2768_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234870272))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235689536))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235689664)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2768_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_2766, groups = var_1186, pad = var_2768_pad_0, pad_type = var_2768_pad_type_0, strides = var_2764, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_195_cast)[name = tensor<string, []>("op_2768_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_75_cast = add(x = var_2768_cast, y = inputs_73_cast)[name = tensor<string, []>("inputs_75_cast")];
            tensor<int32, [1]> var_2772 = const()[name = tensor<string, []>("op_2772"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_75_cast = reduce_mean(axes = var_2772, keep_dims = var_1181, x = inputs_75_cast)[name = tensor<string, []>("channels_mean_75_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_75_cast = sub(x = inputs_75_cast, y = channels_mean_75_cast)[name = tensor<string, []>("zero_mean_75_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_75_cast = mul(x = zero_mean_75_cast, y = zero_mean_75_cast)[name = tensor<string, []>("zero_mean_sq_75_cast")];
            tensor<int32, [1]> var_2776 = const()[name = tensor<string, []>("op_2776"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2777_cast = reduce_mean(axes = var_2776, keep_dims = var_1181, x = zero_mean_sq_75_cast)[name = tensor<string, []>("op_2777_cast")];
            tensor<fp16, []> var_2778_to_fp16 = const()[name = tensor<string, []>("op_2778_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2779_cast = add(x = var_2777_cast, y = var_2778_to_fp16)[name = tensor<string, []>("op_2779_cast")];
            tensor<fp16, []> denom_75_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_75_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_75_cast = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_2779_cast)[name = tensor<string, []>("denom_75_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_75_cast = mul(x = zero_mean_75_cast, y = denom_75_cast)[name = tensor<string, []>("out_75_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2783_to_fp16 = const()[name = tensor<string, []>("op_2783_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235692288)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2784_cast = add(x = out_75_cast, y = var_2783_to_fp16)[name = tensor<string, []>("op_2784_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2786_to_fp16 = const()[name = tensor<string, []>("op_2786_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235694912)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_115_cast = mul(x = var_2784_cast, y = var_2786_to_fp16)[name = tensor<string, []>("hidden_states_115_cast")];
            tensor<int32, [2]> var_2793 = const()[name = tensor<string, []>("op_2793"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2795 = const()[name = tensor<string, []>("op_2795"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_51_pad_type_0 = const()[name = tensor<string, []>("q_51_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_51_pad_0 = const()[name = tensor<string, []>("q_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235697536))), lut = tensor<fp16, [2]>([-0x1.60cp-7, 0x1.608p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_51_cast = conv(dilations = var_2795, groups = var_1186, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_2793, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("q_51_cast")];
            tensor<int32, [2]> var_2799 = const()[name = tensor<string, []>("op_2799"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2801 = const()[name = tensor<string, []>("op_2801"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_51_pad_type_0 = const()[name = tensor<string, []>("k_51_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_51_pad_0 = const()[name = tensor<string, []>("k_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235902400))), lut = tensor<fp16, [4]>([-0x1.838p-7, -0x1.f7p-9, 0x1.f84p-9, 0x1.83cp-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_51_cast = conv(dilations = var_2801, groups = var_1186, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_2799, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_51_cast")];
            tensor<int32, [2]> var_2805 = const()[name = tensor<string, []>("op_2805"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2807 = const()[name = tensor<string, []>("op_2807"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_51_pad_type_0 = const()[name = tensor<string, []>("v_51_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_51_pad_0 = const()[name = tensor<string, []>("v_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236557824))), lut = tensor<fp16, [2]>([-0x1.eap-8, 0x1.ecp-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_51_cast = conv(dilations = var_2807, groups = var_1186, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_2805, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_51_cast")];
            tensor<int32, [4]> var_2811 = const()[name = tensor<string, []>("op_2811"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2812_cast = reshape(shape = var_2811, x = q_51_cast)[name = tensor<string, []>("op_2812_cast")];
            tensor<int32, [4]> var_2813 = const()[name = tensor<string, []>("op_2813"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2814_cast = reshape(shape = var_2813, x = k_51_cast)[name = tensor<string, []>("op_2814_cast")];
            tensor<int32, [4]> var_2815 = const()[name = tensor<string, []>("op_2815"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2816_cast = reshape(shape = var_2815, x = v_51_cast)[name = tensor<string, []>("op_2816_cast")];
            tensor<bool, []> attn_weights_101_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_101_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_101_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_101_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_101_cast = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_2812_cast, y = var_2814_cast)[name = tensor<string, []>("attn_weights_101_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_103_cast = mul(x = attn_weights_101_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_103_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_2820_cast = softmax(axis = var_1170, x = attn_weights_103_cast)[name = tensor<string, []>("op_2820_cast")];
            tensor<bool, []> attn_51_transpose_x_0 = const()[name = tensor<string, []>("attn_51_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_51_transpose_y_0 = const()[name = tensor<string, []>("attn_51_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_51_cast = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2816_cast, y = var_2820_cast)[name = tensor<string, []>("attn_51_cast")];
            tensor<int32, [4]> var_2824 = const()[name = tensor<string, []>("op_2824"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_197_cast = reshape(shape = var_2824, x = attn_51_cast)[name = tensor<string, []>("input_197_cast")];
            tensor<int32, [2]> var_2829 = const()[name = tensor<string, []>("op_2829"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2831 = const()[name = tensor<string, []>("op_2831"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2833_pad_type_0 = const()[name = tensor<string, []>("op_2833_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2833_pad_0 = const()[name = tensor<string, []>("op_2833_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236885568))), lut = tensor<fp16, [2]>([-0x1.2b8p-8, 0x1.2cp-8]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237090432)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2833_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_2831, groups = var_1186, pad = var_2833_pad_0, pad_type = var_2833_pad_type_0, strides = var_2829, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_197_cast)[name = tensor<string, []>("op_2833_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_77_cast = add(x = var_2833_cast, y = inputs_75_cast)[name = tensor<string, []>("inputs_77_cast")];
            tensor<int32, [1]> var_2837 = const()[name = tensor<string, []>("op_2837"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_77_cast = reduce_mean(axes = var_2837, keep_dims = var_1181, x = inputs_77_cast)[name = tensor<string, []>("channels_mean_77_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_77_cast = sub(x = inputs_77_cast, y = channels_mean_77_cast)[name = tensor<string, []>("zero_mean_77_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_77_cast = mul(x = zero_mean_77_cast, y = zero_mean_77_cast)[name = tensor<string, []>("zero_mean_sq_77_cast")];
            tensor<int32, [1]> var_2841 = const()[name = tensor<string, []>("op_2841"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2842_cast = reduce_mean(axes = var_2841, keep_dims = var_1181, x = zero_mean_sq_77_cast)[name = tensor<string, []>("op_2842_cast")];
            tensor<fp16, []> var_2843_to_fp16 = const()[name = tensor<string, []>("op_2843_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2844_cast = add(x = var_2842_cast, y = var_2843_to_fp16)[name = tensor<string, []>("op_2844_cast")];
            tensor<fp16, []> denom_77_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_77_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_77_cast = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_2844_cast)[name = tensor<string, []>("denom_77_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_77_cast = mul(x = zero_mean_77_cast, y = denom_77_cast)[name = tensor<string, []>("out_77_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2848_to_fp16 = const()[name = tensor<string, []>("op_2848_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237093056)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2849_cast = add(x = out_77_cast, y = var_2848_to_fp16)[name = tensor<string, []>("op_2849_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2851_to_fp16 = const()[name = tensor<string, []>("op_2851_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237095680)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_199_cast = mul(x = var_2849_cast, y = var_2851_to_fp16)[name = tensor<string, []>("input_199_cast")];
            tensor<int32, [2]> var_2859 = const()[name = tensor<string, []>("op_2859"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2861 = const()[name = tensor<string, []>("op_2861"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2863_pad_type_0 = const()[name = tensor<string, []>("op_2863_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2863_pad_0 = const()[name = tensor<string, []>("op_2863_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237098304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246928768))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246928960)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_2863_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_2861, groups = var_1186, pad = var_2863_pad_0, pad_type = var_2863_pad_type_0, strides = var_2859, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_199_cast)[name = tensor<string, []>("op_2863_cast")];
            tensor<int32, [2]> var_2864_split_sizes_0 = const()[name = tensor<string, []>("op_2864_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2864_axis_0 = const()[name = tensor<string, []>("op_2864_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_2864_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_2864_cast_1 = split(axis = var_2864_axis_0, split_sizes = var_2864_split_sizes_0, x = var_2863_cast)[name = tensor<string, []>("op_2864_cast")];
            tensor<string, []> var_2866_mode_0 = const()[name = tensor<string, []>("op_2866_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_2866_cast = gelu(mode = var_2866_mode_0, x = var_2864_cast_1)[name = tensor<string, []>("op_2866_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_201_cast = mul(x = var_2864_cast_0, y = var_2866_cast)[name = tensor<string, []>("input_201_cast")];
            tensor<int32, [2]> var_2870 = const()[name = tensor<string, []>("op_2870"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2872 = const()[name = tensor<string, []>("op_2872"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2874_pad_type_0 = const()[name = tensor<string, []>("op_2874_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2874_pad_0 = const()[name = tensor<string, []>("op_2874_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(246949504))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250226368))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250226496)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2874_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_2872, groups = var_1186, pad = var_2874_pad_0, pad_type = var_2874_pad_type_0, strides = var_2870, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_201_cast)[name = tensor<string, []>("op_2874_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_79_cast = add(x = var_2874_cast, y = inputs_77_cast)[name = tensor<string, []>("inputs_79_cast")];
            tensor<int32, [1]> var_2884 = const()[name = tensor<string, []>("op_2884"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_79_cast = reduce_mean(axes = var_2884, keep_dims = var_1181, x = inputs_79_cast)[name = tensor<string, []>("channels_mean_79_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_79_cast = sub(x = inputs_79_cast, y = channels_mean_79_cast)[name = tensor<string, []>("zero_mean_79_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_79_cast = mul(x = zero_mean_79_cast, y = zero_mean_79_cast)[name = tensor<string, []>("zero_mean_sq_79_cast")];
            tensor<int32, [1]> var_2888 = const()[name = tensor<string, []>("op_2888"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2889_cast = reduce_mean(axes = var_2888, keep_dims = var_1181, x = zero_mean_sq_79_cast)[name = tensor<string, []>("op_2889_cast")];
            tensor<fp16, []> var_2890_to_fp16 = const()[name = tensor<string, []>("op_2890_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2891_cast = add(x = var_2889_cast, y = var_2890_to_fp16)[name = tensor<string, []>("op_2891_cast")];
            tensor<fp16, []> denom_79_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_79_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_79_cast = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_2891_cast)[name = tensor<string, []>("denom_79_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_79_cast = mul(x = zero_mean_79_cast, y = denom_79_cast)[name = tensor<string, []>("out_79_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2895_to_fp16 = const()[name = tensor<string, []>("op_2895_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250229120)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2896_cast = add(x = out_79_cast, y = var_2895_to_fp16)[name = tensor<string, []>("op_2896_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2898_to_fp16 = const()[name = tensor<string, []>("op_2898_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250231744)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_119_cast = mul(x = var_2896_cast, y = var_2898_to_fp16)[name = tensor<string, []>("hidden_states_119_cast")];
            tensor<int32, [2]> var_2905 = const()[name = tensor<string, []>("op_2905"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2907 = const()[name = tensor<string, []>("op_2907"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_53_pad_type_0 = const()[name = tensor<string, []>("q_53_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_53_pad_0 = const()[name = tensor<string, []>("q_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250234368))), lut = tensor<fp16, [2]>([-0x1.1d4p-6, 0x1.1dcp-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_53_cast = conv(dilations = var_2907, groups = var_1186, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_2905, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_119_cast)[name = tensor<string, []>("q_53_cast")];
            tensor<int32, [2]> var_2911 = const()[name = tensor<string, []>("op_2911"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2913 = const()[name = tensor<string, []>("op_2913"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_53_pad_type_0 = const()[name = tensor<string, []>("k_53_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_53_pad_0 = const()[name = tensor<string, []>("k_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250439232))), lut = tensor<fp16, [2]>([-0x1.194p-6, 0x1.1ap-6]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_53_cast = conv(dilations = var_2913, groups = var_1186, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_2911, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_119_cast)[name = tensor<string, []>("k_53_cast")];
            tensor<int32, [2]> var_2917 = const()[name = tensor<string, []>("op_2917"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2919 = const()[name = tensor<string, []>("op_2919"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_53_pad_type_0 = const()[name = tensor<string, []>("v_53_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_53_pad_0 = const()[name = tensor<string, []>("v_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250644096))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251463360))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_53_cast = conv(dilations = var_2919, groups = var_1186, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_2917, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_119_cast)[name = tensor<string, []>("v_53_cast")];
            tensor<int32, [4]> var_2923 = const()[name = tensor<string, []>("op_2923"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2924_cast = reshape(shape = var_2923, x = q_53_cast)[name = tensor<string, []>("op_2924_cast")];
            tensor<int32, [4]> var_2925 = const()[name = tensor<string, []>("op_2925"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2926_cast = reshape(shape = var_2925, x = k_53_cast)[name = tensor<string, []>("op_2926_cast")];
            tensor<int32, [4]> var_2927 = const()[name = tensor<string, []>("op_2927"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2928_cast = reshape(shape = var_2927, x = v_53_cast)[name = tensor<string, []>("op_2928_cast")];
            tensor<bool, []> attn_weights_105_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_105_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_105_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_105_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_105_cast = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_2924_cast, y = var_2926_cast)[name = tensor<string, []>("attn_weights_105_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_107_cast = mul(x = attn_weights_105_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_107_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_2932_cast = softmax(axis = var_1170, x = attn_weights_107_cast)[name = tensor<string, []>("op_2932_cast")];
            tensor<bool, []> attn_53_transpose_x_0 = const()[name = tensor<string, []>("attn_53_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_53_transpose_y_0 = const()[name = tensor<string, []>("attn_53_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_53_cast = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_2928_cast, y = var_2932_cast)[name = tensor<string, []>("attn_53_cast")];
            tensor<int32, [4]> var_2936 = const()[name = tensor<string, []>("op_2936"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_203_cast = reshape(shape = var_2936, x = attn_53_cast)[name = tensor<string, []>("input_203_cast")];
            tensor<int32, [2]> var_2941 = const()[name = tensor<string, []>("op_2941"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2943 = const()[name = tensor<string, []>("op_2943"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2945_pad_type_0 = const()[name = tensor<string, []>("op_2945_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2945_pad_0 = const()[name = tensor<string, []>("op_2945_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251463488))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252282752))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252282880)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2945_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_2943, groups = var_1186, pad = var_2945_pad_0, pad_type = var_2945_pad_type_0, strides = var_2941, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_203_cast)[name = tensor<string, []>("op_2945_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_81_cast = add(x = var_2945_cast, y = inputs_79_cast)[name = tensor<string, []>("inputs_81_cast")];
            tensor<int32, [1]> var_2949 = const()[name = tensor<string, []>("op_2949"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_81_cast = reduce_mean(axes = var_2949, keep_dims = var_1181, x = inputs_81_cast)[name = tensor<string, []>("channels_mean_81_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_81_cast = sub(x = inputs_81_cast, y = channels_mean_81_cast)[name = tensor<string, []>("zero_mean_81_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_81_cast = mul(x = zero_mean_81_cast, y = zero_mean_81_cast)[name = tensor<string, []>("zero_mean_sq_81_cast")];
            tensor<int32, [1]> var_2953 = const()[name = tensor<string, []>("op_2953"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_2954_cast = reduce_mean(axes = var_2953, keep_dims = var_1181, x = zero_mean_sq_81_cast)[name = tensor<string, []>("op_2954_cast")];
            tensor<fp16, []> var_2955_to_fp16 = const()[name = tensor<string, []>("op_2955_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_2956_cast = add(x = var_2954_cast, y = var_2955_to_fp16)[name = tensor<string, []>("op_2956_cast")];
            tensor<fp16, []> denom_81_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_81_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_81_cast = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_2956_cast)[name = tensor<string, []>("denom_81_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_81_cast = mul(x = zero_mean_81_cast, y = denom_81_cast)[name = tensor<string, []>("out_81_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2960_to_fp16 = const()[name = tensor<string, []>("op_2960_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252285504)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_2961_cast = add(x = out_81_cast, y = var_2960_to_fp16)[name = tensor<string, []>("op_2961_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2963_to_fp16 = const()[name = tensor<string, []>("op_2963_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252288128)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_121_cast = mul(x = var_2961_cast, y = var_2963_to_fp16)[name = tensor<string, []>("hidden_states_121_cast")];
            tensor<int32, [2]> var_2970 = const()[name = tensor<string, []>("op_2970"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2972 = const()[name = tensor<string, []>("op_2972"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_55_pad_type_0 = const()[name = tensor<string, []>("q_55_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_55_pad_0 = const()[name = tensor<string, []>("q_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252290752))), lut = tensor<fp16, [2]>([-0x1.688p-7, 0x1.688p-7]), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_55_cast = conv(dilations = var_2972, groups = var_1186, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_2970, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("q_55_cast")];
            tensor<int32, [2]> var_2976 = const()[name = tensor<string, []>("op_2976"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2978 = const()[name = tensor<string, []>("op_2978"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_55_pad_type_0 = const()[name = tensor<string, []>("k_55_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_55_pad_0 = const()[name = tensor<string, []>("k_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252495616))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253806400))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_55_cast = conv(dilations = var_2978, groups = var_1186, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_2976, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_55_cast")];
            tensor<int32, [2]> var_2982 = const()[name = tensor<string, []>("op_2982"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2984 = const()[name = tensor<string, []>("op_2984"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_55_pad_type_0 = const()[name = tensor<string, []>("v_55_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_55_pad_0 = const()[name = tensor<string, []>("v_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253806528))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255117312))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_55_cast = conv(dilations = var_2984, groups = var_1186, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_2982, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_55_cast")];
            tensor<int32, [4]> var_2988 = const()[name = tensor<string, []>("op_2988"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_2989_cast = reshape(shape = var_2988, x = q_55_cast)[name = tensor<string, []>("op_2989_cast")];
            tensor<int32, [4]> var_2990 = const()[name = tensor<string, []>("op_2990"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2991_cast = reshape(shape = var_2990, x = k_55_cast)[name = tensor<string, []>("op_2991_cast")];
            tensor<int32, [4]> var_2992 = const()[name = tensor<string, []>("op_2992"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2993_cast = reshape(shape = var_2992, x = v_55_cast)[name = tensor<string, []>("op_2993_cast")];
            tensor<bool, []> attn_weights_109_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_109_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_2989_cast, y = var_2991_cast)[name = tensor<string, []>("attn_weights_109_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_111_cast = mul(x = attn_weights_109_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_111_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_2997_cast = softmax(axis = var_1170, x = attn_weights_111_cast)[name = tensor<string, []>("op_2997_cast")];
            tensor<bool, []> attn_55_transpose_x_0 = const()[name = tensor<string, []>("attn_55_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_55_transpose_y_0 = const()[name = tensor<string, []>("attn_55_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_55_cast = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_2993_cast, y = var_2997_cast)[name = tensor<string, []>("attn_55_cast")];
            tensor<int32, [4]> var_3001 = const()[name = tensor<string, []>("op_3001"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_205_cast = reshape(shape = var_3001, x = attn_55_cast)[name = tensor<string, []>("input_205_cast")];
            tensor<int32, [2]> var_3006 = const()[name = tensor<string, []>("op_3006"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3008 = const()[name = tensor<string, []>("op_3008"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3010_pad_type_0 = const()[name = tensor<string, []>("op_3010_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3010_pad_0 = const()[name = tensor<string, []>("op_3010_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255117440))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255936704))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255936832)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3010_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_3008, groups = var_1186, pad = var_3010_pad_0, pad_type = var_3010_pad_type_0, strides = var_3006, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_205_cast)[name = tensor<string, []>("op_3010_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_83_cast = add(x = var_3010_cast, y = inputs_81_cast)[name = tensor<string, []>("inputs_83_cast")];
            tensor<int32, [1]> var_3014 = const()[name = tensor<string, []>("op_3014"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_83_cast = reduce_mean(axes = var_3014, keep_dims = var_1181, x = inputs_83_cast)[name = tensor<string, []>("channels_mean_83_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_83_cast = sub(x = inputs_83_cast, y = channels_mean_83_cast)[name = tensor<string, []>("zero_mean_83_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_83_cast = mul(x = zero_mean_83_cast, y = zero_mean_83_cast)[name = tensor<string, []>("zero_mean_sq_83_cast")];
            tensor<int32, [1]> var_3018 = const()[name = tensor<string, []>("op_3018"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3019_cast = reduce_mean(axes = var_3018, keep_dims = var_1181, x = zero_mean_sq_83_cast)[name = tensor<string, []>("op_3019_cast")];
            tensor<fp16, []> var_3020_to_fp16 = const()[name = tensor<string, []>("op_3020_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3021_cast = add(x = var_3019_cast, y = var_3020_to_fp16)[name = tensor<string, []>("op_3021_cast")];
            tensor<fp16, []> denom_83_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_83_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_83_cast = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_3021_cast)[name = tensor<string, []>("denom_83_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_83_cast = mul(x = zero_mean_83_cast, y = denom_83_cast)[name = tensor<string, []>("out_83_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3025_to_fp16 = const()[name = tensor<string, []>("op_3025_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255939456)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3026_cast = add(x = out_83_cast, y = var_3025_to_fp16)[name = tensor<string, []>("op_3026_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3028_to_fp16 = const()[name = tensor<string, []>("op_3028_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255942080)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_207_cast = mul(x = var_3026_cast, y = var_3028_to_fp16)[name = tensor<string, []>("input_207_cast")];
            tensor<int32, [2]> var_3036 = const()[name = tensor<string, []>("op_3036"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3038 = const()[name = tensor<string, []>("op_3038"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3040_pad_type_0 = const()[name = tensor<string, []>("op_3040_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3040_pad_0 = const()[name = tensor<string, []>("op_3040_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255944704))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265775168))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265775360)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_3040_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_3038, groups = var_1186, pad = var_3040_pad_0, pad_type = var_3040_pad_type_0, strides = var_3036, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_207_cast)[name = tensor<string, []>("op_3040_cast")];
            tensor<int32, [2]> var_3041_split_sizes_0 = const()[name = tensor<string, []>("op_3041_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_3041_axis_0 = const()[name = tensor<string, []>("op_3041_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_3041_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_3041_cast_1 = split(axis = var_3041_axis_0, split_sizes = var_3041_split_sizes_0, x = var_3040_cast)[name = tensor<string, []>("op_3041_cast")];
            tensor<string, []> var_3043_mode_0 = const()[name = tensor<string, []>("op_3043_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_3043_cast = gelu(mode = var_3043_mode_0, x = var_3041_cast_1)[name = tensor<string, []>("op_3043_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_209_cast = mul(x = var_3041_cast_0, y = var_3043_cast)[name = tensor<string, []>("input_209_cast")];
            tensor<int32, [2]> var_3047 = const()[name = tensor<string, []>("op_3047"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3049 = const()[name = tensor<string, []>("op_3049"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3051_pad_type_0 = const()[name = tensor<string, []>("op_3051_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3051_pad_0 = const()[name = tensor<string, []>("op_3051_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265795904))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269072768))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269072896)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3051_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_3049, groups = var_1186, pad = var_3051_pad_0, pad_type = var_3051_pad_type_0, strides = var_3047, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_209_cast)[name = tensor<string, []>("op_3051_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_125_cast = add(x = var_3051_cast, y = inputs_83_cast)[name = tensor<string, []>("hidden_states_125_cast")];
            tensor<int32, [4]> var_3053 = const()[name = tensor<string, []>("op_3053"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> input_211_cast = reshape(shape = var_3053, x = hidden_states_125_cast)[name = tensor<string, []>("input_211_cast")];
            tensor<int32, [2]> var_3057 = const()[name = tensor<string, []>("op_3057"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3059 = const()[name = tensor<string, []>("op_3059"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_127_pad_type_0 = const()[name = tensor<string, []>("hidden_states_127_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = tensor<string, []>("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269075520))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(270304384))), name = tensor<string, []>("down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(270304576)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_127_cast = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3059, groups = var_1186, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = var_3057, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_211_cast)[name = tensor<string, []>("hidden_states_127_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_213_cast = add(x = hidden_states_127_cast, y = hidden_states_61_cast)[name = tensor<string, []>("input_213_cast")];
            tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_213_cast)[name = tensor<string, []>("reshape_52_cast")];
            tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = tensor<string, []>("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_39_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_39_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_39_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor<string, []>("reduce_mean_39_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor<string, []>("sub_26_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_13_cast = square(x = sub_26_cast)[name = tensor<string, []>("square_13_cast")];
            tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = tensor<string, []>("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_41_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_41_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_41_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor<string, []>("reduce_mean_41_cast")];
            tensor<fp16, []> add_26_y_0_to_fp16 = const()[name = tensor<string, []>("add_26_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor<string, []>("add_26_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor<string, []>("sqrt_13_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor<string, []>("real_div_13_cast")];
            tensor<int32, [4]> reshape_53_shape_0 = const()[name = tensor<string, []>("reshape_53_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor<string, []>("reshape_53_cast")];
            tensor<fp16, [1280]> add_27_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_27_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(270307200)))];
            tensor<fp16, [1280]> add_27_beta_0_to_fp16 = const()[name = tensor<string, []>("add_27_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(270309824)))];
            tensor<fp16, []> add_27_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_27_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_27_cast = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_53_cast)[name = tensor<string, []>("add_27_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_217_cast = silu(x = add_27_cast)[name = tensor<string, []>("input_217_cast")];
            tensor<int32, [2]> var_3074 = const()[name = tensor<string, []>("op_3074"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3076 = const()[name = tensor<string, []>("op_3076"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_129_pad_type_0 = const()[name = tensor<string, []>("hidden_states_129_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_129_pad_0 = const()[name = tensor<string, []>("hidden_states_129_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(270312448))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281371712))), name = tensor<string, []>("down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281371904)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_129_cast = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3076, groups = var_1186, pad = hidden_states_129_pad_0, pad_type = hidden_states_129_pad_type_0, strides = var_3074, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_217_cast)[name = tensor<string, []>("hidden_states_129_cast")];
            tensor<int32, [2]> var_3082 = const()[name = tensor<string, []>("op_3082"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3084 = const()[name = tensor<string, []>("op_3084"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_11_pad_type_0 = const()[name = tensor<string, []>("temb_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_11_pad_0 = const()[name = tensor<string, []>("temb_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281374528))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282193792))), name = tensor<string, []>("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282193920)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_11_cast = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3084, groups = var_1186, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_3082, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_11_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_221_cast = add(x = hidden_states_129_cast, y = temb_11_cast)[name = tensor<string, []>("input_221_cast")];
            tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_56_cast = reshape(shape = reshape_56_shape_0, x = input_221_cast)[name = tensor<string, []>("reshape_56_cast")];
            tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = tensor<string, []>("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_42_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_42_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_42_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor<string, []>("reduce_mean_42_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor<string, []>("sub_28_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_14_cast = square(x = sub_28_cast)[name = tensor<string, []>("square_14_cast")];
            tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = tensor<string, []>("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_44_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_44_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_44_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor<string, []>("reduce_mean_44_cast")];
            tensor<fp16, []> add_28_y_0_to_fp16 = const()[name = tensor<string, []>("add_28_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor<string, []>("add_28_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor<string, []>("sqrt_14_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor<string, []>("real_div_14_cast")];
            tensor<int32, [4]> reshape_57_shape_0 = const()[name = tensor<string, []>("reshape_57_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor<string, []>("reshape_57_cast")];
            tensor<fp16, [1280]> add_29_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_29_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282196544)))];
            tensor<fp16, [1280]> add_29_beta_0_to_fp16 = const()[name = tensor<string, []>("add_29_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282199168)))];
            tensor<fp16, []> add_29_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_29_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_29_cast = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_57_cast)[name = tensor<string, []>("add_29_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_225_cast = silu(x = add_29_cast)[name = tensor<string, []>("input_225_cast")];
            tensor<int32, [2]> var_3094 = const()[name = tensor<string, []>("op_3094"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3096 = const()[name = tensor<string, []>("op_3096"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_131_pad_type_0 = const()[name = tensor<string, []>("hidden_states_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_131_pad_0 = const()[name = tensor<string, []>("hidden_states_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7372800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282201792))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289574656))), name = tensor<string, []>("down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289574784)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_131_cast = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3096, groups = var_1186, pad = hidden_states_131_pad_0, pad_type = hidden_states_131_pad_type_0, strides = var_3094, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_225_cast)[name = tensor<string, []>("hidden_states_131_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_133_cast = add(x = input_213_cast, y = hidden_states_131_cast)[name = tensor<string, []>("hidden_states_133_cast")];
            tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_60_cast = reshape(shape = reshape_60_shape_0, x = hidden_states_133_cast)[name = tensor<string, []>("reshape_60_cast")];
            tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = tensor<string, []>("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_45_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_45_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_45_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor<string, []>("reduce_mean_45_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor<string, []>("sub_30_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_15_cast = square(x = sub_30_cast)[name = tensor<string, []>("square_15_cast")];
            tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = tensor<string, []>("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_47_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_47_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_47_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor<string, []>("reduce_mean_47_cast")];
            tensor<fp16, []> add_30_y_0_to_fp16 = const()[name = tensor<string, []>("add_30_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor<string, []>("add_30_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor<string, []>("sqrt_15_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor<string, []>("real_div_15_cast")];
            tensor<int32, [4]> reshape_61_shape_0 = const()[name = tensor<string, []>("reshape_61_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor<string, []>("reshape_61_cast")];
            tensor<fp16, [1280]> add_31_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_31_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289577408)))];
            tensor<fp16, [1280]> add_31_beta_0_to_fp16 = const()[name = tensor<string, []>("add_31_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289580032)))];
            tensor<fp16, []> add_31_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_31_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_31_cast = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_61_cast)[name = tensor<string, []>("add_31_cast")];
            tensor<int32, [2]> var_3134 = const()[name = tensor<string, []>("op_3134"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3136 = const()[name = tensor<string, []>("op_3136"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_135_pad_type_0 = const()[name = tensor<string, []>("hidden_states_135_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_135_pad_0 = const()[name = tensor<string, []>("hidden_states_135_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289582656))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290401920))), name = tensor<string, []>("down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290402048)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_135_cast = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3136, groups = var_1186, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_3134, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_31_cast)[name = tensor<string, []>("hidden_states_135_cast")];
            tensor<int32, [4]> var_3141 = const()[name = tensor<string, []>("op_3141"), val = tensor<int32, [4]>([2, 1280, 1, 1024])];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_85_cast = reshape(shape = var_3141, x = hidden_states_135_cast)[name = tensor<string, []>("inputs_85_cast")];
            tensor<int32, [1]> var_3151 = const()[name = tensor<string, []>("op_3151"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_85_cast = reduce_mean(axes = var_3151, keep_dims = var_1181, x = inputs_85_cast)[name = tensor<string, []>("channels_mean_85_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_85_cast = sub(x = inputs_85_cast, y = channels_mean_85_cast)[name = tensor<string, []>("zero_mean_85_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_85_cast = mul(x = zero_mean_85_cast, y = zero_mean_85_cast)[name = tensor<string, []>("zero_mean_sq_85_cast")];
            tensor<int32, [1]> var_3155 = const()[name = tensor<string, []>("op_3155"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3156_cast = reduce_mean(axes = var_3155, keep_dims = var_1181, x = zero_mean_sq_85_cast)[name = tensor<string, []>("op_3156_cast")];
            tensor<fp16, []> var_3157_to_fp16 = const()[name = tensor<string, []>("op_3157_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3158_cast = add(x = var_3156_cast, y = var_3157_to_fp16)[name = tensor<string, []>("op_3158_cast")];
            tensor<fp16, []> denom_85_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_85_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_85_cast = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_3158_cast)[name = tensor<string, []>("denom_85_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_85_cast = mul(x = zero_mean_85_cast, y = denom_85_cast)[name = tensor<string, []>("out_85_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3162_to_fp16 = const()[name = tensor<string, []>("op_3162_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290404672)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3163_cast = add(x = out_85_cast, y = var_3162_to_fp16)[name = tensor<string, []>("op_3163_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3165_to_fp16 = const()[name = tensor<string, []>("op_3165_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290407296)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_137_cast = mul(x = var_3163_cast, y = var_3165_to_fp16)[name = tensor<string, []>("hidden_states_137_cast")];
            tensor<int32, [2]> var_3172 = const()[name = tensor<string, []>("op_3172"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3174 = const()[name = tensor<string, []>("op_3174"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_57_pad_type_0 = const()[name = tensor<string, []>("q_57_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_57_pad_0 = const()[name = tensor<string, []>("q_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290409920))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291229184))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_57_cast = conv(dilations = var_3174, groups = var_1186, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_3172, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_137_cast)[name = tensor<string, []>("q_57_cast")];
            tensor<int32, [2]> var_3178 = const()[name = tensor<string, []>("op_3178"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3180 = const()[name = tensor<string, []>("op_3180"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_57_pad_type_0 = const()[name = tensor<string, []>("k_57_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_57_pad_0 = const()[name = tensor<string, []>("k_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291229312))), lut = tensor<fp16, [4]>([-0x1.394p-5, -0x1.784p-7, 0x1.7c4p-7, 0x1.3a4p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_57_cast = conv(dilations = var_3180, groups = var_1186, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_3178, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_137_cast)[name = tensor<string, []>("k_57_cast")];
            tensor<int32, [2]> var_3184 = const()[name = tensor<string, []>("op_3184"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3186 = const()[name = tensor<string, []>("op_3186"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_57_pad_type_0 = const()[name = tensor<string, []>("v_57_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_57_pad_0 = const()[name = tensor<string, []>("v_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291638976))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292458240))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_57_cast = conv(dilations = var_3186, groups = var_1186, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_3184, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_137_cast)[name = tensor<string, []>("v_57_cast")];
            tensor<int32, [4]> var_3190 = const()[name = tensor<string, []>("op_3190"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3191_cast = reshape(shape = var_3190, x = q_57_cast)[name = tensor<string, []>("op_3191_cast")];
            tensor<int32, [4]> var_3192 = const()[name = tensor<string, []>("op_3192"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3193_cast = reshape(shape = var_3192, x = k_57_cast)[name = tensor<string, []>("op_3193_cast")];
            tensor<int32, [4]> var_3194 = const()[name = tensor<string, []>("op_3194"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3195_cast = reshape(shape = var_3194, x = v_57_cast)[name = tensor<string, []>("op_3195_cast")];
            tensor<bool, []> attn_weights_113_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_113_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_113_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_113_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_113_cast = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3191_cast, y = var_3193_cast)[name = tensor<string, []>("attn_weights_113_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_115_cast = mul(x = attn_weights_113_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_115_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_3199_cast = softmax(axis = var_1170, x = attn_weights_115_cast)[name = tensor<string, []>("op_3199_cast")];
            tensor<bool, []> attn_57_transpose_x_0 = const()[name = tensor<string, []>("attn_57_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_57_transpose_y_0 = const()[name = tensor<string, []>("attn_57_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_57_cast = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3195_cast, y = var_3199_cast)[name = tensor<string, []>("attn_57_cast")];
            tensor<int32, [4]> var_3203 = const()[name = tensor<string, []>("op_3203"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_229_cast = reshape(shape = var_3203, x = attn_57_cast)[name = tensor<string, []>("input_229_cast")];
            tensor<int32, [2]> var_3208 = const()[name = tensor<string, []>("op_3208"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3210 = const()[name = tensor<string, []>("op_3210"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3212_pad_type_0 = const()[name = tensor<string, []>("op_3212_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3212_pad_0 = const()[name = tensor<string, []>("op_3212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292458368))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293277632))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293277760)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3212_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3210, groups = var_1186, pad = var_3212_pad_0, pad_type = var_3212_pad_type_0, strides = var_3208, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_229_cast)[name = tensor<string, []>("op_3212_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_87_cast = add(x = var_3212_cast, y = inputs_85_cast)[name = tensor<string, []>("inputs_87_cast")];
            tensor<int32, [1]> var_3216 = const()[name = tensor<string, []>("op_3216"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_87_cast = reduce_mean(axes = var_3216, keep_dims = var_1181, x = inputs_87_cast)[name = tensor<string, []>("channels_mean_87_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_87_cast = sub(x = inputs_87_cast, y = channels_mean_87_cast)[name = tensor<string, []>("zero_mean_87_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_87_cast = mul(x = zero_mean_87_cast, y = zero_mean_87_cast)[name = tensor<string, []>("zero_mean_sq_87_cast")];
            tensor<int32, [1]> var_3220 = const()[name = tensor<string, []>("op_3220"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3221_cast = reduce_mean(axes = var_3220, keep_dims = var_1181, x = zero_mean_sq_87_cast)[name = tensor<string, []>("op_3221_cast")];
            tensor<fp16, []> var_3222_to_fp16 = const()[name = tensor<string, []>("op_3222_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3223_cast = add(x = var_3221_cast, y = var_3222_to_fp16)[name = tensor<string, []>("op_3223_cast")];
            tensor<fp16, []> denom_87_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_87_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_87_cast = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_3223_cast)[name = tensor<string, []>("denom_87_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_87_cast = mul(x = zero_mean_87_cast, y = denom_87_cast)[name = tensor<string, []>("out_87_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3227_to_fp16 = const()[name = tensor<string, []>("op_3227_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293280384)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3228_cast = add(x = out_87_cast, y = var_3227_to_fp16)[name = tensor<string, []>("op_3228_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3230_to_fp16 = const()[name = tensor<string, []>("op_3230_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293283008)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_139_cast = mul(x = var_3228_cast, y = var_3230_to_fp16)[name = tensor<string, []>("hidden_states_139_cast")];
            tensor<int32, [2]> var_3237 = const()[name = tensor<string, []>("op_3237"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3239 = const()[name = tensor<string, []>("op_3239"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_59_pad_type_0 = const()[name = tensor<string, []>("q_59_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_59_pad_0 = const()[name = tensor<string, []>("q_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293285632))), lut = tensor<fp16, [2]>([-0x1.f38p-7, 0x1.f48p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_59_cast = conv(dilations = var_3239, groups = var_1186, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_3237, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_139_cast)[name = tensor<string, []>("q_59_cast")];
            tensor<int32, [2]> var_3243 = const()[name = tensor<string, []>("op_3243"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3245 = const()[name = tensor<string, []>("op_3245"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_59_pad_type_0 = const()[name = tensor<string, []>("k_59_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_59_pad_0 = const()[name = tensor<string, []>("k_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293490496))), lut = tensor<fp16, [4]>([-0x1.dc8p-6, -0x1.1b8p-7, 0x1.198p-7, 0x1.dbp-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_59_cast = conv(dilations = var_3245, groups = var_1186, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_3243, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_59_cast")];
            tensor<int32, [2]> var_3249 = const()[name = tensor<string, []>("op_3249"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3251 = const()[name = tensor<string, []>("op_3251"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_59_pad_type_0 = const()[name = tensor<string, []>("v_59_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_59_pad_0 = const()[name = tensor<string, []>("v_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(294145920))), lut = tensor<fp16, [4]>([-0x1.0d4p-5, -0x1.34cp-7, 0x1.358p-7, 0x1.0d4p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_59_cast = conv(dilations = var_3251, groups = var_1186, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_3249, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_59_cast")];
            tensor<int32, [4]> var_3255 = const()[name = tensor<string, []>("op_3255"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3256_cast = reshape(shape = var_3255, x = q_59_cast)[name = tensor<string, []>("op_3256_cast")];
            tensor<int32, [4]> var_3257 = const()[name = tensor<string, []>("op_3257"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3258_cast = reshape(shape = var_3257, x = k_59_cast)[name = tensor<string, []>("op_3258_cast")];
            tensor<int32, [4]> var_3259 = const()[name = tensor<string, []>("op_3259"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3260_cast = reshape(shape = var_3259, x = v_59_cast)[name = tensor<string, []>("op_3260_cast")];
            tensor<bool, []> attn_weights_117_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_117_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_117_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_117_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_117_cast = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_3256_cast, y = var_3258_cast)[name = tensor<string, []>("attn_weights_117_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_119_cast = mul(x = attn_weights_117_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_119_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_3264_cast = softmax(axis = var_1170, x = attn_weights_119_cast)[name = tensor<string, []>("op_3264_cast")];
            tensor<bool, []> attn_59_transpose_x_0 = const()[name = tensor<string, []>("attn_59_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_59_transpose_y_0 = const()[name = tensor<string, []>("attn_59_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_59_cast = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3260_cast, y = var_3264_cast)[name = tensor<string, []>("attn_59_cast")];
            tensor<int32, [4]> var_3268 = const()[name = tensor<string, []>("op_3268"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_231_cast = reshape(shape = var_3268, x = attn_59_cast)[name = tensor<string, []>("input_231_cast")];
            tensor<int32, [2]> var_3273 = const()[name = tensor<string, []>("op_3273"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3275 = const()[name = tensor<string, []>("op_3275"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3277_pad_type_0 = const()[name = tensor<string, []>("op_3277_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3277_pad_0 = const()[name = tensor<string, []>("op_3277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(294801344))), lut = tensor<fp16, [4]>([-0x1.2d8p-6, -0x1.698p-8, 0x1.68cp-8, 0x1.2dp-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295211008)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3277_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3275, groups = var_1186, pad = var_3277_pad_0, pad_type = var_3277_pad_type_0, strides = var_3273, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_231_cast)[name = tensor<string, []>("op_3277_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_89_cast = add(x = var_3277_cast, y = inputs_87_cast)[name = tensor<string, []>("inputs_89_cast")];
            tensor<int32, [1]> var_3281 = const()[name = tensor<string, []>("op_3281"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_89_cast = reduce_mean(axes = var_3281, keep_dims = var_1181, x = inputs_89_cast)[name = tensor<string, []>("channels_mean_89_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_89_cast = sub(x = inputs_89_cast, y = channels_mean_89_cast)[name = tensor<string, []>("zero_mean_89_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_89_cast = mul(x = zero_mean_89_cast, y = zero_mean_89_cast)[name = tensor<string, []>("zero_mean_sq_89_cast")];
            tensor<int32, [1]> var_3285 = const()[name = tensor<string, []>("op_3285"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3286_cast = reduce_mean(axes = var_3285, keep_dims = var_1181, x = zero_mean_sq_89_cast)[name = tensor<string, []>("op_3286_cast")];
            tensor<fp16, []> var_3287_to_fp16 = const()[name = tensor<string, []>("op_3287_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3288_cast = add(x = var_3286_cast, y = var_3287_to_fp16)[name = tensor<string, []>("op_3288_cast")];
            tensor<fp16, []> denom_89_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_89_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_89_cast = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_3288_cast)[name = tensor<string, []>("denom_89_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_89_cast = mul(x = zero_mean_89_cast, y = denom_89_cast)[name = tensor<string, []>("out_89_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3292_to_fp16 = const()[name = tensor<string, []>("op_3292_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295213632)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3293_cast = add(x = out_89_cast, y = var_3292_to_fp16)[name = tensor<string, []>("op_3293_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3295_to_fp16 = const()[name = tensor<string, []>("op_3295_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295216256)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_233_cast = mul(x = var_3293_cast, y = var_3295_to_fp16)[name = tensor<string, []>("input_233_cast")];
            tensor<int32, [2]> var_3303 = const()[name = tensor<string, []>("op_3303"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3305 = const()[name = tensor<string, []>("op_3305"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3307_pad_type_0 = const()[name = tensor<string, []>("op_3307_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3307_pad_0 = const()[name = tensor<string, []>("op_3307_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295218880))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305049344))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305049536)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_3307_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3305, groups = var_1186, pad = var_3307_pad_0, pad_type = var_3307_pad_type_0, strides = var_3303, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_233_cast)[name = tensor<string, []>("op_3307_cast")];
            tensor<int32, [2]> var_3308_split_sizes_0 = const()[name = tensor<string, []>("op_3308_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_3308_axis_0 = const()[name = tensor<string, []>("op_3308_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_3308_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_3308_cast_1 = split(axis = var_3308_axis_0, split_sizes = var_3308_split_sizes_0, x = var_3307_cast)[name = tensor<string, []>("op_3308_cast")];
            tensor<string, []> var_3310_mode_0 = const()[name = tensor<string, []>("op_3310_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_3310_cast = gelu(mode = var_3310_mode_0, x = var_3308_cast_1)[name = tensor<string, []>("op_3310_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_235_cast = mul(x = var_3308_cast_0, y = var_3310_cast)[name = tensor<string, []>("input_235_cast")];
            tensor<int32, [2]> var_3314 = const()[name = tensor<string, []>("op_3314"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3316 = const()[name = tensor<string, []>("op_3316"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3318_pad_type_0 = const()[name = tensor<string, []>("op_3318_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3318_pad_0 = const()[name = tensor<string, []>("op_3318_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305070080))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308346944))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308347072)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3318_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3316, groups = var_1186, pad = var_3318_pad_0, pad_type = var_3318_pad_type_0, strides = var_3314, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_235_cast)[name = tensor<string, []>("op_3318_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_91_cast = add(x = var_3318_cast, y = inputs_89_cast)[name = tensor<string, []>("inputs_91_cast")];
            tensor<int32, [1]> var_3328 = const()[name = tensor<string, []>("op_3328"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_91_cast = reduce_mean(axes = var_3328, keep_dims = var_1181, x = inputs_91_cast)[name = tensor<string, []>("channels_mean_91_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_91_cast = sub(x = inputs_91_cast, y = channels_mean_91_cast)[name = tensor<string, []>("zero_mean_91_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_91_cast = mul(x = zero_mean_91_cast, y = zero_mean_91_cast)[name = tensor<string, []>("zero_mean_sq_91_cast")];
            tensor<int32, [1]> var_3332 = const()[name = tensor<string, []>("op_3332"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3333_cast = reduce_mean(axes = var_3332, keep_dims = var_1181, x = zero_mean_sq_91_cast)[name = tensor<string, []>("op_3333_cast")];
            tensor<fp16, []> var_3334_to_fp16 = const()[name = tensor<string, []>("op_3334_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3335_cast = add(x = var_3333_cast, y = var_3334_to_fp16)[name = tensor<string, []>("op_3335_cast")];
            tensor<fp16, []> denom_91_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_91_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_91_cast = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_3335_cast)[name = tensor<string, []>("denom_91_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_91_cast = mul(x = zero_mean_91_cast, y = denom_91_cast)[name = tensor<string, []>("out_91_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3339_to_fp16 = const()[name = tensor<string, []>("op_3339_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308349696)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3340_cast = add(x = out_91_cast, y = var_3339_to_fp16)[name = tensor<string, []>("op_3340_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3342_to_fp16 = const()[name = tensor<string, []>("op_3342_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308352320)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_143_cast = mul(x = var_3340_cast, y = var_3342_to_fp16)[name = tensor<string, []>("hidden_states_143_cast")];
            tensor<int32, [2]> var_3349 = const()[name = tensor<string, []>("op_3349"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3351 = const()[name = tensor<string, []>("op_3351"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_61_pad_type_0 = const()[name = tensor<string, []>("q_61_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_61_pad_0 = const()[name = tensor<string, []>("q_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308354944))), lut = tensor<fp16, [4]>([-0x1.414p-5, -0x1.83cp-7, 0x1.814p-7, 0x1.414p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_61_cast = conv(dilations = var_3351, groups = var_1186, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_3349, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_143_cast)[name = tensor<string, []>("q_61_cast")];
            tensor<int32, [2]> var_3355 = const()[name = tensor<string, []>("op_3355"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3357 = const()[name = tensor<string, []>("op_3357"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_61_pad_type_0 = const()[name = tensor<string, []>("k_61_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_61_pad_0 = const()[name = tensor<string, []>("k_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308764608))), lut = tensor<fp16, [4]>([-0x1.444p-5, -0x1.84cp-7, 0x1.884p-7, 0x1.444p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_61_cast = conv(dilations = var_3357, groups = var_1186, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_3355, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_143_cast)[name = tensor<string, []>("k_61_cast")];
            tensor<int32, [2]> var_3361 = const()[name = tensor<string, []>("op_3361"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3363 = const()[name = tensor<string, []>("op_3363"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_61_pad_type_0 = const()[name = tensor<string, []>("v_61_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_61_pad_0 = const()[name = tensor<string, []>("v_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309174272))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309993536))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_61_cast = conv(dilations = var_3363, groups = var_1186, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_3361, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_143_cast)[name = tensor<string, []>("v_61_cast")];
            tensor<int32, [4]> var_3367 = const()[name = tensor<string, []>("op_3367"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3368_cast = reshape(shape = var_3367, x = q_61_cast)[name = tensor<string, []>("op_3368_cast")];
            tensor<int32, [4]> var_3369 = const()[name = tensor<string, []>("op_3369"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3370_cast = reshape(shape = var_3369, x = k_61_cast)[name = tensor<string, []>("op_3370_cast")];
            tensor<int32, [4]> var_3371 = const()[name = tensor<string, []>("op_3371"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3372_cast = reshape(shape = var_3371, x = v_61_cast)[name = tensor<string, []>("op_3372_cast")];
            tensor<bool, []> attn_weights_121_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_121_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3368_cast, y = var_3370_cast)[name = tensor<string, []>("attn_weights_121_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_123_cast = mul(x = attn_weights_121_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_123_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_3376_cast = softmax(axis = var_1170, x = attn_weights_123_cast)[name = tensor<string, []>("op_3376_cast")];
            tensor<bool, []> attn_61_transpose_x_0 = const()[name = tensor<string, []>("attn_61_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_61_transpose_y_0 = const()[name = tensor<string, []>("attn_61_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_61_cast = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3372_cast, y = var_3376_cast)[name = tensor<string, []>("attn_61_cast")];
            tensor<int32, [4]> var_3380 = const()[name = tensor<string, []>("op_3380"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_237_cast = reshape(shape = var_3380, x = attn_61_cast)[name = tensor<string, []>("input_237_cast")];
            tensor<int32, [2]> var_3385 = const()[name = tensor<string, []>("op_3385"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3387 = const()[name = tensor<string, []>("op_3387"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3389_pad_type_0 = const()[name = tensor<string, []>("op_3389_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3389_pad_0 = const()[name = tensor<string, []>("op_3389_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309993664))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310812928))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310813056)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3389_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_3387, groups = var_1186, pad = var_3389_pad_0, pad_type = var_3389_pad_type_0, strides = var_3385, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_237_cast)[name = tensor<string, []>("op_3389_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_93_cast = add(x = var_3389_cast, y = inputs_91_cast)[name = tensor<string, []>("inputs_93_cast")];
            tensor<int32, [1]> var_3393 = const()[name = tensor<string, []>("op_3393"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_93_cast = reduce_mean(axes = var_3393, keep_dims = var_1181, x = inputs_93_cast)[name = tensor<string, []>("channels_mean_93_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_93_cast = sub(x = inputs_93_cast, y = channels_mean_93_cast)[name = tensor<string, []>("zero_mean_93_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_93_cast = mul(x = zero_mean_93_cast, y = zero_mean_93_cast)[name = tensor<string, []>("zero_mean_sq_93_cast")];
            tensor<int32, [1]> var_3397 = const()[name = tensor<string, []>("op_3397"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3398_cast = reduce_mean(axes = var_3397, keep_dims = var_1181, x = zero_mean_sq_93_cast)[name = tensor<string, []>("op_3398_cast")];
            tensor<fp16, []> var_3399_to_fp16 = const()[name = tensor<string, []>("op_3399_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3400_cast = add(x = var_3398_cast, y = var_3399_to_fp16)[name = tensor<string, []>("op_3400_cast")];
            tensor<fp16, []> denom_93_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_93_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_93_cast = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_3400_cast)[name = tensor<string, []>("denom_93_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_93_cast = mul(x = zero_mean_93_cast, y = denom_93_cast)[name = tensor<string, []>("out_93_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3404_to_fp16 = const()[name = tensor<string, []>("op_3404_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310815680)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3405_cast = add(x = out_93_cast, y = var_3404_to_fp16)[name = tensor<string, []>("op_3405_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3407_to_fp16 = const()[name = tensor<string, []>("op_3407_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310818304)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_145_cast = mul(x = var_3405_cast, y = var_3407_to_fp16)[name = tensor<string, []>("hidden_states_145_cast")];
            tensor<int32, [2]> var_3414 = const()[name = tensor<string, []>("op_3414"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3416 = const()[name = tensor<string, []>("op_3416"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_63_pad_type_0 = const()[name = tensor<string, []>("q_63_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_63_pad_0 = const()[name = tensor<string, []>("q_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310820928))), lut = tensor<fp16, [2]>([-0x1.284p-6, 0x1.274p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_63_cast = conv(dilations = var_3416, groups = var_1186, pad = q_63_pad_0, pad_type = q_63_pad_type_0, strides = var_3414, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_145_cast)[name = tensor<string, []>("q_63_cast")];
            tensor<int32, [2]> var_3420 = const()[name = tensor<string, []>("op_3420"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3422 = const()[name = tensor<string, []>("op_3422"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_63_pad_type_0 = const()[name = tensor<string, []>("k_63_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_63_pad_0 = const()[name = tensor<string, []>("k_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311025792))), lut = tensor<fp16, [4]>([-0x1.f2p-6, -0x1.278p-7, 0x1.258p-7, 0x1.f1p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_63_cast = conv(dilations = var_3422, groups = var_1186, pad = k_63_pad_0, pad_type = k_63_pad_type_0, strides = var_3420, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_63_cast")];
            tensor<int32, [2]> var_3426 = const()[name = tensor<string, []>("op_3426"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3428 = const()[name = tensor<string, []>("op_3428"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_63_pad_type_0 = const()[name = tensor<string, []>("v_63_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_63_pad_0 = const()[name = tensor<string, []>("v_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311681216))), lut = tensor<fp16, [2]>([-0x1.19p-6, 0x1.19p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_63_cast = conv(dilations = var_3428, groups = var_1186, pad = v_63_pad_0, pad_type = v_63_pad_type_0, strides = var_3426, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_63_cast")];
            tensor<int32, [4]> var_3432 = const()[name = tensor<string, []>("op_3432"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3433_cast = reshape(shape = var_3432, x = q_63_cast)[name = tensor<string, []>("op_3433_cast")];
            tensor<int32, [4]> var_3434 = const()[name = tensor<string, []>("op_3434"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3435_cast = reshape(shape = var_3434, x = k_63_cast)[name = tensor<string, []>("op_3435_cast")];
            tensor<int32, [4]> var_3436 = const()[name = tensor<string, []>("op_3436"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3437_cast = reshape(shape = var_3436, x = v_63_cast)[name = tensor<string, []>("op_3437_cast")];
            tensor<bool, []> attn_weights_125_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_125_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_125_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_125_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_125_cast = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_3433_cast, y = var_3435_cast)[name = tensor<string, []>("attn_weights_125_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_127_cast = mul(x = attn_weights_125_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_127_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_3441_cast = softmax(axis = var_1170, x = attn_weights_127_cast)[name = tensor<string, []>("op_3441_cast")];
            tensor<bool, []> attn_63_transpose_x_0 = const()[name = tensor<string, []>("attn_63_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_63_transpose_y_0 = const()[name = tensor<string, []>("attn_63_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_63_cast = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3437_cast, y = var_3441_cast)[name = tensor<string, []>("attn_63_cast")];
            tensor<int32, [4]> var_3445 = const()[name = tensor<string, []>("op_3445"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_239_cast = reshape(shape = var_3445, x = attn_63_cast)[name = tensor<string, []>("input_239_cast")];
            tensor<int32, [2]> var_3450 = const()[name = tensor<string, []>("op_3450"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3452 = const()[name = tensor<string, []>("op_3452"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3454_pad_type_0 = const()[name = tensor<string, []>("op_3454_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3454_pad_0 = const()[name = tensor<string, []>("op_3454_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312008960))), lut = tensor<fp16, [2]>([-0x1.6ecp-7, 0x1.6ep-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312213824)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3454_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_3452, groups = var_1186, pad = var_3454_pad_0, pad_type = var_3454_pad_type_0, strides = var_3450, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_239_cast)[name = tensor<string, []>("op_3454_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_95_cast = add(x = var_3454_cast, y = inputs_93_cast)[name = tensor<string, []>("inputs_95_cast")];
            tensor<int32, [1]> var_3458 = const()[name = tensor<string, []>("op_3458"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_95_cast = reduce_mean(axes = var_3458, keep_dims = var_1181, x = inputs_95_cast)[name = tensor<string, []>("channels_mean_95_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_95_cast = sub(x = inputs_95_cast, y = channels_mean_95_cast)[name = tensor<string, []>("zero_mean_95_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_95_cast = mul(x = zero_mean_95_cast, y = zero_mean_95_cast)[name = tensor<string, []>("zero_mean_sq_95_cast")];
            tensor<int32, [1]> var_3462 = const()[name = tensor<string, []>("op_3462"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3463_cast = reduce_mean(axes = var_3462, keep_dims = var_1181, x = zero_mean_sq_95_cast)[name = tensor<string, []>("op_3463_cast")];
            tensor<fp16, []> var_3464_to_fp16 = const()[name = tensor<string, []>("op_3464_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3465_cast = add(x = var_3463_cast, y = var_3464_to_fp16)[name = tensor<string, []>("op_3465_cast")];
            tensor<fp16, []> denom_95_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_95_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_95_cast = rsqrt(epsilon = denom_95_epsilon_0_to_fp16, x = var_3465_cast)[name = tensor<string, []>("denom_95_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_95_cast = mul(x = zero_mean_95_cast, y = denom_95_cast)[name = tensor<string, []>("out_95_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3469_to_fp16 = const()[name = tensor<string, []>("op_3469_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312216448)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3470_cast = add(x = out_95_cast, y = var_3469_to_fp16)[name = tensor<string, []>("op_3470_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3472_to_fp16 = const()[name = tensor<string, []>("op_3472_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312219072)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_241_cast = mul(x = var_3470_cast, y = var_3472_to_fp16)[name = tensor<string, []>("input_241_cast")];
            tensor<int32, [2]> var_3480 = const()[name = tensor<string, []>("op_3480"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3482 = const()[name = tensor<string, []>("op_3482"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3484_pad_type_0 = const()[name = tensor<string, []>("op_3484_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3484_pad_0 = const()[name = tensor<string, []>("op_3484_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312221696))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318775360))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318775488)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_3484_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_3482, groups = var_1186, pad = var_3484_pad_0, pad_type = var_3484_pad_type_0, strides = var_3480, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_241_cast)[name = tensor<string, []>("op_3484_cast")];
            tensor<int32, [2]> var_3485_split_sizes_0 = const()[name = tensor<string, []>("op_3485_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_3485_axis_0 = const()[name = tensor<string, []>("op_3485_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_3485_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_3485_cast_1 = split(axis = var_3485_axis_0, split_sizes = var_3485_split_sizes_0, x = var_3484_cast)[name = tensor<string, []>("op_3485_cast")];
            tensor<string, []> var_3487_mode_0 = const()[name = tensor<string, []>("op_3487_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_3487_cast = gelu(mode = var_3487_mode_0, x = var_3485_cast_1)[name = tensor<string, []>("op_3487_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_243_cast = mul(x = var_3485_cast_0, y = var_3487_cast)[name = tensor<string, []>("input_243_cast")];
            tensor<int32, [2]> var_3491 = const()[name = tensor<string, []>("op_3491"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3493 = const()[name = tensor<string, []>("op_3493"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3495_pad_type_0 = const()[name = tensor<string, []>("op_3495_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3495_pad_0 = const()[name = tensor<string, []>("op_3495_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318796032))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322072896))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322073024)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3495_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_3493, groups = var_1186, pad = var_3495_pad_0, pad_type = var_3495_pad_type_0, strides = var_3491, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_243_cast)[name = tensor<string, []>("op_3495_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_97_cast = add(x = var_3495_cast, y = inputs_95_cast)[name = tensor<string, []>("inputs_97_cast")];
            tensor<int32, [1]> var_3505 = const()[name = tensor<string, []>("op_3505"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_97_cast = reduce_mean(axes = var_3505, keep_dims = var_1181, x = inputs_97_cast)[name = tensor<string, []>("channels_mean_97_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_97_cast = sub(x = inputs_97_cast, y = channels_mean_97_cast)[name = tensor<string, []>("zero_mean_97_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_97_cast = mul(x = zero_mean_97_cast, y = zero_mean_97_cast)[name = tensor<string, []>("zero_mean_sq_97_cast")];
            tensor<int32, [1]> var_3509 = const()[name = tensor<string, []>("op_3509"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3510_cast = reduce_mean(axes = var_3509, keep_dims = var_1181, x = zero_mean_sq_97_cast)[name = tensor<string, []>("op_3510_cast")];
            tensor<fp16, []> var_3511_to_fp16 = const()[name = tensor<string, []>("op_3511_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3512_cast = add(x = var_3510_cast, y = var_3511_to_fp16)[name = tensor<string, []>("op_3512_cast")];
            tensor<fp16, []> denom_97_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_97_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_97_cast = rsqrt(epsilon = denom_97_epsilon_0_to_fp16, x = var_3512_cast)[name = tensor<string, []>("denom_97_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_97_cast = mul(x = zero_mean_97_cast, y = denom_97_cast)[name = tensor<string, []>("out_97_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3516_to_fp16 = const()[name = tensor<string, []>("op_3516_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322075648)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3517_cast = add(x = out_97_cast, y = var_3516_to_fp16)[name = tensor<string, []>("op_3517_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3519_to_fp16 = const()[name = tensor<string, []>("op_3519_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322078272)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_149_cast = mul(x = var_3517_cast, y = var_3519_to_fp16)[name = tensor<string, []>("hidden_states_149_cast")];
            tensor<int32, [2]> var_3526 = const()[name = tensor<string, []>("op_3526"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3528 = const()[name = tensor<string, []>("op_3528"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_65_pad_type_0 = const()[name = tensor<string, []>("q_65_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_65_pad_0 = const()[name = tensor<string, []>("q_65_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322080896))), lut = tensor<fp16, [4]>([-0x1.438p-5, -0x1.854p-7, 0x1.848p-7, 0x1.438p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_65_cast = conv(dilations = var_3528, groups = var_1186, pad = q_65_pad_0, pad_type = q_65_pad_type_0, strides = var_3526, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_149_cast)[name = tensor<string, []>("q_65_cast")];
            tensor<int32, [2]> var_3532 = const()[name = tensor<string, []>("op_3532"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3534 = const()[name = tensor<string, []>("op_3534"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_65_pad_type_0 = const()[name = tensor<string, []>("k_65_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_65_pad_0 = const()[name = tensor<string, []>("k_65_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322490560))), lut = tensor<fp16, [4]>([-0x1.44cp-5, -0x1.86cp-7, 0x1.854p-7, 0x1.444p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_65_cast = conv(dilations = var_3534, groups = var_1186, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_3532, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_149_cast)[name = tensor<string, []>("k_65_cast")];
            tensor<int32, [2]> var_3538 = const()[name = tensor<string, []>("op_3538"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3540 = const()[name = tensor<string, []>("op_3540"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_65_pad_type_0 = const()[name = tensor<string, []>("v_65_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_65_pad_0 = const()[name = tensor<string, []>("v_65_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322900224))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323719488))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_65_cast = conv(dilations = var_3540, groups = var_1186, pad = v_65_pad_0, pad_type = v_65_pad_type_0, strides = var_3538, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_149_cast)[name = tensor<string, []>("v_65_cast")];
            tensor<int32, [4]> var_3544 = const()[name = tensor<string, []>("op_3544"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3545_cast = reshape(shape = var_3544, x = q_65_cast)[name = tensor<string, []>("op_3545_cast")];
            tensor<int32, [4]> var_3546 = const()[name = tensor<string, []>("op_3546"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3547_cast = reshape(shape = var_3546, x = k_65_cast)[name = tensor<string, []>("op_3547_cast")];
            tensor<int32, [4]> var_3548 = const()[name = tensor<string, []>("op_3548"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3549_cast = reshape(shape = var_3548, x = v_65_cast)[name = tensor<string, []>("op_3549_cast")];
            tensor<bool, []> attn_weights_129_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_129_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_129_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_129_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_129_cast = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3545_cast, y = var_3547_cast)[name = tensor<string, []>("attn_weights_129_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_131_cast = mul(x = attn_weights_129_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_131_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_3553_cast = softmax(axis = var_1170, x = attn_weights_131_cast)[name = tensor<string, []>("op_3553_cast")];
            tensor<bool, []> attn_65_transpose_x_0 = const()[name = tensor<string, []>("attn_65_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_65_transpose_y_0 = const()[name = tensor<string, []>("attn_65_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_65_cast = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3549_cast, y = var_3553_cast)[name = tensor<string, []>("attn_65_cast")];
            tensor<int32, [4]> var_3557 = const()[name = tensor<string, []>("op_3557"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_245_cast = reshape(shape = var_3557, x = attn_65_cast)[name = tensor<string, []>("input_245_cast")];
            tensor<int32, [2]> var_3562 = const()[name = tensor<string, []>("op_3562"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3564 = const()[name = tensor<string, []>("op_3564"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3566_pad_type_0 = const()[name = tensor<string, []>("op_3566_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3566_pad_0 = const()[name = tensor<string, []>("op_3566_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323719616))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324538880))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324539008)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3566_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_3564, groups = var_1186, pad = var_3566_pad_0, pad_type = var_3566_pad_type_0, strides = var_3562, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_245_cast)[name = tensor<string, []>("op_3566_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_99_cast = add(x = var_3566_cast, y = inputs_97_cast)[name = tensor<string, []>("inputs_99_cast")];
            tensor<int32, [1]> var_3570 = const()[name = tensor<string, []>("op_3570"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_99_cast = reduce_mean(axes = var_3570, keep_dims = var_1181, x = inputs_99_cast)[name = tensor<string, []>("channels_mean_99_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_99_cast = sub(x = inputs_99_cast, y = channels_mean_99_cast)[name = tensor<string, []>("zero_mean_99_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_99_cast = mul(x = zero_mean_99_cast, y = zero_mean_99_cast)[name = tensor<string, []>("zero_mean_sq_99_cast")];
            tensor<int32, [1]> var_3574 = const()[name = tensor<string, []>("op_3574"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3575_cast = reduce_mean(axes = var_3574, keep_dims = var_1181, x = zero_mean_sq_99_cast)[name = tensor<string, []>("op_3575_cast")];
            tensor<fp16, []> var_3576_to_fp16 = const()[name = tensor<string, []>("op_3576_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3577_cast = add(x = var_3575_cast, y = var_3576_to_fp16)[name = tensor<string, []>("op_3577_cast")];
            tensor<fp16, []> denom_99_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_99_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_99_cast = rsqrt(epsilon = denom_99_epsilon_0_to_fp16, x = var_3577_cast)[name = tensor<string, []>("denom_99_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_99_cast = mul(x = zero_mean_99_cast, y = denom_99_cast)[name = tensor<string, []>("out_99_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3581_to_fp16 = const()[name = tensor<string, []>("op_3581_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324541632)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3582_cast = add(x = out_99_cast, y = var_3581_to_fp16)[name = tensor<string, []>("op_3582_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3584_to_fp16 = const()[name = tensor<string, []>("op_3584_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324544256)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_151_cast = mul(x = var_3582_cast, y = var_3584_to_fp16)[name = tensor<string, []>("hidden_states_151_cast")];
            tensor<int32, [2]> var_3591 = const()[name = tensor<string, []>("op_3591"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3593 = const()[name = tensor<string, []>("op_3593"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_67_pad_type_0 = const()[name = tensor<string, []>("q_67_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_67_pad_0 = const()[name = tensor<string, []>("q_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324546880))), lut = tensor<fp16, [2]>([-0x1.354p-6, 0x1.364p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_67_cast = conv(dilations = var_3593, groups = var_1186, pad = q_67_pad_0, pad_type = q_67_pad_type_0, strides = var_3591, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_151_cast)[name = tensor<string, []>("q_67_cast")];
            tensor<int32, [2]> var_3597 = const()[name = tensor<string, []>("op_3597"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3599 = const()[name = tensor<string, []>("op_3599"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_67_pad_type_0 = const()[name = tensor<string, []>("k_67_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_67_pad_0 = const()[name = tensor<string, []>("k_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324751744))), lut = tensor<fp16, [2]>([-0x1.08p-6, 0x1.084p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_67_cast = conv(dilations = var_3599, groups = var_1186, pad = k_67_pad_0, pad_type = k_67_pad_type_0, strides = var_3597, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_67_cast")];
            tensor<int32, [2]> var_3603 = const()[name = tensor<string, []>("op_3603"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3605 = const()[name = tensor<string, []>("op_3605"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_67_pad_type_0 = const()[name = tensor<string, []>("v_67_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_67_pad_0 = const()[name = tensor<string, []>("v_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325079488))), lut = tensor<fp16, [2]>([-0x1.1f4p-6, 0x1.2p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_67_cast = conv(dilations = var_3605, groups = var_1186, pad = v_67_pad_0, pad_type = v_67_pad_type_0, strides = var_3603, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_67_cast")];
            tensor<int32, [4]> var_3609 = const()[name = tensor<string, []>("op_3609"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3610_cast = reshape(shape = var_3609, x = q_67_cast)[name = tensor<string, []>("op_3610_cast")];
            tensor<int32, [4]> var_3611 = const()[name = tensor<string, []>("op_3611"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3612_cast = reshape(shape = var_3611, x = k_67_cast)[name = tensor<string, []>("op_3612_cast")];
            tensor<int32, [4]> var_3613 = const()[name = tensor<string, []>("op_3613"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3614_cast = reshape(shape = var_3613, x = v_67_cast)[name = tensor<string, []>("op_3614_cast")];
            tensor<bool, []> attn_weights_133_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_133_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = var_3610_cast, y = var_3612_cast)[name = tensor<string, []>("attn_weights_133_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_135_cast = mul(x = attn_weights_133_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_135_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_3618_cast = softmax(axis = var_1170, x = attn_weights_135_cast)[name = tensor<string, []>("op_3618_cast")];
            tensor<bool, []> attn_67_transpose_x_0 = const()[name = tensor<string, []>("attn_67_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_67_transpose_y_0 = const()[name = tensor<string, []>("attn_67_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_67_cast = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3614_cast, y = var_3618_cast)[name = tensor<string, []>("attn_67_cast")];
            tensor<int32, [4]> var_3622 = const()[name = tensor<string, []>("op_3622"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_247_cast = reshape(shape = var_3622, x = attn_67_cast)[name = tensor<string, []>("input_247_cast")];
            tensor<int32, [2]> var_3627 = const()[name = tensor<string, []>("op_3627"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3629 = const()[name = tensor<string, []>("op_3629"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3631_pad_type_0 = const()[name = tensor<string, []>("op_3631_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3631_pad_0 = const()[name = tensor<string, []>("op_3631_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325407232))), lut = tensor<fp16, [2]>([-0x1.6ecp-7, 0x1.6fp-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325612096)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3631_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_3629, groups = var_1186, pad = var_3631_pad_0, pad_type = var_3631_pad_type_0, strides = var_3627, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_247_cast)[name = tensor<string, []>("op_3631_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_101_cast = add(x = var_3631_cast, y = inputs_99_cast)[name = tensor<string, []>("inputs_101_cast")];
            tensor<int32, [1]> var_3635 = const()[name = tensor<string, []>("op_3635"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_101_cast = reduce_mean(axes = var_3635, keep_dims = var_1181, x = inputs_101_cast)[name = tensor<string, []>("channels_mean_101_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_101_cast = sub(x = inputs_101_cast, y = channels_mean_101_cast)[name = tensor<string, []>("zero_mean_101_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_101_cast = mul(x = zero_mean_101_cast, y = zero_mean_101_cast)[name = tensor<string, []>("zero_mean_sq_101_cast")];
            tensor<int32, [1]> var_3639 = const()[name = tensor<string, []>("op_3639"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3640_cast = reduce_mean(axes = var_3639, keep_dims = var_1181, x = zero_mean_sq_101_cast)[name = tensor<string, []>("op_3640_cast")];
            tensor<fp16, []> var_3641_to_fp16 = const()[name = tensor<string, []>("op_3641_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3642_cast = add(x = var_3640_cast, y = var_3641_to_fp16)[name = tensor<string, []>("op_3642_cast")];
            tensor<fp16, []> denom_101_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_101_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_101_cast = rsqrt(epsilon = denom_101_epsilon_0_to_fp16, x = var_3642_cast)[name = tensor<string, []>("denom_101_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_101_cast = mul(x = zero_mean_101_cast, y = denom_101_cast)[name = tensor<string, []>("out_101_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3646_to_fp16 = const()[name = tensor<string, []>("op_3646_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325614720)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3647_cast = add(x = out_101_cast, y = var_3646_to_fp16)[name = tensor<string, []>("op_3647_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3649_to_fp16 = const()[name = tensor<string, []>("op_3649_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325617344)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_249_cast = mul(x = var_3647_cast, y = var_3649_to_fp16)[name = tensor<string, []>("input_249_cast")];
            tensor<int32, [2]> var_3657 = const()[name = tensor<string, []>("op_3657"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3659 = const()[name = tensor<string, []>("op_3659"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3661_pad_type_0 = const()[name = tensor<string, []>("op_3661_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3661_pad_0 = const()[name = tensor<string, []>("op_3661_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325619968))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332173632))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332173760)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_3661_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_3659, groups = var_1186, pad = var_3661_pad_0, pad_type = var_3661_pad_type_0, strides = var_3657, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_249_cast)[name = tensor<string, []>("op_3661_cast")];
            tensor<int32, [2]> var_3662_split_sizes_0 = const()[name = tensor<string, []>("op_3662_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_3662_axis_0 = const()[name = tensor<string, []>("op_3662_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_3662_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_3662_cast_1 = split(axis = var_3662_axis_0, split_sizes = var_3662_split_sizes_0, x = var_3661_cast)[name = tensor<string, []>("op_3662_cast")];
            tensor<string, []> var_3664_mode_0 = const()[name = tensor<string, []>("op_3664_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_3664_cast = gelu(mode = var_3664_mode_0, x = var_3662_cast_1)[name = tensor<string, []>("op_3664_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_251_cast = mul(x = var_3662_cast_0, y = var_3664_cast)[name = tensor<string, []>("input_251_cast")];
            tensor<int32, [2]> var_3668 = const()[name = tensor<string, []>("op_3668"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3670 = const()[name = tensor<string, []>("op_3670"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3672_pad_type_0 = const()[name = tensor<string, []>("op_3672_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3672_pad_0 = const()[name = tensor<string, []>("op_3672_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332194304))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335471168))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335471296)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3672_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_3670, groups = var_1186, pad = var_3672_pad_0, pad_type = var_3672_pad_type_0, strides = var_3668, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_251_cast)[name = tensor<string, []>("op_3672_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_103_cast = add(x = var_3672_cast, y = inputs_101_cast)[name = tensor<string, []>("inputs_103_cast")];
            tensor<int32, [1]> var_3682 = const()[name = tensor<string, []>("op_3682"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_103_cast = reduce_mean(axes = var_3682, keep_dims = var_1181, x = inputs_103_cast)[name = tensor<string, []>("channels_mean_103_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_103_cast = sub(x = inputs_103_cast, y = channels_mean_103_cast)[name = tensor<string, []>("zero_mean_103_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_103_cast = mul(x = zero_mean_103_cast, y = zero_mean_103_cast)[name = tensor<string, []>("zero_mean_sq_103_cast")];
            tensor<int32, [1]> var_3686 = const()[name = tensor<string, []>("op_3686"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3687_cast = reduce_mean(axes = var_3686, keep_dims = var_1181, x = zero_mean_sq_103_cast)[name = tensor<string, []>("op_3687_cast")];
            tensor<fp16, []> var_3688_to_fp16 = const()[name = tensor<string, []>("op_3688_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3689_cast = add(x = var_3687_cast, y = var_3688_to_fp16)[name = tensor<string, []>("op_3689_cast")];
            tensor<fp16, []> denom_103_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_103_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_103_cast = rsqrt(epsilon = denom_103_epsilon_0_to_fp16, x = var_3689_cast)[name = tensor<string, []>("denom_103_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_103_cast = mul(x = zero_mean_103_cast, y = denom_103_cast)[name = tensor<string, []>("out_103_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3693_to_fp16 = const()[name = tensor<string, []>("op_3693_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335473920)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3694_cast = add(x = out_103_cast, y = var_3693_to_fp16)[name = tensor<string, []>("op_3694_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3696_to_fp16 = const()[name = tensor<string, []>("op_3696_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335476544)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_155_cast = mul(x = var_3694_cast, y = var_3696_to_fp16)[name = tensor<string, []>("hidden_states_155_cast")];
            tensor<int32, [2]> var_3703 = const()[name = tensor<string, []>("op_3703"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3705 = const()[name = tensor<string, []>("op_3705"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_69_pad_type_0 = const()[name = tensor<string, []>("q_69_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_69_pad_0 = const()[name = tensor<string, []>("q_69_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335479168))), lut = tensor<fp16, [2]>([-0x1.568p-6, 0x1.58p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_69_cast = conv(dilations = var_3705, groups = var_1186, pad = q_69_pad_0, pad_type = q_69_pad_type_0, strides = var_3703, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_155_cast)[name = tensor<string, []>("q_69_cast")];
            tensor<int32, [2]> var_3709 = const()[name = tensor<string, []>("op_3709"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3711 = const()[name = tensor<string, []>("op_3711"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_69_pad_type_0 = const()[name = tensor<string, []>("k_69_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_69_pad_0 = const()[name = tensor<string, []>("k_69_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335684032))), lut = tensor<fp16, [2]>([-0x1.574p-6, 0x1.56cp-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_69_cast = conv(dilations = var_3711, groups = var_1186, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_3709, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_155_cast)[name = tensor<string, []>("k_69_cast")];
            tensor<int32, [2]> var_3715 = const()[name = tensor<string, []>("op_3715"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3717 = const()[name = tensor<string, []>("op_3717"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_69_pad_type_0 = const()[name = tensor<string, []>("v_69_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_69_pad_0 = const()[name = tensor<string, []>("v_69_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335888896))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336708160))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_69_cast = conv(dilations = var_3717, groups = var_1186, pad = v_69_pad_0, pad_type = v_69_pad_type_0, strides = var_3715, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_155_cast)[name = tensor<string, []>("v_69_cast")];
            tensor<int32, [4]> var_3721 = const()[name = tensor<string, []>("op_3721"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3722_cast = reshape(shape = var_3721, x = q_69_cast)[name = tensor<string, []>("op_3722_cast")];
            tensor<int32, [4]> var_3723 = const()[name = tensor<string, []>("op_3723"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3724_cast = reshape(shape = var_3723, x = k_69_cast)[name = tensor<string, []>("op_3724_cast")];
            tensor<int32, [4]> var_3725 = const()[name = tensor<string, []>("op_3725"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3726_cast = reshape(shape = var_3725, x = v_69_cast)[name = tensor<string, []>("op_3726_cast")];
            tensor<bool, []> attn_weights_137_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_137_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_137_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_137_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_137_cast = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3722_cast, y = var_3724_cast)[name = tensor<string, []>("attn_weights_137_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_139_cast = mul(x = attn_weights_137_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_139_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_3730_cast = softmax(axis = var_1170, x = attn_weights_139_cast)[name = tensor<string, []>("op_3730_cast")];
            tensor<bool, []> attn_69_transpose_x_0 = const()[name = tensor<string, []>("attn_69_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_69_transpose_y_0 = const()[name = tensor<string, []>("attn_69_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_69_cast = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3726_cast, y = var_3730_cast)[name = tensor<string, []>("attn_69_cast")];
            tensor<int32, [4]> var_3734 = const()[name = tensor<string, []>("op_3734"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_253_cast = reshape(shape = var_3734, x = attn_69_cast)[name = tensor<string, []>("input_253_cast")];
            tensor<int32, [2]> var_3739 = const()[name = tensor<string, []>("op_3739"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3741 = const()[name = tensor<string, []>("op_3741"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3743_pad_type_0 = const()[name = tensor<string, []>("op_3743_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3743_pad_0 = const()[name = tensor<string, []>("op_3743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(336708288))), lut = tensor<fp16, [4]>([-0x1.35cp-5, -0x1.74p-7, 0x1.74p-7, 0x1.358p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337117952)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3743_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_3741, groups = var_1186, pad = var_3743_pad_0, pad_type = var_3743_pad_type_0, strides = var_3739, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_253_cast)[name = tensor<string, []>("op_3743_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_105_cast = add(x = var_3743_cast, y = inputs_103_cast)[name = tensor<string, []>("inputs_105_cast")];
            tensor<int32, [1]> var_3747 = const()[name = tensor<string, []>("op_3747"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_105_cast = reduce_mean(axes = var_3747, keep_dims = var_1181, x = inputs_105_cast)[name = tensor<string, []>("channels_mean_105_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_105_cast = sub(x = inputs_105_cast, y = channels_mean_105_cast)[name = tensor<string, []>("zero_mean_105_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_105_cast = mul(x = zero_mean_105_cast, y = zero_mean_105_cast)[name = tensor<string, []>("zero_mean_sq_105_cast")];
            tensor<int32, [1]> var_3751 = const()[name = tensor<string, []>("op_3751"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3752_cast = reduce_mean(axes = var_3751, keep_dims = var_1181, x = zero_mean_sq_105_cast)[name = tensor<string, []>("op_3752_cast")];
            tensor<fp16, []> var_3753_to_fp16 = const()[name = tensor<string, []>("op_3753_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3754_cast = add(x = var_3752_cast, y = var_3753_to_fp16)[name = tensor<string, []>("op_3754_cast")];
            tensor<fp16, []> denom_105_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_105_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_105_cast = rsqrt(epsilon = denom_105_epsilon_0_to_fp16, x = var_3754_cast)[name = tensor<string, []>("denom_105_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_105_cast = mul(x = zero_mean_105_cast, y = denom_105_cast)[name = tensor<string, []>("out_105_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3758_to_fp16 = const()[name = tensor<string, []>("op_3758_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337120576)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3759_cast = add(x = out_105_cast, y = var_3758_to_fp16)[name = tensor<string, []>("op_3759_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3761_to_fp16 = const()[name = tensor<string, []>("op_3761_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337123200)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_157_cast = mul(x = var_3759_cast, y = var_3761_to_fp16)[name = tensor<string, []>("hidden_states_157_cast")];
            tensor<int32, [2]> var_3768 = const()[name = tensor<string, []>("op_3768"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3770 = const()[name = tensor<string, []>("op_3770"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_71_pad_type_0 = const()[name = tensor<string, []>("q_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_71_pad_0 = const()[name = tensor<string, []>("q_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337125824))), lut = tensor<fp16, [2]>([-0x1.26cp-6, 0x1.268p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_71_cast = conv(dilations = var_3770, groups = var_1186, pad = q_71_pad_0, pad_type = q_71_pad_type_0, strides = var_3768, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_157_cast)[name = tensor<string, []>("q_71_cast")];
            tensor<int32, [2]> var_3774 = const()[name = tensor<string, []>("op_3774"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3776 = const()[name = tensor<string, []>("op_3776"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_71_pad_type_0 = const()[name = tensor<string, []>("k_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_71_pad_0 = const()[name = tensor<string, []>("k_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337330688))), lut = tensor<fp16, [2]>([-0x1.f2cp-7, 0x1.f4cp-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_71_cast = conv(dilations = var_3776, groups = var_1186, pad = k_71_pad_0, pad_type = k_71_pad_type_0, strides = var_3774, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_71_cast")];
            tensor<int32, [2]> var_3780 = const()[name = tensor<string, []>("op_3780"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3782 = const()[name = tensor<string, []>("op_3782"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_71_pad_type_0 = const()[name = tensor<string, []>("v_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_71_pad_0 = const()[name = tensor<string, []>("v_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337658432))), lut = tensor<fp16, [2]>([-0x1.1ecp-6, 0x1.1f4p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_71_cast = conv(dilations = var_3782, groups = var_1186, pad = v_71_pad_0, pad_type = v_71_pad_type_0, strides = var_3780, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_71_cast")];
            tensor<int32, [4]> var_3786 = const()[name = tensor<string, []>("op_3786"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3787_cast = reshape(shape = var_3786, x = q_71_cast)[name = tensor<string, []>("op_3787_cast")];
            tensor<int32, [4]> var_3788 = const()[name = tensor<string, []>("op_3788"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3789_cast = reshape(shape = var_3788, x = k_71_cast)[name = tensor<string, []>("op_3789_cast")];
            tensor<int32, [4]> var_3790 = const()[name = tensor<string, []>("op_3790"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3791_cast = reshape(shape = var_3790, x = v_71_cast)[name = tensor<string, []>("op_3791_cast")];
            tensor<bool, []> attn_weights_141_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_141_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_141_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_141_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_141_cast = matmul(transpose_x = attn_weights_141_transpose_x_0, transpose_y = attn_weights_141_transpose_y_0, x = var_3787_cast, y = var_3789_cast)[name = tensor<string, []>("attn_weights_141_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_143_cast = mul(x = attn_weights_141_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_143_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_3795_cast = softmax(axis = var_1170, x = attn_weights_143_cast)[name = tensor<string, []>("op_3795_cast")];
            tensor<bool, []> attn_71_transpose_x_0 = const()[name = tensor<string, []>("attn_71_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_71_transpose_y_0 = const()[name = tensor<string, []>("attn_71_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_71_cast = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3791_cast, y = var_3795_cast)[name = tensor<string, []>("attn_71_cast")];
            tensor<int32, [4]> var_3799 = const()[name = tensor<string, []>("op_3799"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_255_cast = reshape(shape = var_3799, x = attn_71_cast)[name = tensor<string, []>("input_255_cast")];
            tensor<int32, [2]> var_3804 = const()[name = tensor<string, []>("op_3804"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3806 = const()[name = tensor<string, []>("op_3806"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3808_pad_type_0 = const()[name = tensor<string, []>("op_3808_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3808_pad_0 = const()[name = tensor<string, []>("op_3808_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337986176))), lut = tensor<fp16, [2]>([-0x1.618p-7, 0x1.628p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338191040)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3808_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_3806, groups = var_1186, pad = var_3808_pad_0, pad_type = var_3808_pad_type_0, strides = var_3804, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_255_cast)[name = tensor<string, []>("op_3808_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_107_cast = add(x = var_3808_cast, y = inputs_105_cast)[name = tensor<string, []>("inputs_107_cast")];
            tensor<int32, [1]> var_3812 = const()[name = tensor<string, []>("op_3812"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_107_cast = reduce_mean(axes = var_3812, keep_dims = var_1181, x = inputs_107_cast)[name = tensor<string, []>("channels_mean_107_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_107_cast = sub(x = inputs_107_cast, y = channels_mean_107_cast)[name = tensor<string, []>("zero_mean_107_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_107_cast = mul(x = zero_mean_107_cast, y = zero_mean_107_cast)[name = tensor<string, []>("zero_mean_sq_107_cast")];
            tensor<int32, [1]> var_3816 = const()[name = tensor<string, []>("op_3816"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3817_cast = reduce_mean(axes = var_3816, keep_dims = var_1181, x = zero_mean_sq_107_cast)[name = tensor<string, []>("op_3817_cast")];
            tensor<fp16, []> var_3818_to_fp16 = const()[name = tensor<string, []>("op_3818_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3819_cast = add(x = var_3817_cast, y = var_3818_to_fp16)[name = tensor<string, []>("op_3819_cast")];
            tensor<fp16, []> denom_107_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_107_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_107_cast = rsqrt(epsilon = denom_107_epsilon_0_to_fp16, x = var_3819_cast)[name = tensor<string, []>("denom_107_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_107_cast = mul(x = zero_mean_107_cast, y = denom_107_cast)[name = tensor<string, []>("out_107_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3823_to_fp16 = const()[name = tensor<string, []>("op_3823_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338193664)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3824_cast = add(x = out_107_cast, y = var_3823_to_fp16)[name = tensor<string, []>("op_3824_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3826_to_fp16 = const()[name = tensor<string, []>("op_3826_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338196288)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_257_cast = mul(x = var_3824_cast, y = var_3826_to_fp16)[name = tensor<string, []>("input_257_cast")];
            tensor<int32, [2]> var_3834 = const()[name = tensor<string, []>("op_3834"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3836 = const()[name = tensor<string, []>("op_3836"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3838_pad_type_0 = const()[name = tensor<string, []>("op_3838_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3838_pad_0 = const()[name = tensor<string, []>("op_3838_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338198912))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344752576))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344752704)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_3838_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_3836, groups = var_1186, pad = var_3838_pad_0, pad_type = var_3838_pad_type_0, strides = var_3834, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_257_cast)[name = tensor<string, []>("op_3838_cast")];
            tensor<int32, [2]> var_3839_split_sizes_0 = const()[name = tensor<string, []>("op_3839_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_3839_axis_0 = const()[name = tensor<string, []>("op_3839_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_3839_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_3839_cast_1 = split(axis = var_3839_axis_0, split_sizes = var_3839_split_sizes_0, x = var_3838_cast)[name = tensor<string, []>("op_3839_cast")];
            tensor<string, []> var_3841_mode_0 = const()[name = tensor<string, []>("op_3841_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_3841_cast = gelu(mode = var_3841_mode_0, x = var_3839_cast_1)[name = tensor<string, []>("op_3841_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_259_cast = mul(x = var_3839_cast_0, y = var_3841_cast)[name = tensor<string, []>("input_259_cast")];
            tensor<int32, [2]> var_3845 = const()[name = tensor<string, []>("op_3845"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3847 = const()[name = tensor<string, []>("op_3847"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3849_pad_type_0 = const()[name = tensor<string, []>("op_3849_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3849_pad_0 = const()[name = tensor<string, []>("op_3849_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344773248))), lut = tensor<fp16, [4]>([-0x1.4c4p-5, -0x1.8dp-7, 0x1.8dcp-7, 0x1.4c4p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346411712)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3849_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_3847, groups = var_1186, pad = var_3849_pad_0, pad_type = var_3849_pad_type_0, strides = var_3845, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_259_cast)[name = tensor<string, []>("op_3849_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_109_cast = add(x = var_3849_cast, y = inputs_107_cast)[name = tensor<string, []>("inputs_109_cast")];
            tensor<int32, [1]> var_3859 = const()[name = tensor<string, []>("op_3859"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_109_cast = reduce_mean(axes = var_3859, keep_dims = var_1181, x = inputs_109_cast)[name = tensor<string, []>("channels_mean_109_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_109_cast = sub(x = inputs_109_cast, y = channels_mean_109_cast)[name = tensor<string, []>("zero_mean_109_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_109_cast = mul(x = zero_mean_109_cast, y = zero_mean_109_cast)[name = tensor<string, []>("zero_mean_sq_109_cast")];
            tensor<int32, [1]> var_3863 = const()[name = tensor<string, []>("op_3863"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3864_cast = reduce_mean(axes = var_3863, keep_dims = var_1181, x = zero_mean_sq_109_cast)[name = tensor<string, []>("op_3864_cast")];
            tensor<fp16, []> var_3865_to_fp16 = const()[name = tensor<string, []>("op_3865_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3866_cast = add(x = var_3864_cast, y = var_3865_to_fp16)[name = tensor<string, []>("op_3866_cast")];
            tensor<fp16, []> denom_109_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_109_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_109_cast = rsqrt(epsilon = denom_109_epsilon_0_to_fp16, x = var_3866_cast)[name = tensor<string, []>("denom_109_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_109_cast = mul(x = zero_mean_109_cast, y = denom_109_cast)[name = tensor<string, []>("out_109_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3870_to_fp16 = const()[name = tensor<string, []>("op_3870_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346414336)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3871_cast = add(x = out_109_cast, y = var_3870_to_fp16)[name = tensor<string, []>("op_3871_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3873_to_fp16 = const()[name = tensor<string, []>("op_3873_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346416960)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_161_cast = mul(x = var_3871_cast, y = var_3873_to_fp16)[name = tensor<string, []>("hidden_states_161_cast")];
            tensor<int32, [2]> var_3880 = const()[name = tensor<string, []>("op_3880"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3882 = const()[name = tensor<string, []>("op_3882"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_73_pad_type_0 = const()[name = tensor<string, []>("q_73_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_73_pad_0 = const()[name = tensor<string, []>("q_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346419584))), lut = tensor<fp16, [4]>([-0x1.3dcp-5, -0x1.7dp-7, 0x1.7f8p-7, 0x1.3e8p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_73_cast = conv(dilations = var_3882, groups = var_1186, pad = q_73_pad_0, pad_type = q_73_pad_type_0, strides = var_3880, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_161_cast)[name = tensor<string, []>("q_73_cast")];
            tensor<int32, [2]> var_3886 = const()[name = tensor<string, []>("op_3886"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3888 = const()[name = tensor<string, []>("op_3888"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_73_pad_type_0 = const()[name = tensor<string, []>("k_73_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_73_pad_0 = const()[name = tensor<string, []>("k_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346829248))), lut = tensor<fp16, [2]>([-0x1.514p-6, 0x1.52p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_73_cast = conv(dilations = var_3888, groups = var_1186, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_3886, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_161_cast)[name = tensor<string, []>("k_73_cast")];
            tensor<int32, [2]> var_3892 = const()[name = tensor<string, []>("op_3892"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3894 = const()[name = tensor<string, []>("op_3894"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_73_pad_type_0 = const()[name = tensor<string, []>("v_73_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_73_pad_0 = const()[name = tensor<string, []>("v_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347034112))), lut = tensor<fp16, [4]>([-0x1.2ep-5, -0x1.6d4p-7, 0x1.6acp-7, 0x1.2d8p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_73_cast = conv(dilations = var_3894, groups = var_1186, pad = v_73_pad_0, pad_type = v_73_pad_type_0, strides = var_3892, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_161_cast)[name = tensor<string, []>("v_73_cast")];
            tensor<int32, [4]> var_3898 = const()[name = tensor<string, []>("op_3898"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3899_cast = reshape(shape = var_3898, x = q_73_cast)[name = tensor<string, []>("op_3899_cast")];
            tensor<int32, [4]> var_3900 = const()[name = tensor<string, []>("op_3900"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3901_cast = reshape(shape = var_3900, x = k_73_cast)[name = tensor<string, []>("op_3901_cast")];
            tensor<int32, [4]> var_3902 = const()[name = tensor<string, []>("op_3902"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3903_cast = reshape(shape = var_3902, x = v_73_cast)[name = tensor<string, []>("op_3903_cast")];
            tensor<bool, []> attn_weights_145_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_145_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_145_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_145_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_145_cast = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3899_cast, y = var_3901_cast)[name = tensor<string, []>("attn_weights_145_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_147_cast = mul(x = attn_weights_145_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_147_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_3907_cast = softmax(axis = var_1170, x = attn_weights_147_cast)[name = tensor<string, []>("op_3907_cast")];
            tensor<bool, []> attn_73_transpose_x_0 = const()[name = tensor<string, []>("attn_73_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_73_transpose_y_0 = const()[name = tensor<string, []>("attn_73_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_73_cast = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_3903_cast, y = var_3907_cast)[name = tensor<string, []>("attn_73_cast")];
            tensor<int32, [4]> var_3911 = const()[name = tensor<string, []>("op_3911"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_261_cast = reshape(shape = var_3911, x = attn_73_cast)[name = tensor<string, []>("input_261_cast")];
            tensor<int32, [2]> var_3916 = const()[name = tensor<string, []>("op_3916"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3918 = const()[name = tensor<string, []>("op_3918"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3920_pad_type_0 = const()[name = tensor<string, []>("op_3920_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3920_pad_0 = const()[name = tensor<string, []>("op_3920_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347443776))), lut = tensor<fp16, [4]>([-0x1.2c4p-5, -0x1.6c8p-7, 0x1.64p-7, 0x1.2a8p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347853440)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3920_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_3918, groups = var_1186, pad = var_3920_pad_0, pad_type = var_3920_pad_type_0, strides = var_3916, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_261_cast)[name = tensor<string, []>("op_3920_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_111_cast = add(x = var_3920_cast, y = inputs_109_cast)[name = tensor<string, []>("inputs_111_cast")];
            tensor<int32, [1]> var_3924 = const()[name = tensor<string, []>("op_3924"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_111_cast = reduce_mean(axes = var_3924, keep_dims = var_1181, x = inputs_111_cast)[name = tensor<string, []>("channels_mean_111_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_111_cast = sub(x = inputs_111_cast, y = channels_mean_111_cast)[name = tensor<string, []>("zero_mean_111_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_111_cast = mul(x = zero_mean_111_cast, y = zero_mean_111_cast)[name = tensor<string, []>("zero_mean_sq_111_cast")];
            tensor<int32, [1]> var_3928 = const()[name = tensor<string, []>("op_3928"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3929_cast = reduce_mean(axes = var_3928, keep_dims = var_1181, x = zero_mean_sq_111_cast)[name = tensor<string, []>("op_3929_cast")];
            tensor<fp16, []> var_3930_to_fp16 = const()[name = tensor<string, []>("op_3930_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3931_cast = add(x = var_3929_cast, y = var_3930_to_fp16)[name = tensor<string, []>("op_3931_cast")];
            tensor<fp16, []> denom_111_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_111_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_111_cast = rsqrt(epsilon = denom_111_epsilon_0_to_fp16, x = var_3931_cast)[name = tensor<string, []>("denom_111_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_111_cast = mul(x = zero_mean_111_cast, y = denom_111_cast)[name = tensor<string, []>("out_111_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3935_to_fp16 = const()[name = tensor<string, []>("op_3935_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347856064)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3936_cast = add(x = out_111_cast, y = var_3935_to_fp16)[name = tensor<string, []>("op_3936_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3938_to_fp16 = const()[name = tensor<string, []>("op_3938_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347858688)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_163_cast = mul(x = var_3936_cast, y = var_3938_to_fp16)[name = tensor<string, []>("hidden_states_163_cast")];
            tensor<int32, [2]> var_3945 = const()[name = tensor<string, []>("op_3945"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3947 = const()[name = tensor<string, []>("op_3947"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_75_pad_type_0 = const()[name = tensor<string, []>("q_75_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_75_pad_0 = const()[name = tensor<string, []>("q_75_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347861312))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348680576))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_75_cast = conv(dilations = var_3947, groups = var_1186, pad = q_75_pad_0, pad_type = q_75_pad_type_0, strides = var_3945, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_163_cast)[name = tensor<string, []>("q_75_cast")];
            tensor<int32, [2]> var_3951 = const()[name = tensor<string, []>("op_3951"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3953 = const()[name = tensor<string, []>("op_3953"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_75_pad_type_0 = const()[name = tensor<string, []>("k_75_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_75_pad_0 = const()[name = tensor<string, []>("k_75_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(348680704))), lut = tensor<fp16, [2]>([-0x1.d3p-7, 0x1.d54p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_75_cast = conv(dilations = var_3953, groups = var_1186, pad = k_75_pad_0, pad_type = k_75_pad_type_0, strides = var_3951, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_75_cast")];
            tensor<int32, [2]> var_3957 = const()[name = tensor<string, []>("op_3957"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3959 = const()[name = tensor<string, []>("op_3959"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_75_pad_type_0 = const()[name = tensor<string, []>("v_75_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_75_pad_0 = const()[name = tensor<string, []>("v_75_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349008448))), lut = tensor<fp16, [2]>([-0x1.17cp-6, 0x1.188p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_75_cast = conv(dilations = var_3959, groups = var_1186, pad = v_75_pad_0, pad_type = v_75_pad_type_0, strides = var_3957, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_75_cast")];
            tensor<int32, [4]> var_3963 = const()[name = tensor<string, []>("op_3963"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_3964_cast = reshape(shape = var_3963, x = q_75_cast)[name = tensor<string, []>("op_3964_cast")];
            tensor<int32, [4]> var_3965 = const()[name = tensor<string, []>("op_3965"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3966_cast = reshape(shape = var_3965, x = k_75_cast)[name = tensor<string, []>("op_3966_cast")];
            tensor<int32, [4]> var_3967 = const()[name = tensor<string, []>("op_3967"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3968_cast = reshape(shape = var_3967, x = v_75_cast)[name = tensor<string, []>("op_3968_cast")];
            tensor<bool, []> attn_weights_149_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_149_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_149_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_149_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_149_cast = matmul(transpose_x = attn_weights_149_transpose_x_0, transpose_y = attn_weights_149_transpose_y_0, x = var_3964_cast, y = var_3966_cast)[name = tensor<string, []>("attn_weights_149_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_151_cast = mul(x = attn_weights_149_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_151_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_3972_cast = softmax(axis = var_1170, x = attn_weights_151_cast)[name = tensor<string, []>("op_3972_cast")];
            tensor<bool, []> attn_75_transpose_x_0 = const()[name = tensor<string, []>("attn_75_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_75_transpose_y_0 = const()[name = tensor<string, []>("attn_75_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_75_cast = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_3968_cast, y = var_3972_cast)[name = tensor<string, []>("attn_75_cast")];
            tensor<int32, [4]> var_3976 = const()[name = tensor<string, []>("op_3976"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_263_cast = reshape(shape = var_3976, x = attn_75_cast)[name = tensor<string, []>("input_263_cast")];
            tensor<int32, [2]> var_3981 = const()[name = tensor<string, []>("op_3981"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3983 = const()[name = tensor<string, []>("op_3983"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3985_pad_type_0 = const()[name = tensor<string, []>("op_3985_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3985_pad_0 = const()[name = tensor<string, []>("op_3985_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349336192))), lut = tensor<fp16, [2]>([-0x1.5bp-7, 0x1.5a8p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349541056)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_3985_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_3983, groups = var_1186, pad = var_3985_pad_0, pad_type = var_3985_pad_type_0, strides = var_3981, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_263_cast)[name = tensor<string, []>("op_3985_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_113_cast = add(x = var_3985_cast, y = inputs_111_cast)[name = tensor<string, []>("inputs_113_cast")];
            tensor<int32, [1]> var_3989 = const()[name = tensor<string, []>("op_3989"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_113_cast = reduce_mean(axes = var_3989, keep_dims = var_1181, x = inputs_113_cast)[name = tensor<string, []>("channels_mean_113_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_113_cast = sub(x = inputs_113_cast, y = channels_mean_113_cast)[name = tensor<string, []>("zero_mean_113_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_113_cast = mul(x = zero_mean_113_cast, y = zero_mean_113_cast)[name = tensor<string, []>("zero_mean_sq_113_cast")];
            tensor<int32, [1]> var_3993 = const()[name = tensor<string, []>("op_3993"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3994_cast = reduce_mean(axes = var_3993, keep_dims = var_1181, x = zero_mean_sq_113_cast)[name = tensor<string, []>("op_3994_cast")];
            tensor<fp16, []> var_3995_to_fp16 = const()[name = tensor<string, []>("op_3995_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3996_cast = add(x = var_3994_cast, y = var_3995_to_fp16)[name = tensor<string, []>("op_3996_cast")];
            tensor<fp16, []> denom_113_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_113_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_113_cast = rsqrt(epsilon = denom_113_epsilon_0_to_fp16, x = var_3996_cast)[name = tensor<string, []>("denom_113_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_113_cast = mul(x = zero_mean_113_cast, y = denom_113_cast)[name = tensor<string, []>("out_113_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4000_to_fp16 = const()[name = tensor<string, []>("op_4000_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349543680)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4001_cast = add(x = out_113_cast, y = var_4000_to_fp16)[name = tensor<string, []>("op_4001_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4003_to_fp16 = const()[name = tensor<string, []>("op_4003_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349546304)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_265_cast = mul(x = var_4001_cast, y = var_4003_to_fp16)[name = tensor<string, []>("input_265_cast")];
            tensor<int32, [2]> var_4011 = const()[name = tensor<string, []>("op_4011"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4013 = const()[name = tensor<string, []>("op_4013"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4015_pad_type_0 = const()[name = tensor<string, []>("op_4015_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4015_pad_0 = const()[name = tensor<string, []>("op_4015_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349548928))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356102592))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356102720)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_4015_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_4013, groups = var_1186, pad = var_4015_pad_0, pad_type = var_4015_pad_type_0, strides = var_4011, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_265_cast)[name = tensor<string, []>("op_4015_cast")];
            tensor<int32, [2]> var_4016_split_sizes_0 = const()[name = tensor<string, []>("op_4016_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_4016_axis_0 = const()[name = tensor<string, []>("op_4016_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_4016_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_4016_cast_1 = split(axis = var_4016_axis_0, split_sizes = var_4016_split_sizes_0, x = var_4015_cast)[name = tensor<string, []>("op_4016_cast")];
            tensor<string, []> var_4018_mode_0 = const()[name = tensor<string, []>("op_4018_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_4018_cast = gelu(mode = var_4018_mode_0, x = var_4016_cast_1)[name = tensor<string, []>("op_4018_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_267_cast = mul(x = var_4016_cast_0, y = var_4018_cast)[name = tensor<string, []>("input_267_cast")];
            tensor<int32, [2]> var_4022 = const()[name = tensor<string, []>("op_4022"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4024 = const()[name = tensor<string, []>("op_4024"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4026_pad_type_0 = const()[name = tensor<string, []>("op_4026_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4026_pad_0 = const()[name = tensor<string, []>("op_4026_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356123264))), lut = tensor<fp16, [4]>([-0x1.508p-5, -0x1.92cp-7, 0x1.92p-7, 0x1.504p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357761728)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4026_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_4024, groups = var_1186, pad = var_4026_pad_0, pad_type = var_4026_pad_type_0, strides = var_4022, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_267_cast)[name = tensor<string, []>("op_4026_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_115_cast = add(x = var_4026_cast, y = inputs_113_cast)[name = tensor<string, []>("inputs_115_cast")];
            tensor<int32, [1]> var_4036 = const()[name = tensor<string, []>("op_4036"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_115_cast = reduce_mean(axes = var_4036, keep_dims = var_1181, x = inputs_115_cast)[name = tensor<string, []>("channels_mean_115_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_115_cast = sub(x = inputs_115_cast, y = channels_mean_115_cast)[name = tensor<string, []>("zero_mean_115_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_115_cast = mul(x = zero_mean_115_cast, y = zero_mean_115_cast)[name = tensor<string, []>("zero_mean_sq_115_cast")];
            tensor<int32, [1]> var_4040 = const()[name = tensor<string, []>("op_4040"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4041_cast = reduce_mean(axes = var_4040, keep_dims = var_1181, x = zero_mean_sq_115_cast)[name = tensor<string, []>("op_4041_cast")];
            tensor<fp16, []> var_4042_to_fp16 = const()[name = tensor<string, []>("op_4042_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4043_cast = add(x = var_4041_cast, y = var_4042_to_fp16)[name = tensor<string, []>("op_4043_cast")];
            tensor<fp16, []> denom_115_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_115_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_115_cast = rsqrt(epsilon = denom_115_epsilon_0_to_fp16, x = var_4043_cast)[name = tensor<string, []>("denom_115_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_115_cast = mul(x = zero_mean_115_cast, y = denom_115_cast)[name = tensor<string, []>("out_115_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4047_to_fp16 = const()[name = tensor<string, []>("op_4047_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357764352)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4048_cast = add(x = out_115_cast, y = var_4047_to_fp16)[name = tensor<string, []>("op_4048_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4050_to_fp16 = const()[name = tensor<string, []>("op_4050_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357766976)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_167_cast = mul(x = var_4048_cast, y = var_4050_to_fp16)[name = tensor<string, []>("hidden_states_167_cast")];
            tensor<int32, [2]> var_4057 = const()[name = tensor<string, []>("op_4057"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4059 = const()[name = tensor<string, []>("op_4059"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_77_pad_type_0 = const()[name = tensor<string, []>("q_77_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_77_pad_0 = const()[name = tensor<string, []>("q_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357769600))), lut = tensor<fp16, [2]>([-0x1.538p-6, 0x1.548p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_77_cast = conv(dilations = var_4059, groups = var_1186, pad = q_77_pad_0, pad_type = q_77_pad_type_0, strides = var_4057, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor<string, []>("q_77_cast")];
            tensor<int32, [2]> var_4063 = const()[name = tensor<string, []>("op_4063"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4065 = const()[name = tensor<string, []>("op_4065"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_77_pad_type_0 = const()[name = tensor<string, []>("k_77_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_77_pad_0 = const()[name = tensor<string, []>("k_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357974464))), lut = tensor<fp16, [2]>([-0x1.528p-6, 0x1.528p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_77_cast = conv(dilations = var_4065, groups = var_1186, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_4063, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor<string, []>("k_77_cast")];
            tensor<int32, [2]> var_4069 = const()[name = tensor<string, []>("op_4069"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4071 = const()[name = tensor<string, []>("op_4071"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_77_pad_type_0 = const()[name = tensor<string, []>("v_77_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_77_pad_0 = const()[name = tensor<string, []>("v_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358179328))), lut = tensor<fp16, [4]>([-0x1.2d8p-5, -0x1.6b4p-7, 0x1.6bp-7, 0x1.2d4p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_77_cast = conv(dilations = var_4071, groups = var_1186, pad = v_77_pad_0, pad_type = v_77_pad_type_0, strides = var_4069, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor<string, []>("v_77_cast")];
            tensor<int32, [4]> var_4075 = const()[name = tensor<string, []>("op_4075"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4076_cast = reshape(shape = var_4075, x = q_77_cast)[name = tensor<string, []>("op_4076_cast")];
            tensor<int32, [4]> var_4077 = const()[name = tensor<string, []>("op_4077"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4078_cast = reshape(shape = var_4077, x = k_77_cast)[name = tensor<string, []>("op_4078_cast")];
            tensor<int32, [4]> var_4079 = const()[name = tensor<string, []>("op_4079"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4080_cast = reshape(shape = var_4079, x = v_77_cast)[name = tensor<string, []>("op_4080_cast")];
            tensor<bool, []> attn_weights_153_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_153_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_153_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_153_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_153_cast = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_4076_cast, y = var_4078_cast)[name = tensor<string, []>("attn_weights_153_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_155_cast = mul(x = attn_weights_153_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_155_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_4084_cast = softmax(axis = var_1170, x = attn_weights_155_cast)[name = tensor<string, []>("op_4084_cast")];
            tensor<bool, []> attn_77_transpose_x_0 = const()[name = tensor<string, []>("attn_77_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_77_transpose_y_0 = const()[name = tensor<string, []>("attn_77_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_77_cast = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4080_cast, y = var_4084_cast)[name = tensor<string, []>("attn_77_cast")];
            tensor<int32, [4]> var_4088 = const()[name = tensor<string, []>("op_4088"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_269_cast = reshape(shape = var_4088, x = attn_77_cast)[name = tensor<string, []>("input_269_cast")];
            tensor<int32, [2]> var_4093 = const()[name = tensor<string, []>("op_4093"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4095 = const()[name = tensor<string, []>("op_4095"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4097_pad_type_0 = const()[name = tensor<string, []>("op_4097_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4097_pad_0 = const()[name = tensor<string, []>("op_4097_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358588992))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359408256))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359408384)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4097_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_4095, groups = var_1186, pad = var_4097_pad_0, pad_type = var_4097_pad_type_0, strides = var_4093, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_269_cast)[name = tensor<string, []>("op_4097_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_117_cast = add(x = var_4097_cast, y = inputs_115_cast)[name = tensor<string, []>("inputs_117_cast")];
            tensor<int32, [1]> var_4101 = const()[name = tensor<string, []>("op_4101"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_117_cast = reduce_mean(axes = var_4101, keep_dims = var_1181, x = inputs_117_cast)[name = tensor<string, []>("channels_mean_117_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_117_cast = sub(x = inputs_117_cast, y = channels_mean_117_cast)[name = tensor<string, []>("zero_mean_117_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_117_cast = mul(x = zero_mean_117_cast, y = zero_mean_117_cast)[name = tensor<string, []>("zero_mean_sq_117_cast")];
            tensor<int32, [1]> var_4105 = const()[name = tensor<string, []>("op_4105"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4106_cast = reduce_mean(axes = var_4105, keep_dims = var_1181, x = zero_mean_sq_117_cast)[name = tensor<string, []>("op_4106_cast")];
            tensor<fp16, []> var_4107_to_fp16 = const()[name = tensor<string, []>("op_4107_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4108_cast = add(x = var_4106_cast, y = var_4107_to_fp16)[name = tensor<string, []>("op_4108_cast")];
            tensor<fp16, []> denom_117_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_117_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_117_cast = rsqrt(epsilon = denom_117_epsilon_0_to_fp16, x = var_4108_cast)[name = tensor<string, []>("denom_117_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_117_cast = mul(x = zero_mean_117_cast, y = denom_117_cast)[name = tensor<string, []>("out_117_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4112_to_fp16 = const()[name = tensor<string, []>("op_4112_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359411008)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4113_cast = add(x = out_117_cast, y = var_4112_to_fp16)[name = tensor<string, []>("op_4113_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4115_to_fp16 = const()[name = tensor<string, []>("op_4115_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359413632)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_169_cast = mul(x = var_4113_cast, y = var_4115_to_fp16)[name = tensor<string, []>("hidden_states_169_cast")];
            tensor<int32, [2]> var_4122 = const()[name = tensor<string, []>("op_4122"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4124 = const()[name = tensor<string, []>("op_4124"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_79_pad_type_0 = const()[name = tensor<string, []>("q_79_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_79_pad_0 = const()[name = tensor<string, []>("q_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359416256))), lut = tensor<fp16, [2]>([-0x1.0fcp-6, 0x1.0fp-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_79_cast = conv(dilations = var_4124, groups = var_1186, pad = q_79_pad_0, pad_type = q_79_pad_type_0, strides = var_4122, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_169_cast)[name = tensor<string, []>("q_79_cast")];
            tensor<int32, [2]> var_4128 = const()[name = tensor<string, []>("op_4128"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4130 = const()[name = tensor<string, []>("op_4130"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_79_pad_type_0 = const()[name = tensor<string, []>("k_79_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_79_pad_0 = const()[name = tensor<string, []>("k_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359621120))), lut = tensor<fp16, [2]>([-0x1.b3p-7, 0x1.b38p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_79_cast = conv(dilations = var_4130, groups = var_1186, pad = k_79_pad_0, pad_type = k_79_pad_type_0, strides = var_4128, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_79_cast")];
            tensor<int32, [2]> var_4134 = const()[name = tensor<string, []>("op_4134"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4136 = const()[name = tensor<string, []>("op_4136"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_79_pad_type_0 = const()[name = tensor<string, []>("v_79_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_79_pad_0 = const()[name = tensor<string, []>("v_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359948864))), lut = tensor<fp16, [2]>([-0x1.12cp-6, 0x1.12p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_79_cast = conv(dilations = var_4136, groups = var_1186, pad = v_79_pad_0, pad_type = v_79_pad_type_0, strides = var_4134, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_79_cast")];
            tensor<int32, [4]> var_4140 = const()[name = tensor<string, []>("op_4140"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4141_cast = reshape(shape = var_4140, x = q_79_cast)[name = tensor<string, []>("op_4141_cast")];
            tensor<int32, [4]> var_4142 = const()[name = tensor<string, []>("op_4142"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4143_cast = reshape(shape = var_4142, x = k_79_cast)[name = tensor<string, []>("op_4143_cast")];
            tensor<int32, [4]> var_4144 = const()[name = tensor<string, []>("op_4144"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4145_cast = reshape(shape = var_4144, x = v_79_cast)[name = tensor<string, []>("op_4145_cast")];
            tensor<bool, []> attn_weights_157_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_157_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_157_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_157_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_157_cast = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = var_4141_cast, y = var_4143_cast)[name = tensor<string, []>("attn_weights_157_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_159_cast = mul(x = attn_weights_157_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_159_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_4149_cast = softmax(axis = var_1170, x = attn_weights_159_cast)[name = tensor<string, []>("op_4149_cast")];
            tensor<bool, []> attn_79_transpose_x_0 = const()[name = tensor<string, []>("attn_79_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_79_transpose_y_0 = const()[name = tensor<string, []>("attn_79_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_79_cast = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4145_cast, y = var_4149_cast)[name = tensor<string, []>("attn_79_cast")];
            tensor<int32, [4]> var_4153 = const()[name = tensor<string, []>("op_4153"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_271_cast = reshape(shape = var_4153, x = attn_79_cast)[name = tensor<string, []>("input_271_cast")];
            tensor<int32, [2]> var_4158 = const()[name = tensor<string, []>("op_4158"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4160 = const()[name = tensor<string, []>("op_4160"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4162_pad_type_0 = const()[name = tensor<string, []>("op_4162_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4162_pad_0 = const()[name = tensor<string, []>("op_4162_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360276608))), lut = tensor<fp16, [2]>([-0x1.578p-7, 0x1.57cp-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360481472)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4162_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_4160, groups = var_1186, pad = var_4162_pad_0, pad_type = var_4162_pad_type_0, strides = var_4158, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_271_cast)[name = tensor<string, []>("op_4162_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_119_cast = add(x = var_4162_cast, y = inputs_117_cast)[name = tensor<string, []>("inputs_119_cast")];
            tensor<int32, [1]> var_4166 = const()[name = tensor<string, []>("op_4166"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_119_cast = reduce_mean(axes = var_4166, keep_dims = var_1181, x = inputs_119_cast)[name = tensor<string, []>("channels_mean_119_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_119_cast = sub(x = inputs_119_cast, y = channels_mean_119_cast)[name = tensor<string, []>("zero_mean_119_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_119_cast = mul(x = zero_mean_119_cast, y = zero_mean_119_cast)[name = tensor<string, []>("zero_mean_sq_119_cast")];
            tensor<int32, [1]> var_4170 = const()[name = tensor<string, []>("op_4170"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4171_cast = reduce_mean(axes = var_4170, keep_dims = var_1181, x = zero_mean_sq_119_cast)[name = tensor<string, []>("op_4171_cast")];
            tensor<fp16, []> var_4172_to_fp16 = const()[name = tensor<string, []>("op_4172_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4173_cast = add(x = var_4171_cast, y = var_4172_to_fp16)[name = tensor<string, []>("op_4173_cast")];
            tensor<fp16, []> denom_119_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_119_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_119_cast = rsqrt(epsilon = denom_119_epsilon_0_to_fp16, x = var_4173_cast)[name = tensor<string, []>("denom_119_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_119_cast = mul(x = zero_mean_119_cast, y = denom_119_cast)[name = tensor<string, []>("out_119_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4177_to_fp16 = const()[name = tensor<string, []>("op_4177_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360484096)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4178_cast = add(x = out_119_cast, y = var_4177_to_fp16)[name = tensor<string, []>("op_4178_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4180_to_fp16 = const()[name = tensor<string, []>("op_4180_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360486720)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_273_cast = mul(x = var_4178_cast, y = var_4180_to_fp16)[name = tensor<string, []>("input_273_cast")];
            tensor<int32, [2]> var_4188 = const()[name = tensor<string, []>("op_4188"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4190 = const()[name = tensor<string, []>("op_4190"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4192_pad_type_0 = const()[name = tensor<string, []>("op_4192_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4192_pad_0 = const()[name = tensor<string, []>("op_4192_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360489344))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367043008))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367043136)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_4192_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_4190, groups = var_1186, pad = var_4192_pad_0, pad_type = var_4192_pad_type_0, strides = var_4188, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_273_cast)[name = tensor<string, []>("op_4192_cast")];
            tensor<int32, [2]> var_4193_split_sizes_0 = const()[name = tensor<string, []>("op_4193_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_4193_axis_0 = const()[name = tensor<string, []>("op_4193_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_4193_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_4193_cast_1 = split(axis = var_4193_axis_0, split_sizes = var_4193_split_sizes_0, x = var_4192_cast)[name = tensor<string, []>("op_4193_cast")];
            tensor<string, []> var_4195_mode_0 = const()[name = tensor<string, []>("op_4195_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_4195_cast = gelu(mode = var_4195_mode_0, x = var_4193_cast_1)[name = tensor<string, []>("op_4195_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_275_cast = mul(x = var_4193_cast_0, y = var_4195_cast)[name = tensor<string, []>("input_275_cast")];
            tensor<int32, [2]> var_4199 = const()[name = tensor<string, []>("op_4199"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4201 = const()[name = tensor<string, []>("op_4201"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4203_pad_type_0 = const()[name = tensor<string, []>("op_4203_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4203_pad_0 = const()[name = tensor<string, []>("op_4203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367063680))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370340544))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370340672)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4203_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_4201, groups = var_1186, pad = var_4203_pad_0, pad_type = var_4203_pad_type_0, strides = var_4199, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_275_cast)[name = tensor<string, []>("op_4203_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_121_cast = add(x = var_4203_cast, y = inputs_119_cast)[name = tensor<string, []>("inputs_121_cast")];
            tensor<int32, [1]> var_4213 = const()[name = tensor<string, []>("op_4213"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_121_cast = reduce_mean(axes = var_4213, keep_dims = var_1181, x = inputs_121_cast)[name = tensor<string, []>("channels_mean_121_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_121_cast = sub(x = inputs_121_cast, y = channels_mean_121_cast)[name = tensor<string, []>("zero_mean_121_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_121_cast = mul(x = zero_mean_121_cast, y = zero_mean_121_cast)[name = tensor<string, []>("zero_mean_sq_121_cast")];
            tensor<int32, [1]> var_4217 = const()[name = tensor<string, []>("op_4217"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4218_cast = reduce_mean(axes = var_4217, keep_dims = var_1181, x = zero_mean_sq_121_cast)[name = tensor<string, []>("op_4218_cast")];
            tensor<fp16, []> var_4219_to_fp16 = const()[name = tensor<string, []>("op_4219_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4220_cast = add(x = var_4218_cast, y = var_4219_to_fp16)[name = tensor<string, []>("op_4220_cast")];
            tensor<fp16, []> denom_121_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_121_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_121_cast = rsqrt(epsilon = denom_121_epsilon_0_to_fp16, x = var_4220_cast)[name = tensor<string, []>("denom_121_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_121_cast = mul(x = zero_mean_121_cast, y = denom_121_cast)[name = tensor<string, []>("out_121_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4224_to_fp16 = const()[name = tensor<string, []>("op_4224_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370343296)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4225_cast = add(x = out_121_cast, y = var_4224_to_fp16)[name = tensor<string, []>("op_4225_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4227_to_fp16 = const()[name = tensor<string, []>("op_4227_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370345920)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_173_cast = mul(x = var_4225_cast, y = var_4227_to_fp16)[name = tensor<string, []>("hidden_states_173_cast")];
            tensor<int32, [2]> var_4234 = const()[name = tensor<string, []>("op_4234"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4236 = const()[name = tensor<string, []>("op_4236"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_81_pad_type_0 = const()[name = tensor<string, []>("q_81_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_81_pad_0 = const()[name = tensor<string, []>("q_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370348544))), lut = tensor<fp16, [2]>([-0x1.50cp-6, 0x1.51p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_81_cast = conv(dilations = var_4236, groups = var_1186, pad = q_81_pad_0, pad_type = q_81_pad_type_0, strides = var_4234, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_173_cast)[name = tensor<string, []>("q_81_cast")];
            tensor<int32, [2]> var_4240 = const()[name = tensor<string, []>("op_4240"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4242 = const()[name = tensor<string, []>("op_4242"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_81_pad_type_0 = const()[name = tensor<string, []>("k_81_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_81_pad_0 = const()[name = tensor<string, []>("k_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370553408))), lut = tensor<fp16, [2]>([-0x1.4fp-6, 0x1.4f8p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_81_cast = conv(dilations = var_4242, groups = var_1186, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_4240, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_173_cast)[name = tensor<string, []>("k_81_cast")];
            tensor<int32, [2]> var_4246 = const()[name = tensor<string, []>("op_4246"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4248 = const()[name = tensor<string, []>("op_4248"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_81_pad_type_0 = const()[name = tensor<string, []>("v_81_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_81_pad_0 = const()[name = tensor<string, []>("v_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370758272))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371577536))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_81_cast = conv(dilations = var_4248, groups = var_1186, pad = v_81_pad_0, pad_type = v_81_pad_type_0, strides = var_4246, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_173_cast)[name = tensor<string, []>("v_81_cast")];
            tensor<int32, [4]> var_4252 = const()[name = tensor<string, []>("op_4252"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4253_cast = reshape(shape = var_4252, x = q_81_cast)[name = tensor<string, []>("op_4253_cast")];
            tensor<int32, [4]> var_4254 = const()[name = tensor<string, []>("op_4254"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4255_cast = reshape(shape = var_4254, x = k_81_cast)[name = tensor<string, []>("op_4255_cast")];
            tensor<int32, [4]> var_4256 = const()[name = tensor<string, []>("op_4256"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4257_cast = reshape(shape = var_4256, x = v_81_cast)[name = tensor<string, []>("op_4257_cast")];
            tensor<bool, []> attn_weights_161_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_161_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_161_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_161_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_161_cast = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4253_cast, y = var_4255_cast)[name = tensor<string, []>("attn_weights_161_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_163_cast = mul(x = attn_weights_161_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_163_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_4261_cast = softmax(axis = var_1170, x = attn_weights_163_cast)[name = tensor<string, []>("op_4261_cast")];
            tensor<bool, []> attn_81_transpose_x_0 = const()[name = tensor<string, []>("attn_81_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_81_transpose_y_0 = const()[name = tensor<string, []>("attn_81_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_81_cast = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4257_cast, y = var_4261_cast)[name = tensor<string, []>("attn_81_cast")];
            tensor<int32, [4]> var_4265 = const()[name = tensor<string, []>("op_4265"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_277_cast = reshape(shape = var_4265, x = attn_81_cast)[name = tensor<string, []>("input_277_cast")];
            tensor<int32, [2]> var_4270 = const()[name = tensor<string, []>("op_4270"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4272 = const()[name = tensor<string, []>("op_4272"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4274_pad_type_0 = const()[name = tensor<string, []>("op_4274_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4274_pad_0 = const()[name = tensor<string, []>("op_4274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371577664))), lut = tensor<fp16, [4]>([-0x1.2e4p-5, -0x1.6bp-7, 0x1.6acp-7, 0x1.2ep-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371987328)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4274_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_4272, groups = var_1186, pad = var_4274_pad_0, pad_type = var_4274_pad_type_0, strides = var_4270, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_277_cast)[name = tensor<string, []>("op_4274_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_123_cast = add(x = var_4274_cast, y = inputs_121_cast)[name = tensor<string, []>("inputs_123_cast")];
            tensor<int32, [1]> var_4278 = const()[name = tensor<string, []>("op_4278"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_123_cast = reduce_mean(axes = var_4278, keep_dims = var_1181, x = inputs_123_cast)[name = tensor<string, []>("channels_mean_123_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_123_cast = sub(x = inputs_123_cast, y = channels_mean_123_cast)[name = tensor<string, []>("zero_mean_123_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_123_cast = mul(x = zero_mean_123_cast, y = zero_mean_123_cast)[name = tensor<string, []>("zero_mean_sq_123_cast")];
            tensor<int32, [1]> var_4282 = const()[name = tensor<string, []>("op_4282"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4283_cast = reduce_mean(axes = var_4282, keep_dims = var_1181, x = zero_mean_sq_123_cast)[name = tensor<string, []>("op_4283_cast")];
            tensor<fp16, []> var_4284_to_fp16 = const()[name = tensor<string, []>("op_4284_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4285_cast = add(x = var_4283_cast, y = var_4284_to_fp16)[name = tensor<string, []>("op_4285_cast")];
            tensor<fp16, []> denom_123_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_123_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_123_cast = rsqrt(epsilon = denom_123_epsilon_0_to_fp16, x = var_4285_cast)[name = tensor<string, []>("denom_123_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_123_cast = mul(x = zero_mean_123_cast, y = denom_123_cast)[name = tensor<string, []>("out_123_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4289_to_fp16 = const()[name = tensor<string, []>("op_4289_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371989952)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4290_cast = add(x = out_123_cast, y = var_4289_to_fp16)[name = tensor<string, []>("op_4290_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4292_to_fp16 = const()[name = tensor<string, []>("op_4292_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371992576)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_175_cast = mul(x = var_4290_cast, y = var_4292_to_fp16)[name = tensor<string, []>("hidden_states_175_cast")];
            tensor<int32, [2]> var_4299 = const()[name = tensor<string, []>("op_4299"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4301 = const()[name = tensor<string, []>("op_4301"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_83_pad_type_0 = const()[name = tensor<string, []>("q_83_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_83_pad_0 = const()[name = tensor<string, []>("q_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371995200))), lut = tensor<fp16, [2]>([-0x1.e5p-7, 0x1.e64p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_83_cast = conv(dilations = var_4301, groups = var_1186, pad = q_83_pad_0, pad_type = q_83_pad_type_0, strides = var_4299, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_175_cast)[name = tensor<string, []>("q_83_cast")];
            tensor<int32, [2]> var_4305 = const()[name = tensor<string, []>("op_4305"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4307 = const()[name = tensor<string, []>("op_4307"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_83_pad_type_0 = const()[name = tensor<string, []>("k_83_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_83_pad_0 = const()[name = tensor<string, []>("k_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372200064))), lut = tensor<fp16, [2]>([-0x1.798p-7, 0x1.78p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_83_cast = conv(dilations = var_4307, groups = var_1186, pad = k_83_pad_0, pad_type = k_83_pad_type_0, strides = var_4305, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_83_cast")];
            tensor<int32, [2]> var_4311 = const()[name = tensor<string, []>("op_4311"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4313 = const()[name = tensor<string, []>("op_4313"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_83_pad_type_0 = const()[name = tensor<string, []>("v_83_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_83_pad_0 = const()[name = tensor<string, []>("v_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372527808))), lut = tensor<fp16, [2]>([-0x1.fb4p-7, 0x1.fap-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_83_cast = conv(dilations = var_4313, groups = var_1186, pad = v_83_pad_0, pad_type = v_83_pad_type_0, strides = var_4311, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_83_cast")];
            tensor<int32, [4]> var_4317 = const()[name = tensor<string, []>("op_4317"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4318_cast = reshape(shape = var_4317, x = q_83_cast)[name = tensor<string, []>("op_4318_cast")];
            tensor<int32, [4]> var_4319 = const()[name = tensor<string, []>("op_4319"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4320_cast = reshape(shape = var_4319, x = k_83_cast)[name = tensor<string, []>("op_4320_cast")];
            tensor<int32, [4]> var_4321 = const()[name = tensor<string, []>("op_4321"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4322_cast = reshape(shape = var_4321, x = v_83_cast)[name = tensor<string, []>("op_4322_cast")];
            tensor<bool, []> attn_weights_165_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_165_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_165_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_165_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_165_cast = matmul(transpose_x = attn_weights_165_transpose_x_0, transpose_y = attn_weights_165_transpose_y_0, x = var_4318_cast, y = var_4320_cast)[name = tensor<string, []>("attn_weights_165_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_167_cast = mul(x = attn_weights_165_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_167_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_4326_cast = softmax(axis = var_1170, x = attn_weights_167_cast)[name = tensor<string, []>("op_4326_cast")];
            tensor<bool, []> attn_83_transpose_x_0 = const()[name = tensor<string, []>("attn_83_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_83_transpose_y_0 = const()[name = tensor<string, []>("attn_83_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_83_cast = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4322_cast, y = var_4326_cast)[name = tensor<string, []>("attn_83_cast")];
            tensor<int32, [4]> var_4330 = const()[name = tensor<string, []>("op_4330"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_279_cast = reshape(shape = var_4330, x = attn_83_cast)[name = tensor<string, []>("input_279_cast")];
            tensor<int32, [2]> var_4335 = const()[name = tensor<string, []>("op_4335"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4337 = const()[name = tensor<string, []>("op_4337"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4339_pad_type_0 = const()[name = tensor<string, []>("op_4339_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4339_pad_0 = const()[name = tensor<string, []>("op_4339_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372855552))), lut = tensor<fp16, [2]>([-0x1.3e8p-7, 0x1.3f4p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(373060416)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4339_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_4337, groups = var_1186, pad = var_4339_pad_0, pad_type = var_4339_pad_type_0, strides = var_4335, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_279_cast)[name = tensor<string, []>("op_4339_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_125_cast = add(x = var_4339_cast, y = inputs_123_cast)[name = tensor<string, []>("inputs_125_cast")];
            tensor<int32, [1]> var_4343 = const()[name = tensor<string, []>("op_4343"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_125_cast = reduce_mean(axes = var_4343, keep_dims = var_1181, x = inputs_125_cast)[name = tensor<string, []>("channels_mean_125_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_125_cast = sub(x = inputs_125_cast, y = channels_mean_125_cast)[name = tensor<string, []>("zero_mean_125_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_125_cast = mul(x = zero_mean_125_cast, y = zero_mean_125_cast)[name = tensor<string, []>("zero_mean_sq_125_cast")];
            tensor<int32, [1]> var_4347 = const()[name = tensor<string, []>("op_4347"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4348_cast = reduce_mean(axes = var_4347, keep_dims = var_1181, x = zero_mean_sq_125_cast)[name = tensor<string, []>("op_4348_cast")];
            tensor<fp16, []> var_4349_to_fp16 = const()[name = tensor<string, []>("op_4349_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4350_cast = add(x = var_4348_cast, y = var_4349_to_fp16)[name = tensor<string, []>("op_4350_cast")];
            tensor<fp16, []> denom_125_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_125_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_125_cast = rsqrt(epsilon = denom_125_epsilon_0_to_fp16, x = var_4350_cast)[name = tensor<string, []>("denom_125_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_125_cast = mul(x = zero_mean_125_cast, y = denom_125_cast)[name = tensor<string, []>("out_125_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4354_to_fp16 = const()[name = tensor<string, []>("op_4354_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(373063040)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4355_cast = add(x = out_125_cast, y = var_4354_to_fp16)[name = tensor<string, []>("op_4355_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4357_to_fp16 = const()[name = tensor<string, []>("op_4357_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(373065664)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_281_cast = mul(x = var_4355_cast, y = var_4357_to_fp16)[name = tensor<string, []>("input_281_cast")];
            tensor<int32, [2]> var_4365 = const()[name = tensor<string, []>("op_4365"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4367 = const()[name = tensor<string, []>("op_4367"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4369_pad_type_0 = const()[name = tensor<string, []>("op_4369_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4369_pad_0 = const()[name = tensor<string, []>("op_4369_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(373068288))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379621952))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379622080)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_4369_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_4367, groups = var_1186, pad = var_4369_pad_0, pad_type = var_4369_pad_type_0, strides = var_4365, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_281_cast)[name = tensor<string, []>("op_4369_cast")];
            tensor<int32, [2]> var_4370_split_sizes_0 = const()[name = tensor<string, []>("op_4370_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_4370_axis_0 = const()[name = tensor<string, []>("op_4370_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_4370_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_4370_cast_1 = split(axis = var_4370_axis_0, split_sizes = var_4370_split_sizes_0, x = var_4369_cast)[name = tensor<string, []>("op_4370_cast")];
            tensor<string, []> var_4372_mode_0 = const()[name = tensor<string, []>("op_4372_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_4372_cast = gelu(mode = var_4372_mode_0, x = var_4370_cast_1)[name = tensor<string, []>("op_4372_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_283_cast = mul(x = var_4370_cast_0, y = var_4372_cast)[name = tensor<string, []>("input_283_cast")];
            tensor<int32, [2]> var_4376 = const()[name = tensor<string, []>("op_4376"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4378 = const()[name = tensor<string, []>("op_4378"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4380_pad_type_0 = const()[name = tensor<string, []>("op_4380_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4380_pad_0 = const()[name = tensor<string, []>("op_4380_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379642624))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382919488))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382919616)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4380_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_4378, groups = var_1186, pad = var_4380_pad_0, pad_type = var_4380_pad_type_0, strides = var_4376, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_283_cast)[name = tensor<string, []>("op_4380_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_127_cast = add(x = var_4380_cast, y = inputs_125_cast)[name = tensor<string, []>("inputs_127_cast")];
            tensor<int32, [1]> var_4390 = const()[name = tensor<string, []>("op_4390"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_127_cast = reduce_mean(axes = var_4390, keep_dims = var_1181, x = inputs_127_cast)[name = tensor<string, []>("channels_mean_127_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_127_cast = sub(x = inputs_127_cast, y = channels_mean_127_cast)[name = tensor<string, []>("zero_mean_127_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_127_cast = mul(x = zero_mean_127_cast, y = zero_mean_127_cast)[name = tensor<string, []>("zero_mean_sq_127_cast")];
            tensor<int32, [1]> var_4394 = const()[name = tensor<string, []>("op_4394"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4395_cast = reduce_mean(axes = var_4394, keep_dims = var_1181, x = zero_mean_sq_127_cast)[name = tensor<string, []>("op_4395_cast")];
            tensor<fp16, []> var_4396_to_fp16 = const()[name = tensor<string, []>("op_4396_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4397_cast = add(x = var_4395_cast, y = var_4396_to_fp16)[name = tensor<string, []>("op_4397_cast")];
            tensor<fp16, []> denom_127_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_127_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_127_cast = rsqrt(epsilon = denom_127_epsilon_0_to_fp16, x = var_4397_cast)[name = tensor<string, []>("denom_127_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_127_cast = mul(x = zero_mean_127_cast, y = denom_127_cast)[name = tensor<string, []>("out_127_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4401_to_fp16 = const()[name = tensor<string, []>("op_4401_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382922240)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4402_cast = add(x = out_127_cast, y = var_4401_to_fp16)[name = tensor<string, []>("op_4402_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4404_to_fp16 = const()[name = tensor<string, []>("op_4404_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382924864)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_179_cast = mul(x = var_4402_cast, y = var_4404_to_fp16)[name = tensor<string, []>("hidden_states_179_cast")];
            tensor<int32, [2]> var_4411 = const()[name = tensor<string, []>("op_4411"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4413 = const()[name = tensor<string, []>("op_4413"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_85_pad_type_0 = const()[name = tensor<string, []>("q_85_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_85_pad_0 = const()[name = tensor<string, []>("q_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382927488))), lut = tensor<fp16, [2]>([-0x1.50cp-6, 0x1.52p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_85_cast = conv(dilations = var_4413, groups = var_1186, pad = q_85_pad_0, pad_type = q_85_pad_type_0, strides = var_4411, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_179_cast)[name = tensor<string, []>("q_85_cast")];
            tensor<int32, [2]> var_4417 = const()[name = tensor<string, []>("op_4417"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4419 = const()[name = tensor<string, []>("op_4419"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_85_pad_type_0 = const()[name = tensor<string, []>("k_85_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_85_pad_0 = const()[name = tensor<string, []>("k_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383132352))), lut = tensor<fp16, [2]>([-0x1.4f4p-6, 0x1.4ecp-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_85_cast = conv(dilations = var_4419, groups = var_1186, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_4417, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_179_cast)[name = tensor<string, []>("k_85_cast")];
            tensor<int32, [2]> var_4423 = const()[name = tensor<string, []>("op_4423"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4425 = const()[name = tensor<string, []>("op_4425"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_85_pad_type_0 = const()[name = tensor<string, []>("v_85_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_85_pad_0 = const()[name = tensor<string, []>("v_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383337216))), lut = tensor<fp16, [4]>([-0x1.37p-5, -0x1.75cp-7, 0x1.794p-7, 0x1.374p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_85_cast = conv(dilations = var_4425, groups = var_1186, pad = v_85_pad_0, pad_type = v_85_pad_type_0, strides = var_4423, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_179_cast)[name = tensor<string, []>("v_85_cast")];
            tensor<int32, [4]> var_4429 = const()[name = tensor<string, []>("op_4429"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4430_cast = reshape(shape = var_4429, x = q_85_cast)[name = tensor<string, []>("op_4430_cast")];
            tensor<int32, [4]> var_4431 = const()[name = tensor<string, []>("op_4431"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4432_cast = reshape(shape = var_4431, x = k_85_cast)[name = tensor<string, []>("op_4432_cast")];
            tensor<int32, [4]> var_4433 = const()[name = tensor<string, []>("op_4433"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4434_cast = reshape(shape = var_4433, x = v_85_cast)[name = tensor<string, []>("op_4434_cast")];
            tensor<bool, []> attn_weights_169_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_169_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_169_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_169_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_169_cast = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4430_cast, y = var_4432_cast)[name = tensor<string, []>("attn_weights_169_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_171_cast = mul(x = attn_weights_169_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_171_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_4438_cast = softmax(axis = var_1170, x = attn_weights_171_cast)[name = tensor<string, []>("op_4438_cast")];
            tensor<bool, []> attn_85_transpose_x_0 = const()[name = tensor<string, []>("attn_85_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_85_transpose_y_0 = const()[name = tensor<string, []>("attn_85_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_85_cast = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4434_cast, y = var_4438_cast)[name = tensor<string, []>("attn_85_cast")];
            tensor<int32, [4]> var_4442 = const()[name = tensor<string, []>("op_4442"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_285_cast = reshape(shape = var_4442, x = attn_85_cast)[name = tensor<string, []>("input_285_cast")];
            tensor<int32, [2]> var_4447 = const()[name = tensor<string, []>("op_4447"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4449 = const()[name = tensor<string, []>("op_4449"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4451_pad_type_0 = const()[name = tensor<string, []>("op_4451_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4451_pad_0 = const()[name = tensor<string, []>("op_4451_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383746880))), lut = tensor<fp16, [4]>([-0x1.31cp-5, -0x1.6f4p-7, 0x1.704p-7, 0x1.31cp-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384156544)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4451_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_4449, groups = var_1186, pad = var_4451_pad_0, pad_type = var_4451_pad_type_0, strides = var_4447, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_285_cast)[name = tensor<string, []>("op_4451_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_129_cast = add(x = var_4451_cast, y = inputs_127_cast)[name = tensor<string, []>("inputs_129_cast")];
            tensor<int32, [1]> var_4455 = const()[name = tensor<string, []>("op_4455"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_129_cast = reduce_mean(axes = var_4455, keep_dims = var_1181, x = inputs_129_cast)[name = tensor<string, []>("channels_mean_129_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_129_cast = sub(x = inputs_129_cast, y = channels_mean_129_cast)[name = tensor<string, []>("zero_mean_129_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_129_cast = mul(x = zero_mean_129_cast, y = zero_mean_129_cast)[name = tensor<string, []>("zero_mean_sq_129_cast")];
            tensor<int32, [1]> var_4459 = const()[name = tensor<string, []>("op_4459"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4460_cast = reduce_mean(axes = var_4459, keep_dims = var_1181, x = zero_mean_sq_129_cast)[name = tensor<string, []>("op_4460_cast")];
            tensor<fp16, []> var_4461_to_fp16 = const()[name = tensor<string, []>("op_4461_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4462_cast = add(x = var_4460_cast, y = var_4461_to_fp16)[name = tensor<string, []>("op_4462_cast")];
            tensor<fp16, []> denom_129_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_129_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_129_cast = rsqrt(epsilon = denom_129_epsilon_0_to_fp16, x = var_4462_cast)[name = tensor<string, []>("denom_129_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_129_cast = mul(x = zero_mean_129_cast, y = denom_129_cast)[name = tensor<string, []>("out_129_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4466_to_fp16 = const()[name = tensor<string, []>("op_4466_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384159168)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4467_cast = add(x = out_129_cast, y = var_4466_to_fp16)[name = tensor<string, []>("op_4467_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4469_to_fp16 = const()[name = tensor<string, []>("op_4469_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384161792)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_181_cast = mul(x = var_4467_cast, y = var_4469_to_fp16)[name = tensor<string, []>("hidden_states_181_cast")];
            tensor<int32, [2]> var_4476 = const()[name = tensor<string, []>("op_4476"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4478 = const()[name = tensor<string, []>("op_4478"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_87_pad_type_0 = const()[name = tensor<string, []>("q_87_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_87_pad_0 = const()[name = tensor<string, []>("q_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384164416))), lut = tensor<fp16, [2]>([-0x1.d38p-7, 0x1.d44p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_87_cast = conv(dilations = var_4478, groups = var_1186, pad = q_87_pad_0, pad_type = q_87_pad_type_0, strides = var_4476, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_181_cast)[name = tensor<string, []>("q_87_cast")];
            tensor<int32, [2]> var_4482 = const()[name = tensor<string, []>("op_4482"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4484 = const()[name = tensor<string, []>("op_4484"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_87_pad_type_0 = const()[name = tensor<string, []>("k_87_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_87_pad_0 = const()[name = tensor<string, []>("k_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384369280))), lut = tensor<fp16, [2]>([-0x1.648p-7, 0x1.62cp-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_87_cast = conv(dilations = var_4484, groups = var_1186, pad = k_87_pad_0, pad_type = k_87_pad_type_0, strides = var_4482, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_87_cast")];
            tensor<int32, [2]> var_4488 = const()[name = tensor<string, []>("op_4488"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4490 = const()[name = tensor<string, []>("op_4490"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_87_pad_type_0 = const()[name = tensor<string, []>("v_87_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_87_pad_0 = const()[name = tensor<string, []>("v_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384697024))), lut = tensor<fp16, [2]>([-0x1.e88p-7, 0x1.e78p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_87_cast = conv(dilations = var_4490, groups = var_1186, pad = v_87_pad_0, pad_type = v_87_pad_type_0, strides = var_4488, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_87_cast")];
            tensor<int32, [4]> var_4494 = const()[name = tensor<string, []>("op_4494"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4495_cast = reshape(shape = var_4494, x = q_87_cast)[name = tensor<string, []>("op_4495_cast")];
            tensor<int32, [4]> var_4496 = const()[name = tensor<string, []>("op_4496"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4497_cast = reshape(shape = var_4496, x = k_87_cast)[name = tensor<string, []>("op_4497_cast")];
            tensor<int32, [4]> var_4498 = const()[name = tensor<string, []>("op_4498"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4499_cast = reshape(shape = var_4498, x = v_87_cast)[name = tensor<string, []>("op_4499_cast")];
            tensor<bool, []> attn_weights_173_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_173_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_173_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_173_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_173_cast = matmul(transpose_x = attn_weights_173_transpose_x_0, transpose_y = attn_weights_173_transpose_y_0, x = var_4495_cast, y = var_4497_cast)[name = tensor<string, []>("attn_weights_173_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_175_cast = mul(x = attn_weights_173_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_175_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_4503_cast = softmax(axis = var_1170, x = attn_weights_175_cast)[name = tensor<string, []>("op_4503_cast")];
            tensor<bool, []> attn_87_transpose_x_0 = const()[name = tensor<string, []>("attn_87_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_87_transpose_y_0 = const()[name = tensor<string, []>("attn_87_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_87_cast = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_4499_cast, y = var_4503_cast)[name = tensor<string, []>("attn_87_cast")];
            tensor<int32, [4]> var_4507 = const()[name = tensor<string, []>("op_4507"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_287_cast = reshape(shape = var_4507, x = attn_87_cast)[name = tensor<string, []>("input_287_cast")];
            tensor<int32, [2]> var_4512 = const()[name = tensor<string, []>("op_4512"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4514 = const()[name = tensor<string, []>("op_4514"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4516_pad_type_0 = const()[name = tensor<string, []>("op_4516_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4516_pad_0 = const()[name = tensor<string, []>("op_4516_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385024768))), lut = tensor<fp16, [2]>([-0x1.354p-7, 0x1.37p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385229632)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4516_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_4514, groups = var_1186, pad = var_4516_pad_0, pad_type = var_4516_pad_type_0, strides = var_4512, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_287_cast)[name = tensor<string, []>("op_4516_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_131_cast = add(x = var_4516_cast, y = inputs_129_cast)[name = tensor<string, []>("inputs_131_cast")];
            tensor<int32, [1]> var_4520 = const()[name = tensor<string, []>("op_4520"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_131_cast = reduce_mean(axes = var_4520, keep_dims = var_1181, x = inputs_131_cast)[name = tensor<string, []>("channels_mean_131_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_131_cast = sub(x = inputs_131_cast, y = channels_mean_131_cast)[name = tensor<string, []>("zero_mean_131_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_131_cast = mul(x = zero_mean_131_cast, y = zero_mean_131_cast)[name = tensor<string, []>("zero_mean_sq_131_cast")];
            tensor<int32, [1]> var_4524 = const()[name = tensor<string, []>("op_4524"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4525_cast = reduce_mean(axes = var_4524, keep_dims = var_1181, x = zero_mean_sq_131_cast)[name = tensor<string, []>("op_4525_cast")];
            tensor<fp16, []> var_4526_to_fp16 = const()[name = tensor<string, []>("op_4526_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4527_cast = add(x = var_4525_cast, y = var_4526_to_fp16)[name = tensor<string, []>("op_4527_cast")];
            tensor<fp16, []> denom_131_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_131_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_131_cast = rsqrt(epsilon = denom_131_epsilon_0_to_fp16, x = var_4527_cast)[name = tensor<string, []>("denom_131_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_131_cast = mul(x = zero_mean_131_cast, y = denom_131_cast)[name = tensor<string, []>("out_131_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4531_to_fp16 = const()[name = tensor<string, []>("op_4531_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385232256)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4532_cast = add(x = out_131_cast, y = var_4531_to_fp16)[name = tensor<string, []>("op_4532_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4534_to_fp16 = const()[name = tensor<string, []>("op_4534_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385234880)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_289_cast = mul(x = var_4532_cast, y = var_4534_to_fp16)[name = tensor<string, []>("input_289_cast")];
            tensor<int32, [2]> var_4542 = const()[name = tensor<string, []>("op_4542"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4544 = const()[name = tensor<string, []>("op_4544"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4546_pad_type_0 = const()[name = tensor<string, []>("op_4546_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4546_pad_0 = const()[name = tensor<string, []>("op_4546_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385237504))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(391791168))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(391791296)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_4546_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_4544, groups = var_1186, pad = var_4546_pad_0, pad_type = var_4546_pad_type_0, strides = var_4542, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_289_cast)[name = tensor<string, []>("op_4546_cast")];
            tensor<int32, [2]> var_4547_split_sizes_0 = const()[name = tensor<string, []>("op_4547_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_4547_axis_0 = const()[name = tensor<string, []>("op_4547_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_4547_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_4547_cast_1 = split(axis = var_4547_axis_0, split_sizes = var_4547_split_sizes_0, x = var_4546_cast)[name = tensor<string, []>("op_4547_cast")];
            tensor<string, []> var_4549_mode_0 = const()[name = tensor<string, []>("op_4549_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_4549_cast = gelu(mode = var_4549_mode_0, x = var_4547_cast_1)[name = tensor<string, []>("op_4549_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_291_cast = mul(x = var_4547_cast_0, y = var_4549_cast)[name = tensor<string, []>("input_291_cast")];
            tensor<int32, [2]> var_4553 = const()[name = tensor<string, []>("op_4553"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4555 = const()[name = tensor<string, []>("op_4555"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4557_pad_type_0 = const()[name = tensor<string, []>("op_4557_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4557_pad_0 = const()[name = tensor<string, []>("op_4557_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(391811840))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395088704))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395088832)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4557_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_4555, groups = var_1186, pad = var_4557_pad_0, pad_type = var_4557_pad_type_0, strides = var_4553, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_291_cast)[name = tensor<string, []>("op_4557_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_133_cast = add(x = var_4557_cast, y = inputs_131_cast)[name = tensor<string, []>("inputs_133_cast")];
            tensor<int32, [1]> var_4567 = const()[name = tensor<string, []>("op_4567"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_133_cast = reduce_mean(axes = var_4567, keep_dims = var_1181, x = inputs_133_cast)[name = tensor<string, []>("channels_mean_133_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_133_cast = sub(x = inputs_133_cast, y = channels_mean_133_cast)[name = tensor<string, []>("zero_mean_133_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_133_cast = mul(x = zero_mean_133_cast, y = zero_mean_133_cast)[name = tensor<string, []>("zero_mean_sq_133_cast")];
            tensor<int32, [1]> var_4571 = const()[name = tensor<string, []>("op_4571"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4572_cast = reduce_mean(axes = var_4571, keep_dims = var_1181, x = zero_mean_sq_133_cast)[name = tensor<string, []>("op_4572_cast")];
            tensor<fp16, []> var_4573_to_fp16 = const()[name = tensor<string, []>("op_4573_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4574_cast = add(x = var_4572_cast, y = var_4573_to_fp16)[name = tensor<string, []>("op_4574_cast")];
            tensor<fp16, []> denom_133_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_133_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_133_cast = rsqrt(epsilon = denom_133_epsilon_0_to_fp16, x = var_4574_cast)[name = tensor<string, []>("denom_133_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_133_cast = mul(x = zero_mean_133_cast, y = denom_133_cast)[name = tensor<string, []>("out_133_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4578_to_fp16 = const()[name = tensor<string, []>("op_4578_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395091456)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4579_cast = add(x = out_133_cast, y = var_4578_to_fp16)[name = tensor<string, []>("op_4579_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4581_to_fp16 = const()[name = tensor<string, []>("op_4581_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395094080)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_185_cast = mul(x = var_4579_cast, y = var_4581_to_fp16)[name = tensor<string, []>("hidden_states_185_cast")];
            tensor<int32, [2]> var_4588 = const()[name = tensor<string, []>("op_4588"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4590 = const()[name = tensor<string, []>("op_4590"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_89_pad_type_0 = const()[name = tensor<string, []>("q_89_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_89_pad_0 = const()[name = tensor<string, []>("q_89_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395096704))), lut = tensor<fp16, [2]>([-0x1.544p-6, 0x1.54p-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_89_cast = conv(dilations = var_4590, groups = var_1186, pad = q_89_pad_0, pad_type = q_89_pad_type_0, strides = var_4588, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_185_cast)[name = tensor<string, []>("q_89_cast")];
            tensor<int32, [2]> var_4594 = const()[name = tensor<string, []>("op_4594"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4596 = const()[name = tensor<string, []>("op_4596"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_89_pad_type_0 = const()[name = tensor<string, []>("k_89_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_89_pad_0 = const()[name = tensor<string, []>("k_89_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395301568))), lut = tensor<fp16, [2]>([-0x1.53p-6, 0x1.51cp-6]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_89_cast = conv(dilations = var_4596, groups = var_1186, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_4594, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_185_cast)[name = tensor<string, []>("k_89_cast")];
            tensor<int32, [2]> var_4600 = const()[name = tensor<string, []>("op_4600"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4602 = const()[name = tensor<string, []>("op_4602"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_89_pad_type_0 = const()[name = tensor<string, []>("v_89_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_89_pad_0 = const()[name = tensor<string, []>("v_89_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395506432))), lut = tensor<fp16, [4]>([-0x1.3e4p-5, -0x1.7ecp-7, 0x1.82p-7, 0x1.3f4p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_89_cast = conv(dilations = var_4602, groups = var_1186, pad = v_89_pad_0, pad_type = v_89_pad_type_0, strides = var_4600, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_185_cast)[name = tensor<string, []>("v_89_cast")];
            tensor<int32, [4]> var_4606 = const()[name = tensor<string, []>("op_4606"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4607_cast = reshape(shape = var_4606, x = q_89_cast)[name = tensor<string, []>("op_4607_cast")];
            tensor<int32, [4]> var_4608 = const()[name = tensor<string, []>("op_4608"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4609_cast = reshape(shape = var_4608, x = k_89_cast)[name = tensor<string, []>("op_4609_cast")];
            tensor<int32, [4]> var_4610 = const()[name = tensor<string, []>("op_4610"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4611_cast = reshape(shape = var_4610, x = v_89_cast)[name = tensor<string, []>("op_4611_cast")];
            tensor<bool, []> attn_weights_177_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_177_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_177_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_177_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_177_cast = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4607_cast, y = var_4609_cast)[name = tensor<string, []>("attn_weights_177_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_179_cast = mul(x = attn_weights_177_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_179_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_4615_cast = softmax(axis = var_1170, x = attn_weights_179_cast)[name = tensor<string, []>("op_4615_cast")];
            tensor<bool, []> attn_89_transpose_x_0 = const()[name = tensor<string, []>("attn_89_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_89_transpose_y_0 = const()[name = tensor<string, []>("attn_89_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_89_cast = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_4611_cast, y = var_4615_cast)[name = tensor<string, []>("attn_89_cast")];
            tensor<int32, [4]> var_4619 = const()[name = tensor<string, []>("op_4619"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_293_cast = reshape(shape = var_4619, x = attn_89_cast)[name = tensor<string, []>("input_293_cast")];
            tensor<int32, [2]> var_4624 = const()[name = tensor<string, []>("op_4624"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4626 = const()[name = tensor<string, []>("op_4626"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4628_pad_type_0 = const()[name = tensor<string, []>("op_4628_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4628_pad_0 = const()[name = tensor<string, []>("op_4628_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395916096))), lut = tensor<fp16, [4]>([-0x1.378p-5, -0x1.74p-7, 0x1.7bcp-7, 0x1.394p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396325760)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4628_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_4626, groups = var_1186, pad = var_4628_pad_0, pad_type = var_4628_pad_type_0, strides = var_4624, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_293_cast)[name = tensor<string, []>("op_4628_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_135_cast = add(x = var_4628_cast, y = inputs_133_cast)[name = tensor<string, []>("inputs_135_cast")];
            tensor<int32, [1]> var_4632 = const()[name = tensor<string, []>("op_4632"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_135_cast = reduce_mean(axes = var_4632, keep_dims = var_1181, x = inputs_135_cast)[name = tensor<string, []>("channels_mean_135_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_135_cast = sub(x = inputs_135_cast, y = channels_mean_135_cast)[name = tensor<string, []>("zero_mean_135_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_135_cast = mul(x = zero_mean_135_cast, y = zero_mean_135_cast)[name = tensor<string, []>("zero_mean_sq_135_cast")];
            tensor<int32, [1]> var_4636 = const()[name = tensor<string, []>("op_4636"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4637_cast = reduce_mean(axes = var_4636, keep_dims = var_1181, x = zero_mean_sq_135_cast)[name = tensor<string, []>("op_4637_cast")];
            tensor<fp16, []> var_4638_to_fp16 = const()[name = tensor<string, []>("op_4638_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4639_cast = add(x = var_4637_cast, y = var_4638_to_fp16)[name = tensor<string, []>("op_4639_cast")];
            tensor<fp16, []> denom_135_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_135_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_135_cast = rsqrt(epsilon = denom_135_epsilon_0_to_fp16, x = var_4639_cast)[name = tensor<string, []>("denom_135_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_135_cast = mul(x = zero_mean_135_cast, y = denom_135_cast)[name = tensor<string, []>("out_135_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4643_to_fp16 = const()[name = tensor<string, []>("op_4643_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396328384)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4644_cast = add(x = out_135_cast, y = var_4643_to_fp16)[name = tensor<string, []>("op_4644_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4646_to_fp16 = const()[name = tensor<string, []>("op_4646_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396331008)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_187_cast = mul(x = var_4644_cast, y = var_4646_to_fp16)[name = tensor<string, []>("hidden_states_187_cast")];
            tensor<int32, [2]> var_4653 = const()[name = tensor<string, []>("op_4653"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4655 = const()[name = tensor<string, []>("op_4655"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_91_pad_type_0 = const()[name = tensor<string, []>("q_91_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_91_pad_0 = const()[name = tensor<string, []>("q_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396333632))), lut = tensor<fp16, [2]>([-0x1.cd4p-7, 0x1.cf4p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_91_cast = conv(dilations = var_4655, groups = var_1186, pad = q_91_pad_0, pad_type = q_91_pad_type_0, strides = var_4653, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor<string, []>("q_91_cast")];
            tensor<int32, [2]> var_4659 = const()[name = tensor<string, []>("op_4659"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4661 = const()[name = tensor<string, []>("op_4661"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_91_pad_type_0 = const()[name = tensor<string, []>("k_91_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_91_pad_0 = const()[name = tensor<string, []>("k_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396538496))), lut = tensor<fp16, [2]>([-0x1.584p-7, 0x1.568p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_91_cast = conv(dilations = var_4661, groups = var_1186, pad = k_91_pad_0, pad_type = k_91_pad_type_0, strides = var_4659, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_91_cast")];
            tensor<int32, [2]> var_4665 = const()[name = tensor<string, []>("op_4665"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4667 = const()[name = tensor<string, []>("op_4667"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_91_pad_type_0 = const()[name = tensor<string, []>("v_91_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_91_pad_0 = const()[name = tensor<string, []>("v_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396866240))), lut = tensor<fp16, [2]>([-0x1.ce8p-7, 0x1.cd8p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_91_cast = conv(dilations = var_4667, groups = var_1186, pad = v_91_pad_0, pad_type = v_91_pad_type_0, strides = var_4665, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_91_cast")];
            tensor<int32, [4]> var_4671 = const()[name = tensor<string, []>("op_4671"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4672_cast = reshape(shape = var_4671, x = q_91_cast)[name = tensor<string, []>("op_4672_cast")];
            tensor<int32, [4]> var_4673 = const()[name = tensor<string, []>("op_4673"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4674_cast = reshape(shape = var_4673, x = k_91_cast)[name = tensor<string, []>("op_4674_cast")];
            tensor<int32, [4]> var_4675 = const()[name = tensor<string, []>("op_4675"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4676_cast = reshape(shape = var_4675, x = v_91_cast)[name = tensor<string, []>("op_4676_cast")];
            tensor<bool, []> attn_weights_181_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_181_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_181_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_181_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_181_cast = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = var_4672_cast, y = var_4674_cast)[name = tensor<string, []>("attn_weights_181_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_183_cast = mul(x = attn_weights_181_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_183_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_4680_cast = softmax(axis = var_1170, x = attn_weights_183_cast)[name = tensor<string, []>("op_4680_cast")];
            tensor<bool, []> attn_91_transpose_x_0 = const()[name = tensor<string, []>("attn_91_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_91_transpose_y_0 = const()[name = tensor<string, []>("attn_91_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_91_cast = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_4676_cast, y = var_4680_cast)[name = tensor<string, []>("attn_91_cast")];
            tensor<int32, [4]> var_4684 = const()[name = tensor<string, []>("op_4684"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_295_cast = reshape(shape = var_4684, x = attn_91_cast)[name = tensor<string, []>("input_295_cast")];
            tensor<int32, [2]> var_4689 = const()[name = tensor<string, []>("op_4689"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4691 = const()[name = tensor<string, []>("op_4691"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4693_pad_type_0 = const()[name = tensor<string, []>("op_4693_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4693_pad_0 = const()[name = tensor<string, []>("op_4693_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397193984))), lut = tensor<fp16, [2]>([-0x1.23p-7, 0x1.25p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397398848)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4693_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_4691, groups = var_1186, pad = var_4693_pad_0, pad_type = var_4693_pad_type_0, strides = var_4689, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_295_cast)[name = tensor<string, []>("op_4693_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_137_cast = add(x = var_4693_cast, y = inputs_135_cast)[name = tensor<string, []>("inputs_137_cast")];
            tensor<int32, [1]> var_4697 = const()[name = tensor<string, []>("op_4697"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_137_cast = reduce_mean(axes = var_4697, keep_dims = var_1181, x = inputs_137_cast)[name = tensor<string, []>("channels_mean_137_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_137_cast = sub(x = inputs_137_cast, y = channels_mean_137_cast)[name = tensor<string, []>("zero_mean_137_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_137_cast = mul(x = zero_mean_137_cast, y = zero_mean_137_cast)[name = tensor<string, []>("zero_mean_sq_137_cast")];
            tensor<int32, [1]> var_4701 = const()[name = tensor<string, []>("op_4701"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4702_cast = reduce_mean(axes = var_4701, keep_dims = var_1181, x = zero_mean_sq_137_cast)[name = tensor<string, []>("op_4702_cast")];
            tensor<fp16, []> var_4703_to_fp16 = const()[name = tensor<string, []>("op_4703_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4704_cast = add(x = var_4702_cast, y = var_4703_to_fp16)[name = tensor<string, []>("op_4704_cast")];
            tensor<fp16, []> denom_137_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_137_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_137_cast = rsqrt(epsilon = denom_137_epsilon_0_to_fp16, x = var_4704_cast)[name = tensor<string, []>("denom_137_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_137_cast = mul(x = zero_mean_137_cast, y = denom_137_cast)[name = tensor<string, []>("out_137_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4708_to_fp16 = const()[name = tensor<string, []>("op_4708_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397401472)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4709_cast = add(x = out_137_cast, y = var_4708_to_fp16)[name = tensor<string, []>("op_4709_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4711_to_fp16 = const()[name = tensor<string, []>("op_4711_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397404096)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_297_cast = mul(x = var_4709_cast, y = var_4711_to_fp16)[name = tensor<string, []>("input_297_cast")];
            tensor<int32, [2]> var_4719 = const()[name = tensor<string, []>("op_4719"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4721 = const()[name = tensor<string, []>("op_4721"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4723_pad_type_0 = const()[name = tensor<string, []>("op_4723_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4723_pad_0 = const()[name = tensor<string, []>("op_4723_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397406720))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403960384))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403960512)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_4723_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_4721, groups = var_1186, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_297_cast)[name = tensor<string, []>("op_4723_cast")];
            tensor<int32, [2]> var_4724_split_sizes_0 = const()[name = tensor<string, []>("op_4724_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_4724_axis_0 = const()[name = tensor<string, []>("op_4724_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_4724_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_4724_cast_1 = split(axis = var_4724_axis_0, split_sizes = var_4724_split_sizes_0, x = var_4723_cast)[name = tensor<string, []>("op_4724_cast")];
            tensor<string, []> var_4726_mode_0 = const()[name = tensor<string, []>("op_4726_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_4726_cast = gelu(mode = var_4726_mode_0, x = var_4724_cast_1)[name = tensor<string, []>("op_4726_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_299_cast = mul(x = var_4724_cast_0, y = var_4726_cast)[name = tensor<string, []>("input_299_cast")];
            tensor<int32, [2]> var_4730 = const()[name = tensor<string, []>("op_4730"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4732 = const()[name = tensor<string, []>("op_4732"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4734_pad_type_0 = const()[name = tensor<string, []>("op_4734_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4734_pad_0 = const()[name = tensor<string, []>("op_4734_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403981056))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407257920))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407258048)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4734_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_4732, groups = var_1186, pad = var_4734_pad_0, pad_type = var_4734_pad_type_0, strides = var_4730, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_299_cast)[name = tensor<string, []>("op_4734_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_139_cast = add(x = var_4734_cast, y = inputs_137_cast)[name = tensor<string, []>("inputs_139_cast")];
            tensor<int32, [1]> var_4744 = const()[name = tensor<string, []>("op_4744"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_139_cast = reduce_mean(axes = var_4744, keep_dims = var_1181, x = inputs_139_cast)[name = tensor<string, []>("channels_mean_139_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_139_cast = sub(x = inputs_139_cast, y = channels_mean_139_cast)[name = tensor<string, []>("zero_mean_139_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_139_cast = mul(x = zero_mean_139_cast, y = zero_mean_139_cast)[name = tensor<string, []>("zero_mean_sq_139_cast")];
            tensor<int32, [1]> var_4748 = const()[name = tensor<string, []>("op_4748"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4749_cast = reduce_mean(axes = var_4748, keep_dims = var_1181, x = zero_mean_sq_139_cast)[name = tensor<string, []>("op_4749_cast")];
            tensor<fp16, []> var_4750_to_fp16 = const()[name = tensor<string, []>("op_4750_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4751_cast = add(x = var_4749_cast, y = var_4750_to_fp16)[name = tensor<string, []>("op_4751_cast")];
            tensor<fp16, []> denom_139_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_139_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_139_cast = rsqrt(epsilon = denom_139_epsilon_0_to_fp16, x = var_4751_cast)[name = tensor<string, []>("denom_139_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_139_cast = mul(x = zero_mean_139_cast, y = denom_139_cast)[name = tensor<string, []>("out_139_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4755_to_fp16 = const()[name = tensor<string, []>("op_4755_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407260672)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4756_cast = add(x = out_139_cast, y = var_4755_to_fp16)[name = tensor<string, []>("op_4756_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4758_to_fp16 = const()[name = tensor<string, []>("op_4758_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407263296)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_191_cast = mul(x = var_4756_cast, y = var_4758_to_fp16)[name = tensor<string, []>("hidden_states_191_cast")];
            tensor<int32, [2]> var_4765 = const()[name = tensor<string, []>("op_4765"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4767 = const()[name = tensor<string, []>("op_4767"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_93_pad_type_0 = const()[name = tensor<string, []>("q_93_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_93_pad_0 = const()[name = tensor<string, []>("q_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407265920))), lut = tensor<fp16, [4]>([-0x1.4a4p-5, -0x1.8b8p-7, 0x1.90cp-7, 0x1.4b4p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_93_cast = conv(dilations = var_4767, groups = var_1186, pad = q_93_pad_0, pad_type = q_93_pad_type_0, strides = var_4765, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_191_cast)[name = tensor<string, []>("q_93_cast")];
            tensor<int32, [2]> var_4771 = const()[name = tensor<string, []>("op_4771"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4773 = const()[name = tensor<string, []>("op_4773"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_93_pad_type_0 = const()[name = tensor<string, []>("k_93_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_93_pad_0 = const()[name = tensor<string, []>("k_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407675584))), lut = tensor<fp16, [4]>([-0x1.474p-5, -0x1.8a4p-7, 0x1.898p-7, 0x1.474p-5]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_93_cast = conv(dilations = var_4773, groups = var_1186, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_4771, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_191_cast)[name = tensor<string, []>("k_93_cast")];
            tensor<int32, [2]> var_4777 = const()[name = tensor<string, []>("op_4777"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4779 = const()[name = tensor<string, []>("op_4779"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_93_pad_type_0 = const()[name = tensor<string, []>("v_93_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_93_pad_0 = const()[name = tensor<string, []>("v_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408085248))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408904512))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_93_cast = conv(dilations = var_4779, groups = var_1186, pad = v_93_pad_0, pad_type = v_93_pad_type_0, strides = var_4777, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_191_cast)[name = tensor<string, []>("v_93_cast")];
            tensor<int32, [4]> var_4783 = const()[name = tensor<string, []>("op_4783"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4784_cast = reshape(shape = var_4783, x = q_93_cast)[name = tensor<string, []>("op_4784_cast")];
            tensor<int32, [4]> var_4785 = const()[name = tensor<string, []>("op_4785"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4786_cast = reshape(shape = var_4785, x = k_93_cast)[name = tensor<string, []>("op_4786_cast")];
            tensor<int32, [4]> var_4787 = const()[name = tensor<string, []>("op_4787"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4788_cast = reshape(shape = var_4787, x = v_93_cast)[name = tensor<string, []>("op_4788_cast")];
            tensor<bool, []> attn_weights_185_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_185_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_185_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_185_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_185_cast = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4784_cast, y = var_4786_cast)[name = tensor<string, []>("attn_weights_185_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_187_cast = mul(x = attn_weights_185_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_187_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_4792_cast = softmax(axis = var_1170, x = attn_weights_187_cast)[name = tensor<string, []>("op_4792_cast")];
            tensor<bool, []> attn_93_transpose_x_0 = const()[name = tensor<string, []>("attn_93_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_93_transpose_y_0 = const()[name = tensor<string, []>("attn_93_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_93_cast = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_4788_cast, y = var_4792_cast)[name = tensor<string, []>("attn_93_cast")];
            tensor<int32, [4]> var_4796 = const()[name = tensor<string, []>("op_4796"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_301_cast = reshape(shape = var_4796, x = attn_93_cast)[name = tensor<string, []>("input_301_cast")];
            tensor<int32, [2]> var_4801 = const()[name = tensor<string, []>("op_4801"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4803 = const()[name = tensor<string, []>("op_4803"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4805_pad_type_0 = const()[name = tensor<string, []>("op_4805_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4805_pad_0 = const()[name = tensor<string, []>("op_4805_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408904640))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409723904))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409724032)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4805_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_4803, groups = var_1186, pad = var_4805_pad_0, pad_type = var_4805_pad_type_0, strides = var_4801, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_301_cast)[name = tensor<string, []>("op_4805_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_141_cast = add(x = var_4805_cast, y = inputs_139_cast)[name = tensor<string, []>("inputs_141_cast")];
            tensor<int32, [1]> var_4809 = const()[name = tensor<string, []>("op_4809"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_141_cast = reduce_mean(axes = var_4809, keep_dims = var_1181, x = inputs_141_cast)[name = tensor<string, []>("channels_mean_141_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_141_cast = sub(x = inputs_141_cast, y = channels_mean_141_cast)[name = tensor<string, []>("zero_mean_141_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_141_cast = mul(x = zero_mean_141_cast, y = zero_mean_141_cast)[name = tensor<string, []>("zero_mean_sq_141_cast")];
            tensor<int32, [1]> var_4813 = const()[name = tensor<string, []>("op_4813"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4814_cast = reduce_mean(axes = var_4813, keep_dims = var_1181, x = zero_mean_sq_141_cast)[name = tensor<string, []>("op_4814_cast")];
            tensor<fp16, []> var_4815_to_fp16 = const()[name = tensor<string, []>("op_4815_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4816_cast = add(x = var_4814_cast, y = var_4815_to_fp16)[name = tensor<string, []>("op_4816_cast")];
            tensor<fp16, []> denom_141_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_141_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_141_cast = rsqrt(epsilon = denom_141_epsilon_0_to_fp16, x = var_4816_cast)[name = tensor<string, []>("denom_141_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_141_cast = mul(x = zero_mean_141_cast, y = denom_141_cast)[name = tensor<string, []>("out_141_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4820_to_fp16 = const()[name = tensor<string, []>("op_4820_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409726656)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4821_cast = add(x = out_141_cast, y = var_4820_to_fp16)[name = tensor<string, []>("op_4821_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4823_to_fp16 = const()[name = tensor<string, []>("op_4823_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409729280)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_193_cast = mul(x = var_4821_cast, y = var_4823_to_fp16)[name = tensor<string, []>("hidden_states_193_cast")];
            tensor<int32, [2]> var_4830 = const()[name = tensor<string, []>("op_4830"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4832 = const()[name = tensor<string, []>("op_4832"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_95_pad_type_0 = const()[name = tensor<string, []>("q_95_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_95_pad_0 = const()[name = tensor<string, []>("q_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409731904))), lut = tensor<fp16, [2]>([-0x1.a64p-7, 0x1.a5cp-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_95_cast = conv(dilations = var_4832, groups = var_1186, pad = q_95_pad_0, pad_type = q_95_pad_type_0, strides = var_4830, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_193_cast)[name = tensor<string, []>("q_95_cast")];
            tensor<int32, [2]> var_4836 = const()[name = tensor<string, []>("op_4836"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4838 = const()[name = tensor<string, []>("op_4838"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_95_pad_type_0 = const()[name = tensor<string, []>("k_95_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_95_pad_0 = const()[name = tensor<string, []>("k_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409936768))), lut = tensor<fp16, [2]>([-0x1.27cp-7, 0x1.288p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_95_cast = conv(dilations = var_4838, groups = var_1186, pad = k_95_pad_0, pad_type = k_95_pad_type_0, strides = var_4836, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_95_cast")];
            tensor<int32, [2]> var_4842 = const()[name = tensor<string, []>("op_4842"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4844 = const()[name = tensor<string, []>("op_4844"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_95_pad_type_0 = const()[name = tensor<string, []>("v_95_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_95_pad_0 = const()[name = tensor<string, []>("v_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410264512))), lut = tensor<fp16, [2]>([-0x1.77cp-7, 0x1.79p-7]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_95_cast = conv(dilations = var_4844, groups = var_1186, pad = v_95_pad_0, pad_type = v_95_pad_type_0, strides = var_4842, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_95_cast")];
            tensor<int32, [4]> var_4848 = const()[name = tensor<string, []>("op_4848"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_4849_cast = reshape(shape = var_4848, x = q_95_cast)[name = tensor<string, []>("op_4849_cast")];
            tensor<int32, [4]> var_4850 = const()[name = tensor<string, []>("op_4850"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4851_cast = reshape(shape = var_4850, x = k_95_cast)[name = tensor<string, []>("op_4851_cast")];
            tensor<int32, [4]> var_4852 = const()[name = tensor<string, []>("op_4852"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_4853_cast = reshape(shape = var_4852, x = v_95_cast)[name = tensor<string, []>("op_4853_cast")];
            tensor<bool, []> attn_weights_189_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_189_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_189_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_189_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_189_cast = matmul(transpose_x = attn_weights_189_transpose_x_0, transpose_y = attn_weights_189_transpose_y_0, x = var_4849_cast, y = var_4851_cast)[name = tensor<string, []>("attn_weights_189_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_191_cast = mul(x = attn_weights_189_cast, y = var_1177_to_fp16)[name = tensor<string, []>("attn_weights_191_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_4857_cast = softmax(axis = var_1170, x = attn_weights_191_cast)[name = tensor<string, []>("op_4857_cast")];
            tensor<bool, []> attn_95_transpose_x_0 = const()[name = tensor<string, []>("attn_95_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_95_transpose_y_0 = const()[name = tensor<string, []>("attn_95_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_95_cast = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_4853_cast, y = var_4857_cast)[name = tensor<string, []>("attn_95_cast")];
            tensor<int32, [4]> var_4861 = const()[name = tensor<string, []>("op_4861"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_303_cast = reshape(shape = var_4861, x = attn_95_cast)[name = tensor<string, []>("input_303_cast")];
            tensor<int32, [2]> var_4866 = const()[name = tensor<string, []>("op_4866"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4868 = const()[name = tensor<string, []>("op_4868"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4870_pad_type_0 = const()[name = tensor<string, []>("op_4870_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4870_pad_0 = const()[name = tensor<string, []>("op_4870_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410592256))), lut = tensor<fp16, [2]>([-0x1.e74p-8, 0x1.e64p-8]), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410797120)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4870_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_4868, groups = var_1186, pad = var_4870_pad_0, pad_type = var_4870_pad_type_0, strides = var_4866, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_303_cast)[name = tensor<string, []>("op_4870_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_143_cast = add(x = var_4870_cast, y = inputs_141_cast)[name = tensor<string, []>("inputs_143_cast")];
            tensor<int32, [1]> var_4874 = const()[name = tensor<string, []>("op_4874"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_143_cast = reduce_mean(axes = var_4874, keep_dims = var_1181, x = inputs_143_cast)[name = tensor<string, []>("channels_mean_143_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_143_cast = sub(x = inputs_143_cast, y = channels_mean_143_cast)[name = tensor<string, []>("zero_mean_143_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_143_cast = mul(x = zero_mean_143_cast, y = zero_mean_143_cast)[name = tensor<string, []>("zero_mean_sq_143_cast")];
            tensor<int32, [1]> var_4878 = const()[name = tensor<string, []>("op_4878"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_4879_cast = reduce_mean(axes = var_4878, keep_dims = var_1181, x = zero_mean_sq_143_cast)[name = tensor<string, []>("op_4879_cast")];
            tensor<fp16, []> var_4880_to_fp16 = const()[name = tensor<string, []>("op_4880_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_4881_cast = add(x = var_4879_cast, y = var_4880_to_fp16)[name = tensor<string, []>("op_4881_cast")];
            tensor<fp16, []> denom_143_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_143_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_143_cast = rsqrt(epsilon = denom_143_epsilon_0_to_fp16, x = var_4881_cast)[name = tensor<string, []>("denom_143_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_143_cast = mul(x = zero_mean_143_cast, y = denom_143_cast)[name = tensor<string, []>("out_143_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4885_to_fp16 = const()[name = tensor<string, []>("op_4885_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410799744)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4886_cast = add(x = out_143_cast, y = var_4885_to_fp16)[name = tensor<string, []>("op_4886_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_4888_to_fp16 = const()[name = tensor<string, []>("op_4888_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410802368)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_305_cast = mul(x = var_4886_cast, y = var_4888_to_fp16)[name = tensor<string, []>("input_305_cast")];
            tensor<int32, [2]> var_4896 = const()[name = tensor<string, []>("op_4896"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4898 = const()[name = tensor<string, []>("op_4898"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4900_pad_type_0 = const()[name = tensor<string, []>("op_4900_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4900_pad_0 = const()[name = tensor<string, []>("op_4900_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410804992))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417358656))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417358784)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_4900_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_4898, groups = var_1186, pad = var_4900_pad_0, pad_type = var_4900_pad_type_0, strides = var_4896, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_305_cast)[name = tensor<string, []>("op_4900_cast")];
            tensor<int32, [2]> var_4901_split_sizes_0 = const()[name = tensor<string, []>("op_4901_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_4901_axis_0 = const()[name = tensor<string, []>("op_4901_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_4901_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_4901_cast_1 = split(axis = var_4901_axis_0, split_sizes = var_4901_split_sizes_0, x = var_4900_cast)[name = tensor<string, []>("op_4901_cast")];
            tensor<string, []> var_4903_mode_0 = const()[name = tensor<string, []>("op_4903_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_4903_cast = gelu(mode = var_4903_mode_0, x = var_4901_cast_1)[name = tensor<string, []>("op_4903_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_307_cast = mul(x = var_4901_cast_0, y = var_4903_cast)[name = tensor<string, []>("input_307_cast")];
            tensor<int32, [2]> var_4907 = const()[name = tensor<string, []>("op_4907"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4909 = const()[name = tensor<string, []>("op_4909"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4911_pad_type_0 = const()[name = tensor<string, []>("op_4911_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4911_pad_0 = const()[name = tensor<string, []>("op_4911_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417379328))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420656192))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420656320)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_4911_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_4909, groups = var_1186, pad = var_4911_pad_0, pad_type = var_4911_pad_type_0, strides = var_4907, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_307_cast)[name = tensor<string, []>("op_4911_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_197_cast = add(x = var_4911_cast, y = inputs_143_cast)[name = tensor<string, []>("hidden_states_197_cast")];
            tensor<int32, [4]> var_4913 = const()[name = tensor<string, []>("op_4913"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> input_309_cast = reshape(shape = var_4913, x = hidden_states_197_cast)[name = tensor<string, []>("input_309_cast")];
            tensor<int32, [2]> var_4917 = const()[name = tensor<string, []>("op_4917"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4919 = const()[name = tensor<string, []>("op_4919"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_199_pad_type_0 = const()[name = tensor<string, []>("hidden_states_199_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_199_pad_0 = const()[name = tensor<string, []>("hidden_states_199_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420658944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421887808))), name = tensor<string, []>("down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421888000)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_199_cast = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_4919, groups = var_1186, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_4917, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_309_cast)[name = tensor<string, []>("hidden_states_199_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_311_cast = add(x = hidden_states_199_cast, y = hidden_states_133_cast)[name = tensor<string, []>("input_311_cast")];
            tensor<int32, []> var_4927 = const()[name = tensor<string, []>("op_4927"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_4938 = const()[name = tensor<string, []>("op_4938"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_4943 = const()[name = tensor<string, []>("op_4943"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_311_cast)[name = tensor<string, []>("reshape_64_cast")];
            tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = tensor<string, []>("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_48_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_48_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_48_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor<string, []>("reduce_mean_48_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor<string, []>("sub_32_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_16_cast = square(x = sub_32_cast)[name = tensor<string, []>("square_16_cast")];
            tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = tensor<string, []>("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_50_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_50_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_50_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor<string, []>("reduce_mean_50_cast")];
            tensor<fp16, []> add_32_y_0_to_fp16 = const()[name = tensor<string, []>("add_32_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor<string, []>("add_32_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor<string, []>("sqrt_16_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor<string, []>("real_div_16_cast")];
            tensor<int32, [4]> reshape_65_shape_0 = const()[name = tensor<string, []>("reshape_65_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor<string, []>("reshape_65_cast")];
            tensor<fp16, [1280]> add_33_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_33_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421890624)))];
            tensor<fp16, [1280]> add_33_beta_0_to_fp16 = const()[name = tensor<string, []>("add_33_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421893248)))];
            tensor<fp16, []> add_33_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_33_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_33_cast = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_65_cast)[name = tensor<string, []>("add_33_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_315_cast = silu(x = add_33_cast)[name = tensor<string, []>("input_315_cast")];
            tensor<int32, [2]> var_4961 = const()[name = tensor<string, []>("op_4961"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4963 = const()[name = tensor<string, []>("op_4963"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_201_pad_type_0 = const()[name = tensor<string, []>("hidden_states_201_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_201_pad_0 = const()[name = tensor<string, []>("hidden_states_201_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421895872))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432955136))), name = tensor<string, []>("mid_block_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432955328)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_201_cast = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_4963, groups = var_4943, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_4961, weight = mid_block_resnets_0_conv1_weight_to_fp16_palettized, x = input_315_cast)[name = tensor<string, []>("hidden_states_201_cast")];
            tensor<int32, [2]> var_4969 = const()[name = tensor<string, []>("op_4969"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4971 = const()[name = tensor<string, []>("op_4971"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_13_pad_type_0 = const()[name = tensor<string, []>("temb_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_13_pad_0 = const()[name = tensor<string, []>("temb_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432957952))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433777216))), name = tensor<string, []>("mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433777344)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_13_cast = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4971, groups = var_4943, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_4969, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_13_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_319_cast = add(x = hidden_states_201_cast, y = temb_13_cast)[name = tensor<string, []>("input_319_cast")];
            tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_319_cast)[name = tensor<string, []>("reshape_68_cast")];
            tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = tensor<string, []>("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_51_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_51_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_51_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor<string, []>("reduce_mean_51_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor<string, []>("sub_34_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_17_cast = square(x = sub_34_cast)[name = tensor<string, []>("square_17_cast")];
            tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = tensor<string, []>("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_53_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_53_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_53_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor<string, []>("reduce_mean_53_cast")];
            tensor<fp16, []> add_34_y_0_to_fp16 = const()[name = tensor<string, []>("add_34_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor<string, []>("add_34_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor<string, []>("sqrt_17_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor<string, []>("real_div_17_cast")];
            tensor<int32, [4]> reshape_69_shape_0 = const()[name = tensor<string, []>("reshape_69_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor<string, []>("reshape_69_cast")];
            tensor<fp16, [1280]> add_35_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_35_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433779968)))];
            tensor<fp16, [1280]> add_35_beta_0_to_fp16 = const()[name = tensor<string, []>("add_35_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433782592)))];
            tensor<fp16, []> add_35_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_35_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_35_cast = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_69_cast)[name = tensor<string, []>("add_35_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_323_cast = silu(x = add_35_cast)[name = tensor<string, []>("input_323_cast")];
            tensor<int32, [2]> var_4981 = const()[name = tensor<string, []>("op_4981"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4983 = const()[name = tensor<string, []>("op_4983"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_203_pad_type_0 = const()[name = tensor<string, []>("hidden_states_203_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = tensor<string, []>("hidden_states_203_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433785216))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444844480))), name = tensor<string, []>("mid_block_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444844672)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_203_cast = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_4983, groups = var_4943, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = var_4981, weight = mid_block_resnets_0_conv2_weight_to_fp16_palettized, x = input_323_cast)[name = tensor<string, []>("hidden_states_203_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_205_cast = add(x = input_311_cast, y = hidden_states_203_cast)[name = tensor<string, []>("hidden_states_205_cast")];
            tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_72_cast = reshape(shape = reshape_72_shape_0, x = hidden_states_205_cast)[name = tensor<string, []>("reshape_72_cast")];
            tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = tensor<string, []>("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_54_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_54_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_54_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor<string, []>("reduce_mean_54_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor<string, []>("sub_36_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_18_cast = square(x = sub_36_cast)[name = tensor<string, []>("square_18_cast")];
            tensor<int32, [3]> reduce_mean_56_axes_0 = const()[name = tensor<string, []>("reduce_mean_56_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_56_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_56_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_56_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor<string, []>("reduce_mean_56_cast")];
            tensor<fp16, []> add_36_y_0_to_fp16 = const()[name = tensor<string, []>("add_36_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor<string, []>("add_36_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor<string, []>("sqrt_18_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor<string, []>("real_div_18_cast")];
            tensor<int32, [4]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor<string, []>("reshape_73_cast")];
            tensor<fp16, [1280]> add_37_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_37_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444847296)))];
            tensor<fp16, [1280]> add_37_beta_0_to_fp16 = const()[name = tensor<string, []>("add_37_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444849920)))];
            tensor<fp16, []> add_37_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_37_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_37_cast = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_73_cast)[name = tensor<string, []>("add_37_cast")];
            tensor<int32, [2]> var_5021 = const()[name = tensor<string, []>("op_5021"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5023 = const()[name = tensor<string, []>("op_5023"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_207_pad_type_0 = const()[name = tensor<string, []>("hidden_states_207_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = tensor<string, []>("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444852544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446081408))), name = tensor<string, []>("mid_block_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446081600)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_207_cast = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_5023, groups = var_4943, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = var_5021, weight = mid_block_attentions_0_proj_in_weight_to_fp16_palettized, x = add_37_cast)[name = tensor<string, []>("hidden_states_207_cast")];
            tensor<int32, [4]> var_5028 = const()[name = tensor<string, []>("op_5028"), val = tensor<int32, [4]>([2, 1280, 1, 1024])];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_145_cast = reshape(shape = var_5028, x = hidden_states_207_cast)[name = tensor<string, []>("inputs_145_cast")];
            tensor<int32, [1]> var_5038 = const()[name = tensor<string, []>("op_5038"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_145_cast = reduce_mean(axes = var_5038, keep_dims = var_4938, x = inputs_145_cast)[name = tensor<string, []>("channels_mean_145_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_145_cast = sub(x = inputs_145_cast, y = channels_mean_145_cast)[name = tensor<string, []>("zero_mean_145_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_145_cast = mul(x = zero_mean_145_cast, y = zero_mean_145_cast)[name = tensor<string, []>("zero_mean_sq_145_cast")];
            tensor<int32, [1]> var_5042 = const()[name = tensor<string, []>("op_5042"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5043_cast = reduce_mean(axes = var_5042, keep_dims = var_4938, x = zero_mean_sq_145_cast)[name = tensor<string, []>("op_5043_cast")];
            tensor<fp16, []> var_5044_to_fp16 = const()[name = tensor<string, []>("op_5044_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5045_cast = add(x = var_5043_cast, y = var_5044_to_fp16)[name = tensor<string, []>("op_5045_cast")];
            tensor<fp16, []> denom_145_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_145_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_145_cast = rsqrt(epsilon = denom_145_epsilon_0_to_fp16, x = var_5045_cast)[name = tensor<string, []>("denom_145_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_145_cast = mul(x = zero_mean_145_cast, y = denom_145_cast)[name = tensor<string, []>("out_145_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5049_to_fp16 = const()[name = tensor<string, []>("op_5049_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446084224)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5050_cast = add(x = out_145_cast, y = var_5049_to_fp16)[name = tensor<string, []>("op_5050_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5052_to_fp16 = const()[name = tensor<string, []>("op_5052_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446086848)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_209_cast = mul(x = var_5050_cast, y = var_5052_to_fp16)[name = tensor<string, []>("hidden_states_209_cast")];
            tensor<int32, [2]> var_5059 = const()[name = tensor<string, []>("op_5059"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5061 = const()[name = tensor<string, []>("op_5061"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_97_pad_type_0 = const()[name = tensor<string, []>("q_97_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_97_pad_0 = const()[name = tensor<string, []>("q_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446089472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447318336))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_97_cast = conv(dilations = var_5061, groups = var_4943, pad = q_97_pad_0, pad_type = q_97_pad_type_0, strides = var_5059, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_209_cast)[name = tensor<string, []>("q_97_cast")];
            tensor<int32, [2]> var_5065 = const()[name = tensor<string, []>("op_5065"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5067 = const()[name = tensor<string, []>("op_5067"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_97_pad_type_0 = const()[name = tensor<string, []>("k_97_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_97_pad_0 = const()[name = tensor<string, []>("k_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447318528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448547392))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_97_cast = conv(dilations = var_5067, groups = var_4943, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_5065, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_209_cast)[name = tensor<string, []>("k_97_cast")];
            tensor<int32, [2]> var_5071 = const()[name = tensor<string, []>("op_5071"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5073 = const()[name = tensor<string, []>("op_5073"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_97_pad_type_0 = const()[name = tensor<string, []>("v_97_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_97_pad_0 = const()[name = tensor<string, []>("v_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448547584))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449776448))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_97_cast = conv(dilations = var_5073, groups = var_4943, pad = v_97_pad_0, pad_type = v_97_pad_type_0, strides = var_5071, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_209_cast)[name = tensor<string, []>("v_97_cast")];
            tensor<int32, [4]> var_5077 = const()[name = tensor<string, []>("op_5077"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5078_cast = reshape(shape = var_5077, x = q_97_cast)[name = tensor<string, []>("op_5078_cast")];
            tensor<int32, [4]> var_5079 = const()[name = tensor<string, []>("op_5079"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5080_cast = reshape(shape = var_5079, x = k_97_cast)[name = tensor<string, []>("op_5080_cast")];
            tensor<int32, [4]> var_5081 = const()[name = tensor<string, []>("op_5081"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5082_cast = reshape(shape = var_5081, x = v_97_cast)[name = tensor<string, []>("op_5082_cast")];
            tensor<bool, []> attn_weights_193_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_193_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_193_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_193_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_193_cast = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_5078_cast, y = var_5080_cast)[name = tensor<string, []>("attn_weights_193_cast")];
            tensor<fp16, []> var_4934_to_fp16 = const()[name = tensor<string, []>("op_4934_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_195_cast = mul(x = attn_weights_193_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_195_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_5086_cast = softmax(axis = var_4927, x = attn_weights_195_cast)[name = tensor<string, []>("op_5086_cast")];
            tensor<bool, []> attn_97_transpose_x_0 = const()[name = tensor<string, []>("attn_97_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_97_transpose_y_0 = const()[name = tensor<string, []>("attn_97_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_97_cast = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5082_cast, y = var_5086_cast)[name = tensor<string, []>("attn_97_cast")];
            tensor<int32, [4]> var_5090 = const()[name = tensor<string, []>("op_5090"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_327_cast = reshape(shape = var_5090, x = attn_97_cast)[name = tensor<string, []>("input_327_cast")];
            tensor<int32, [2]> var_5095 = const()[name = tensor<string, []>("op_5095"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5097 = const()[name = tensor<string, []>("op_5097"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5099_pad_type_0 = const()[name = tensor<string, []>("op_5099_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5099_pad_0 = const()[name = tensor<string, []>("op_5099_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449776640))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450595904))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450596032)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5099_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5097, groups = var_4943, pad = var_5099_pad_0, pad_type = var_5099_pad_type_0, strides = var_5095, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_327_cast)[name = tensor<string, []>("op_5099_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_147_cast = add(x = var_5099_cast, y = inputs_145_cast)[name = tensor<string, []>("inputs_147_cast")];
            tensor<int32, [1]> var_5103 = const()[name = tensor<string, []>("op_5103"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_147_cast = reduce_mean(axes = var_5103, keep_dims = var_4938, x = inputs_147_cast)[name = tensor<string, []>("channels_mean_147_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_147_cast = sub(x = inputs_147_cast, y = channels_mean_147_cast)[name = tensor<string, []>("zero_mean_147_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_147_cast = mul(x = zero_mean_147_cast, y = zero_mean_147_cast)[name = tensor<string, []>("zero_mean_sq_147_cast")];
            tensor<int32, [1]> var_5107 = const()[name = tensor<string, []>("op_5107"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5108_cast = reduce_mean(axes = var_5107, keep_dims = var_4938, x = zero_mean_sq_147_cast)[name = tensor<string, []>("op_5108_cast")];
            tensor<fp16, []> var_5109_to_fp16 = const()[name = tensor<string, []>("op_5109_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5110_cast = add(x = var_5108_cast, y = var_5109_to_fp16)[name = tensor<string, []>("op_5110_cast")];
            tensor<fp16, []> denom_147_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_147_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_147_cast = rsqrt(epsilon = denom_147_epsilon_0_to_fp16, x = var_5110_cast)[name = tensor<string, []>("denom_147_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_147_cast = mul(x = zero_mean_147_cast, y = denom_147_cast)[name = tensor<string, []>("out_147_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5114_to_fp16 = const()[name = tensor<string, []>("op_5114_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450598656)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5115_cast = add(x = out_147_cast, y = var_5114_to_fp16)[name = tensor<string, []>("op_5115_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5117_to_fp16 = const()[name = tensor<string, []>("op_5117_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450601280)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_211_cast = mul(x = var_5115_cast, y = var_5117_to_fp16)[name = tensor<string, []>("hidden_states_211_cast")];
            tensor<int32, [2]> var_5124 = const()[name = tensor<string, []>("op_5124"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5126 = const()[name = tensor<string, []>("op_5126"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_99_pad_type_0 = const()[name = tensor<string, []>("q_99_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_99_pad_0 = const()[name = tensor<string, []>("q_99_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450603904))), lut = tensor<fp16, [2]>([-0x1.964p-7, 0x1.96p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_99_cast = conv(dilations = var_5126, groups = var_4943, pad = q_99_pad_0, pad_type = q_99_pad_type_0, strides = var_5124, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_211_cast)[name = tensor<string, []>("q_99_cast")];
            tensor<int32, [2]> var_5130 = const()[name = tensor<string, []>("op_5130"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5132 = const()[name = tensor<string, []>("op_5132"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_99_pad_type_0 = const()[name = tensor<string, []>("k_99_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_99_pad_0 = const()[name = tensor<string, []>("k_99_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(450808768))), lut = tensor<fp16, [2]>([-0x1.444p-7, 0x1.44cp-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_99_cast = conv(dilations = var_5132, groups = var_4943, pad = k_99_pad_0, pad_type = k_99_pad_type_0, strides = var_5130, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_99_cast")];
            tensor<int32, [2]> var_5136 = const()[name = tensor<string, []>("op_5136"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5138 = const()[name = tensor<string, []>("op_5138"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_99_pad_type_0 = const()[name = tensor<string, []>("v_99_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_99_pad_0 = const()[name = tensor<string, []>("v_99_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451136512))), lut = tensor<fp16, [2]>([-0x1.658p-7, 0x1.66p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_99_cast = conv(dilations = var_5138, groups = var_4943, pad = v_99_pad_0, pad_type = v_99_pad_type_0, strides = var_5136, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_99_cast")];
            tensor<int32, [4]> var_5142 = const()[name = tensor<string, []>("op_5142"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5143_cast = reshape(shape = var_5142, x = q_99_cast)[name = tensor<string, []>("op_5143_cast")];
            tensor<int32, [4]> var_5144 = const()[name = tensor<string, []>("op_5144"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5145_cast = reshape(shape = var_5144, x = k_99_cast)[name = tensor<string, []>("op_5145_cast")];
            tensor<int32, [4]> var_5146 = const()[name = tensor<string, []>("op_5146"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5147_cast = reshape(shape = var_5146, x = v_99_cast)[name = tensor<string, []>("op_5147_cast")];
            tensor<bool, []> attn_weights_197_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_197_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_197_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_197_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_197_cast = matmul(transpose_x = attn_weights_197_transpose_x_0, transpose_y = attn_weights_197_transpose_y_0, x = var_5143_cast, y = var_5145_cast)[name = tensor<string, []>("attn_weights_197_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_199_cast = mul(x = attn_weights_197_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_199_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_5151_cast = softmax(axis = var_4927, x = attn_weights_199_cast)[name = tensor<string, []>("op_5151_cast")];
            tensor<bool, []> attn_99_transpose_x_0 = const()[name = tensor<string, []>("attn_99_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_99_transpose_y_0 = const()[name = tensor<string, []>("attn_99_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_99_cast = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5147_cast, y = var_5151_cast)[name = tensor<string, []>("attn_99_cast")];
            tensor<int32, [4]> var_5155 = const()[name = tensor<string, []>("op_5155"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_329_cast = reshape(shape = var_5155, x = attn_99_cast)[name = tensor<string, []>("input_329_cast")];
            tensor<int32, [2]> var_5160 = const()[name = tensor<string, []>("op_5160"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5162 = const()[name = tensor<string, []>("op_5162"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5164_pad_type_0 = const()[name = tensor<string, []>("op_5164_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5164_pad_0 = const()[name = tensor<string, []>("op_5164_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451464256))), lut = tensor<fp16, [2]>([-0x1.78cp-8, 0x1.764p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451669120)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5164_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5162, groups = var_4943, pad = var_5164_pad_0, pad_type = var_5164_pad_type_0, strides = var_5160, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_329_cast)[name = tensor<string, []>("op_5164_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_149_cast = add(x = var_5164_cast, y = inputs_147_cast)[name = tensor<string, []>("inputs_149_cast")];
            tensor<int32, [1]> var_5168 = const()[name = tensor<string, []>("op_5168"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_149_cast = reduce_mean(axes = var_5168, keep_dims = var_4938, x = inputs_149_cast)[name = tensor<string, []>("channels_mean_149_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_149_cast = sub(x = inputs_149_cast, y = channels_mean_149_cast)[name = tensor<string, []>("zero_mean_149_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_149_cast = mul(x = zero_mean_149_cast, y = zero_mean_149_cast)[name = tensor<string, []>("zero_mean_sq_149_cast")];
            tensor<int32, [1]> var_5172 = const()[name = tensor<string, []>("op_5172"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5173_cast = reduce_mean(axes = var_5172, keep_dims = var_4938, x = zero_mean_sq_149_cast)[name = tensor<string, []>("op_5173_cast")];
            tensor<fp16, []> var_5174_to_fp16 = const()[name = tensor<string, []>("op_5174_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5175_cast = add(x = var_5173_cast, y = var_5174_to_fp16)[name = tensor<string, []>("op_5175_cast")];
            tensor<fp16, []> denom_149_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_149_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_149_cast = rsqrt(epsilon = denom_149_epsilon_0_to_fp16, x = var_5175_cast)[name = tensor<string, []>("denom_149_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_149_cast = mul(x = zero_mean_149_cast, y = denom_149_cast)[name = tensor<string, []>("out_149_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5179_to_fp16 = const()[name = tensor<string, []>("op_5179_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451671744)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5180_cast = add(x = out_149_cast, y = var_5179_to_fp16)[name = tensor<string, []>("op_5180_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5182_to_fp16 = const()[name = tensor<string, []>("op_5182_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451674368)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_331_cast = mul(x = var_5180_cast, y = var_5182_to_fp16)[name = tensor<string, []>("input_331_cast")];
            tensor<int32, [2]> var_5190 = const()[name = tensor<string, []>("op_5190"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5192 = const()[name = tensor<string, []>("op_5192"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5194_pad_type_0 = const()[name = tensor<string, []>("op_5194_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5194_pad_0 = const()[name = tensor<string, []>("op_5194_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(451676992))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458230656))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458230784)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_5194_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_5192, groups = var_4943, pad = var_5194_pad_0, pad_type = var_5194_pad_type_0, strides = var_5190, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_331_cast)[name = tensor<string, []>("op_5194_cast")];
            tensor<int32, [2]> var_5195_split_sizes_0 = const()[name = tensor<string, []>("op_5195_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_5195_axis_0 = const()[name = tensor<string, []>("op_5195_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_5195_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_5195_cast_1 = split(axis = var_5195_axis_0, split_sizes = var_5195_split_sizes_0, x = var_5194_cast)[name = tensor<string, []>("op_5195_cast")];
            tensor<string, []> var_5197_mode_0 = const()[name = tensor<string, []>("op_5197_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_5197_cast = gelu(mode = var_5197_mode_0, x = var_5195_cast_1)[name = tensor<string, []>("op_5197_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_333_cast = mul(x = var_5195_cast_0, y = var_5197_cast)[name = tensor<string, []>("input_333_cast")];
            tensor<int32, [2]> var_5201 = const()[name = tensor<string, []>("op_5201"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5203 = const()[name = tensor<string, []>("op_5203"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5205_pad_type_0 = const()[name = tensor<string, []>("op_5205_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5205_pad_0 = const()[name = tensor<string, []>("op_5205_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458251328))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461528192))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461528320)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5205_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5203, groups = var_4943, pad = var_5205_pad_0, pad_type = var_5205_pad_type_0, strides = var_5201, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_333_cast)[name = tensor<string, []>("op_5205_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_151_cast = add(x = var_5205_cast, y = inputs_149_cast)[name = tensor<string, []>("inputs_151_cast")];
            tensor<int32, [1]> var_5215 = const()[name = tensor<string, []>("op_5215"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_151_cast = reduce_mean(axes = var_5215, keep_dims = var_4938, x = inputs_151_cast)[name = tensor<string, []>("channels_mean_151_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_151_cast = sub(x = inputs_151_cast, y = channels_mean_151_cast)[name = tensor<string, []>("zero_mean_151_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_151_cast = mul(x = zero_mean_151_cast, y = zero_mean_151_cast)[name = tensor<string, []>("zero_mean_sq_151_cast")];
            tensor<int32, [1]> var_5219 = const()[name = tensor<string, []>("op_5219"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5220_cast = reduce_mean(axes = var_5219, keep_dims = var_4938, x = zero_mean_sq_151_cast)[name = tensor<string, []>("op_5220_cast")];
            tensor<fp16, []> var_5221_to_fp16 = const()[name = tensor<string, []>("op_5221_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5222_cast = add(x = var_5220_cast, y = var_5221_to_fp16)[name = tensor<string, []>("op_5222_cast")];
            tensor<fp16, []> denom_151_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_151_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_151_cast = rsqrt(epsilon = denom_151_epsilon_0_to_fp16, x = var_5222_cast)[name = tensor<string, []>("denom_151_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_151_cast = mul(x = zero_mean_151_cast, y = denom_151_cast)[name = tensor<string, []>("out_151_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5226_to_fp16 = const()[name = tensor<string, []>("op_5226_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461530944)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5227_cast = add(x = out_151_cast, y = var_5226_to_fp16)[name = tensor<string, []>("op_5227_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5229_to_fp16 = const()[name = tensor<string, []>("op_5229_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461533568)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_215_cast = mul(x = var_5227_cast, y = var_5229_to_fp16)[name = tensor<string, []>("hidden_states_215_cast")];
            tensor<int32, [2]> var_5236 = const()[name = tensor<string, []>("op_5236"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5238 = const()[name = tensor<string, []>("op_5238"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_101_pad_type_0 = const()[name = tensor<string, []>("q_101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_101_pad_0 = const()[name = tensor<string, []>("q_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461536192))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(462355456))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_101_cast = conv(dilations = var_5238, groups = var_4943, pad = q_101_pad_0, pad_type = q_101_pad_type_0, strides = var_5236, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_215_cast)[name = tensor<string, []>("q_101_cast")];
            tensor<int32, [2]> var_5242 = const()[name = tensor<string, []>("op_5242"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5244 = const()[name = tensor<string, []>("op_5244"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_101_pad_type_0 = const()[name = tensor<string, []>("k_101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_101_pad_0 = const()[name = tensor<string, []>("k_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(462355584))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463584448))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_101_cast = conv(dilations = var_5244, groups = var_4943, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_5242, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_215_cast)[name = tensor<string, []>("k_101_cast")];
            tensor<int32, [2]> var_5248 = const()[name = tensor<string, []>("op_5248"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5250 = const()[name = tensor<string, []>("op_5250"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_101_pad_type_0 = const()[name = tensor<string, []>("v_101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_101_pad_0 = const()[name = tensor<string, []>("v_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(463584640))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464403904))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_101_cast = conv(dilations = var_5250, groups = var_4943, pad = v_101_pad_0, pad_type = v_101_pad_type_0, strides = var_5248, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_215_cast)[name = tensor<string, []>("v_101_cast")];
            tensor<int32, [4]> var_5254 = const()[name = tensor<string, []>("op_5254"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5255_cast = reshape(shape = var_5254, x = q_101_cast)[name = tensor<string, []>("op_5255_cast")];
            tensor<int32, [4]> var_5256 = const()[name = tensor<string, []>("op_5256"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5257_cast = reshape(shape = var_5256, x = k_101_cast)[name = tensor<string, []>("op_5257_cast")];
            tensor<int32, [4]> var_5258 = const()[name = tensor<string, []>("op_5258"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5259_cast = reshape(shape = var_5258, x = v_101_cast)[name = tensor<string, []>("op_5259_cast")];
            tensor<bool, []> attn_weights_201_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_201_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_201_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_201_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_201_cast = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_5255_cast, y = var_5257_cast)[name = tensor<string, []>("attn_weights_201_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_203_cast = mul(x = attn_weights_201_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_203_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_5263_cast = softmax(axis = var_4927, x = attn_weights_203_cast)[name = tensor<string, []>("op_5263_cast")];
            tensor<bool, []> attn_101_transpose_x_0 = const()[name = tensor<string, []>("attn_101_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_101_transpose_y_0 = const()[name = tensor<string, []>("attn_101_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_101_cast = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5259_cast, y = var_5263_cast)[name = tensor<string, []>("attn_101_cast")];
            tensor<int32, [4]> var_5267 = const()[name = tensor<string, []>("op_5267"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_335_cast = reshape(shape = var_5267, x = attn_101_cast)[name = tensor<string, []>("input_335_cast")];
            tensor<int32, [2]> var_5272 = const()[name = tensor<string, []>("op_5272"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5274 = const()[name = tensor<string, []>("op_5274"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5276_pad_type_0 = const()[name = tensor<string, []>("op_5276_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5276_pad_0 = const()[name = tensor<string, []>("op_5276_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464404032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465632896))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465633088)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5276_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_5274, groups = var_4943, pad = var_5276_pad_0, pad_type = var_5276_pad_type_0, strides = var_5272, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_335_cast)[name = tensor<string, []>("op_5276_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_153_cast = add(x = var_5276_cast, y = inputs_151_cast)[name = tensor<string, []>("inputs_153_cast")];
            tensor<int32, [1]> var_5280 = const()[name = tensor<string, []>("op_5280"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_153_cast = reduce_mean(axes = var_5280, keep_dims = var_4938, x = inputs_153_cast)[name = tensor<string, []>("channels_mean_153_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_153_cast = sub(x = inputs_153_cast, y = channels_mean_153_cast)[name = tensor<string, []>("zero_mean_153_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_153_cast = mul(x = zero_mean_153_cast, y = zero_mean_153_cast)[name = tensor<string, []>("zero_mean_sq_153_cast")];
            tensor<int32, [1]> var_5284 = const()[name = tensor<string, []>("op_5284"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5285_cast = reduce_mean(axes = var_5284, keep_dims = var_4938, x = zero_mean_sq_153_cast)[name = tensor<string, []>("op_5285_cast")];
            tensor<fp16, []> var_5286_to_fp16 = const()[name = tensor<string, []>("op_5286_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5287_cast = add(x = var_5285_cast, y = var_5286_to_fp16)[name = tensor<string, []>("op_5287_cast")];
            tensor<fp16, []> denom_153_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_153_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_153_cast = rsqrt(epsilon = denom_153_epsilon_0_to_fp16, x = var_5287_cast)[name = tensor<string, []>("denom_153_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_153_cast = mul(x = zero_mean_153_cast, y = denom_153_cast)[name = tensor<string, []>("out_153_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5291_to_fp16 = const()[name = tensor<string, []>("op_5291_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465635712)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5292_cast = add(x = out_153_cast, y = var_5291_to_fp16)[name = tensor<string, []>("op_5292_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5294_to_fp16 = const()[name = tensor<string, []>("op_5294_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465638336)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_217_cast = mul(x = var_5292_cast, y = var_5294_to_fp16)[name = tensor<string, []>("hidden_states_217_cast")];
            tensor<int32, [2]> var_5301 = const()[name = tensor<string, []>("op_5301"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5303 = const()[name = tensor<string, []>("op_5303"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_103_pad_type_0 = const()[name = tensor<string, []>("q_103_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_103_pad_0 = const()[name = tensor<string, []>("q_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465640960))), lut = tensor<fp16, [2]>([-0x1.acp-7, 0x1.ab4p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_103_cast = conv(dilations = var_5303, groups = var_4943, pad = q_103_pad_0, pad_type = q_103_pad_type_0, strides = var_5301, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_217_cast)[name = tensor<string, []>("q_103_cast")];
            tensor<int32, [2]> var_5307 = const()[name = tensor<string, []>("op_5307"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5309 = const()[name = tensor<string, []>("op_5309"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_103_pad_type_0 = const()[name = tensor<string, []>("k_103_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_103_pad_0 = const()[name = tensor<string, []>("k_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465845824))), lut = tensor<fp16, [2]>([-0x1.514p-7, 0x1.528p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_103_cast = conv(dilations = var_5309, groups = var_4943, pad = k_103_pad_0, pad_type = k_103_pad_type_0, strides = var_5307, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_103_cast")];
            tensor<int32, [2]> var_5313 = const()[name = tensor<string, []>("op_5313"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5315 = const()[name = tensor<string, []>("op_5315"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_103_pad_type_0 = const()[name = tensor<string, []>("v_103_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_103_pad_0 = const()[name = tensor<string, []>("v_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466173568))), lut = tensor<fp16, [2]>([-0x1.88cp-7, 0x1.884p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_103_cast = conv(dilations = var_5315, groups = var_4943, pad = v_103_pad_0, pad_type = v_103_pad_type_0, strides = var_5313, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_103_cast")];
            tensor<int32, [4]> var_5319 = const()[name = tensor<string, []>("op_5319"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5320_cast = reshape(shape = var_5319, x = q_103_cast)[name = tensor<string, []>("op_5320_cast")];
            tensor<int32, [4]> var_5321 = const()[name = tensor<string, []>("op_5321"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5322_cast = reshape(shape = var_5321, x = k_103_cast)[name = tensor<string, []>("op_5322_cast")];
            tensor<int32, [4]> var_5323 = const()[name = tensor<string, []>("op_5323"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5324_cast = reshape(shape = var_5323, x = v_103_cast)[name = tensor<string, []>("op_5324_cast")];
            tensor<bool, []> attn_weights_205_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_205_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_205_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_205_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_205_cast = matmul(transpose_x = attn_weights_205_transpose_x_0, transpose_y = attn_weights_205_transpose_y_0, x = var_5320_cast, y = var_5322_cast)[name = tensor<string, []>("attn_weights_205_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_207_cast = mul(x = attn_weights_205_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_207_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_5328_cast = softmax(axis = var_4927, x = attn_weights_207_cast)[name = tensor<string, []>("op_5328_cast")];
            tensor<bool, []> attn_103_transpose_x_0 = const()[name = tensor<string, []>("attn_103_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_103_transpose_y_0 = const()[name = tensor<string, []>("attn_103_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_103_cast = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5324_cast, y = var_5328_cast)[name = tensor<string, []>("attn_103_cast")];
            tensor<int32, [4]> var_5332 = const()[name = tensor<string, []>("op_5332"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_337_cast = reshape(shape = var_5332, x = attn_103_cast)[name = tensor<string, []>("input_337_cast")];
            tensor<int32, [2]> var_5337 = const()[name = tensor<string, []>("op_5337"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5339 = const()[name = tensor<string, []>("op_5339"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5341_pad_type_0 = const()[name = tensor<string, []>("op_5341_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5341_pad_0 = const()[name = tensor<string, []>("op_5341_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466501312))), lut = tensor<fp16, [2]>([-0x1.aacp-8, 0x1.ab4p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466706176)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5341_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_5339, groups = var_4943, pad = var_5341_pad_0, pad_type = var_5341_pad_type_0, strides = var_5337, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_337_cast)[name = tensor<string, []>("op_5341_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_155_cast = add(x = var_5341_cast, y = inputs_153_cast)[name = tensor<string, []>("inputs_155_cast")];
            tensor<int32, [1]> var_5345 = const()[name = tensor<string, []>("op_5345"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_155_cast = reduce_mean(axes = var_5345, keep_dims = var_4938, x = inputs_155_cast)[name = tensor<string, []>("channels_mean_155_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_155_cast = sub(x = inputs_155_cast, y = channels_mean_155_cast)[name = tensor<string, []>("zero_mean_155_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_155_cast = mul(x = zero_mean_155_cast, y = zero_mean_155_cast)[name = tensor<string, []>("zero_mean_sq_155_cast")];
            tensor<int32, [1]> var_5349 = const()[name = tensor<string, []>("op_5349"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5350_cast = reduce_mean(axes = var_5349, keep_dims = var_4938, x = zero_mean_sq_155_cast)[name = tensor<string, []>("op_5350_cast")];
            tensor<fp16, []> var_5351_to_fp16 = const()[name = tensor<string, []>("op_5351_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5352_cast = add(x = var_5350_cast, y = var_5351_to_fp16)[name = tensor<string, []>("op_5352_cast")];
            tensor<fp16, []> denom_155_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_155_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_155_cast = rsqrt(epsilon = denom_155_epsilon_0_to_fp16, x = var_5352_cast)[name = tensor<string, []>("denom_155_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_155_cast = mul(x = zero_mean_155_cast, y = denom_155_cast)[name = tensor<string, []>("out_155_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5356_to_fp16 = const()[name = tensor<string, []>("op_5356_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466708800)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5357_cast = add(x = out_155_cast, y = var_5356_to_fp16)[name = tensor<string, []>("op_5357_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5359_to_fp16 = const()[name = tensor<string, []>("op_5359_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466711424)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_339_cast = mul(x = var_5357_cast, y = var_5359_to_fp16)[name = tensor<string, []>("input_339_cast")];
            tensor<int32, [2]> var_5367 = const()[name = tensor<string, []>("op_5367"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5369 = const()[name = tensor<string, []>("op_5369"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5371_pad_type_0 = const()[name = tensor<string, []>("op_5371_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5371_pad_0 = const()[name = tensor<string, []>("op_5371_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466714048))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(476544512))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(476544704)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_5371_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_5369, groups = var_4943, pad = var_5371_pad_0, pad_type = var_5371_pad_type_0, strides = var_5367, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_339_cast)[name = tensor<string, []>("op_5371_cast")];
            tensor<int32, [2]> var_5372_split_sizes_0 = const()[name = tensor<string, []>("op_5372_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_5372_axis_0 = const()[name = tensor<string, []>("op_5372_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_5372_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_5372_cast_1 = split(axis = var_5372_axis_0, split_sizes = var_5372_split_sizes_0, x = var_5371_cast)[name = tensor<string, []>("op_5372_cast")];
            tensor<string, []> var_5374_mode_0 = const()[name = tensor<string, []>("op_5374_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_5374_cast = gelu(mode = var_5374_mode_0, x = var_5372_cast_1)[name = tensor<string, []>("op_5374_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_341_cast = mul(x = var_5372_cast_0, y = var_5374_cast)[name = tensor<string, []>("input_341_cast")];
            tensor<int32, [2]> var_5378 = const()[name = tensor<string, []>("op_5378"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5380 = const()[name = tensor<string, []>("op_5380"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5382_pad_type_0 = const()[name = tensor<string, []>("op_5382_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5382_pad_0 = const()[name = tensor<string, []>("op_5382_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(476565248))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479842112))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479842240)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5382_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_5380, groups = var_4943, pad = var_5382_pad_0, pad_type = var_5382_pad_type_0, strides = var_5378, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_341_cast)[name = tensor<string, []>("op_5382_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_157_cast = add(x = var_5382_cast, y = inputs_155_cast)[name = tensor<string, []>("inputs_157_cast")];
            tensor<int32, [1]> var_5392 = const()[name = tensor<string, []>("op_5392"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_157_cast = reduce_mean(axes = var_5392, keep_dims = var_4938, x = inputs_157_cast)[name = tensor<string, []>("channels_mean_157_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_157_cast = sub(x = inputs_157_cast, y = channels_mean_157_cast)[name = tensor<string, []>("zero_mean_157_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_157_cast = mul(x = zero_mean_157_cast, y = zero_mean_157_cast)[name = tensor<string, []>("zero_mean_sq_157_cast")];
            tensor<int32, [1]> var_5396 = const()[name = tensor<string, []>("op_5396"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5397_cast = reduce_mean(axes = var_5396, keep_dims = var_4938, x = zero_mean_sq_157_cast)[name = tensor<string, []>("op_5397_cast")];
            tensor<fp16, []> var_5398_to_fp16 = const()[name = tensor<string, []>("op_5398_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5399_cast = add(x = var_5397_cast, y = var_5398_to_fp16)[name = tensor<string, []>("op_5399_cast")];
            tensor<fp16, []> denom_157_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_157_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_157_cast = rsqrt(epsilon = denom_157_epsilon_0_to_fp16, x = var_5399_cast)[name = tensor<string, []>("denom_157_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_157_cast = mul(x = zero_mean_157_cast, y = denom_157_cast)[name = tensor<string, []>("out_157_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5403_to_fp16 = const()[name = tensor<string, []>("op_5403_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479844864)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5404_cast = add(x = out_157_cast, y = var_5403_to_fp16)[name = tensor<string, []>("op_5404_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5406_to_fp16 = const()[name = tensor<string, []>("op_5406_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479847488)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_221_cast = mul(x = var_5404_cast, y = var_5406_to_fp16)[name = tensor<string, []>("hidden_states_221_cast")];
            tensor<int32, [2]> var_5413 = const()[name = tensor<string, []>("op_5413"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5415 = const()[name = tensor<string, []>("op_5415"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_105_pad_type_0 = const()[name = tensor<string, []>("q_105_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_105_pad_0 = const()[name = tensor<string, []>("q_105_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479850112))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480669376))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_105_cast = conv(dilations = var_5415, groups = var_4943, pad = q_105_pad_0, pad_type = q_105_pad_type_0, strides = var_5413, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_221_cast)[name = tensor<string, []>("q_105_cast")];
            tensor<int32, [2]> var_5419 = const()[name = tensor<string, []>("op_5419"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5421 = const()[name = tensor<string, []>("op_5421"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_105_pad_type_0 = const()[name = tensor<string, []>("k_105_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_105_pad_0 = const()[name = tensor<string, []>("k_105_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480669504))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481488768))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_105_cast = conv(dilations = var_5421, groups = var_4943, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_5419, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_221_cast)[name = tensor<string, []>("k_105_cast")];
            tensor<int32, [2]> var_5425 = const()[name = tensor<string, []>("op_5425"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5427 = const()[name = tensor<string, []>("op_5427"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_105_pad_type_0 = const()[name = tensor<string, []>("v_105_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_105_pad_0 = const()[name = tensor<string, []>("v_105_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481488896))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482308160))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_105_cast = conv(dilations = var_5427, groups = var_4943, pad = v_105_pad_0, pad_type = v_105_pad_type_0, strides = var_5425, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_221_cast)[name = tensor<string, []>("v_105_cast")];
            tensor<int32, [4]> var_5431 = const()[name = tensor<string, []>("op_5431"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5432_cast = reshape(shape = var_5431, x = q_105_cast)[name = tensor<string, []>("op_5432_cast")];
            tensor<int32, [4]> var_5433 = const()[name = tensor<string, []>("op_5433"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5434_cast = reshape(shape = var_5433, x = k_105_cast)[name = tensor<string, []>("op_5434_cast")];
            tensor<int32, [4]> var_5435 = const()[name = tensor<string, []>("op_5435"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5436_cast = reshape(shape = var_5435, x = v_105_cast)[name = tensor<string, []>("op_5436_cast")];
            tensor<bool, []> attn_weights_209_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_209_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_209_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_209_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_209_cast = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5432_cast, y = var_5434_cast)[name = tensor<string, []>("attn_weights_209_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_211_cast = mul(x = attn_weights_209_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_211_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_5440_cast = softmax(axis = var_4927, x = attn_weights_211_cast)[name = tensor<string, []>("op_5440_cast")];
            tensor<bool, []> attn_105_transpose_x_0 = const()[name = tensor<string, []>("attn_105_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_105_transpose_y_0 = const()[name = tensor<string, []>("attn_105_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_105_cast = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5436_cast, y = var_5440_cast)[name = tensor<string, []>("attn_105_cast")];
            tensor<int32, [4]> var_5444 = const()[name = tensor<string, []>("op_5444"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_343_cast = reshape(shape = var_5444, x = attn_105_cast)[name = tensor<string, []>("input_343_cast")];
            tensor<int32, [2]> var_5449 = const()[name = tensor<string, []>("op_5449"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5451 = const()[name = tensor<string, []>("op_5451"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5453_pad_type_0 = const()[name = tensor<string, []>("op_5453_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5453_pad_0 = const()[name = tensor<string, []>("op_5453_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482308288))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483127552))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483127680)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5453_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_5451, groups = var_4943, pad = var_5453_pad_0, pad_type = var_5453_pad_type_0, strides = var_5449, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_343_cast)[name = tensor<string, []>("op_5453_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_159_cast = add(x = var_5453_cast, y = inputs_157_cast)[name = tensor<string, []>("inputs_159_cast")];
            tensor<int32, [1]> var_5457 = const()[name = tensor<string, []>("op_5457"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_159_cast = reduce_mean(axes = var_5457, keep_dims = var_4938, x = inputs_159_cast)[name = tensor<string, []>("channels_mean_159_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_159_cast = sub(x = inputs_159_cast, y = channels_mean_159_cast)[name = tensor<string, []>("zero_mean_159_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_159_cast = mul(x = zero_mean_159_cast, y = zero_mean_159_cast)[name = tensor<string, []>("zero_mean_sq_159_cast")];
            tensor<int32, [1]> var_5461 = const()[name = tensor<string, []>("op_5461"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5462_cast = reduce_mean(axes = var_5461, keep_dims = var_4938, x = zero_mean_sq_159_cast)[name = tensor<string, []>("op_5462_cast")];
            tensor<fp16, []> var_5463_to_fp16 = const()[name = tensor<string, []>("op_5463_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5464_cast = add(x = var_5462_cast, y = var_5463_to_fp16)[name = tensor<string, []>("op_5464_cast")];
            tensor<fp16, []> denom_159_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_159_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_159_cast = rsqrt(epsilon = denom_159_epsilon_0_to_fp16, x = var_5464_cast)[name = tensor<string, []>("denom_159_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_159_cast = mul(x = zero_mean_159_cast, y = denom_159_cast)[name = tensor<string, []>("out_159_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5468_to_fp16 = const()[name = tensor<string, []>("op_5468_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483130304)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5469_cast = add(x = out_159_cast, y = var_5468_to_fp16)[name = tensor<string, []>("op_5469_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5471_to_fp16 = const()[name = tensor<string, []>("op_5471_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483132928)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_223_cast = mul(x = var_5469_cast, y = var_5471_to_fp16)[name = tensor<string, []>("hidden_states_223_cast")];
            tensor<int32, [2]> var_5478 = const()[name = tensor<string, []>("op_5478"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5480 = const()[name = tensor<string, []>("op_5480"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_107_pad_type_0 = const()[name = tensor<string, []>("q_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_107_pad_0 = const()[name = tensor<string, []>("q_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483135552))), lut = tensor<fp16, [2]>([-0x1.93p-7, 0x1.938p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_107_cast = conv(dilations = var_5480, groups = var_4943, pad = q_107_pad_0, pad_type = q_107_pad_type_0, strides = var_5478, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_223_cast)[name = tensor<string, []>("q_107_cast")];
            tensor<int32, [2]> var_5484 = const()[name = tensor<string, []>("op_5484"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5486 = const()[name = tensor<string, []>("op_5486"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_107_pad_type_0 = const()[name = tensor<string, []>("k_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_107_pad_0 = const()[name = tensor<string, []>("k_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483340416))), lut = tensor<fp16, [2]>([-0x1.294p-7, 0x1.28cp-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_107_cast = conv(dilations = var_5486, groups = var_4943, pad = k_107_pad_0, pad_type = k_107_pad_type_0, strides = var_5484, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_107_cast")];
            tensor<int32, [2]> var_5490 = const()[name = tensor<string, []>("op_5490"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5492 = const()[name = tensor<string, []>("op_5492"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_107_pad_type_0 = const()[name = tensor<string, []>("v_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_107_pad_0 = const()[name = tensor<string, []>("v_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483668160))), lut = tensor<fp16, [2]>([-0x1.63cp-7, 0x1.62cp-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_107_cast = conv(dilations = var_5492, groups = var_4943, pad = v_107_pad_0, pad_type = v_107_pad_type_0, strides = var_5490, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_107_cast")];
            tensor<int32, [4]> var_5496 = const()[name = tensor<string, []>("op_5496"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5497_cast = reshape(shape = var_5496, x = q_107_cast)[name = tensor<string, []>("op_5497_cast")];
            tensor<int32, [4]> var_5498 = const()[name = tensor<string, []>("op_5498"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5499_cast = reshape(shape = var_5498, x = k_107_cast)[name = tensor<string, []>("op_5499_cast")];
            tensor<int32, [4]> var_5500 = const()[name = tensor<string, []>("op_5500"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5501_cast = reshape(shape = var_5500, x = v_107_cast)[name = tensor<string, []>("op_5501_cast")];
            tensor<bool, []> attn_weights_213_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_213_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_213_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_213_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_213_cast = matmul(transpose_x = attn_weights_213_transpose_x_0, transpose_y = attn_weights_213_transpose_y_0, x = var_5497_cast, y = var_5499_cast)[name = tensor<string, []>("attn_weights_213_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_215_cast = mul(x = attn_weights_213_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_215_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_5505_cast = softmax(axis = var_4927, x = attn_weights_215_cast)[name = tensor<string, []>("op_5505_cast")];
            tensor<bool, []> attn_107_transpose_x_0 = const()[name = tensor<string, []>("attn_107_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_107_transpose_y_0 = const()[name = tensor<string, []>("attn_107_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_107_cast = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5501_cast, y = var_5505_cast)[name = tensor<string, []>("attn_107_cast")];
            tensor<int32, [4]> var_5509 = const()[name = tensor<string, []>("op_5509"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_345_cast = reshape(shape = var_5509, x = attn_107_cast)[name = tensor<string, []>("input_345_cast")];
            tensor<int32, [2]> var_5514 = const()[name = tensor<string, []>("op_5514"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5516 = const()[name = tensor<string, []>("op_5516"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5518_pad_type_0 = const()[name = tensor<string, []>("op_5518_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5518_pad_0 = const()[name = tensor<string, []>("op_5518_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483995904))), lut = tensor<fp16, [2]>([-0x1.878p-8, 0x1.86cp-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484200768)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5518_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_5516, groups = var_4943, pad = var_5518_pad_0, pad_type = var_5518_pad_type_0, strides = var_5514, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_345_cast)[name = tensor<string, []>("op_5518_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_161_cast = add(x = var_5518_cast, y = inputs_159_cast)[name = tensor<string, []>("inputs_161_cast")];
            tensor<int32, [1]> var_5522 = const()[name = tensor<string, []>("op_5522"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_161_cast = reduce_mean(axes = var_5522, keep_dims = var_4938, x = inputs_161_cast)[name = tensor<string, []>("channels_mean_161_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_161_cast = sub(x = inputs_161_cast, y = channels_mean_161_cast)[name = tensor<string, []>("zero_mean_161_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_161_cast = mul(x = zero_mean_161_cast, y = zero_mean_161_cast)[name = tensor<string, []>("zero_mean_sq_161_cast")];
            tensor<int32, [1]> var_5526 = const()[name = tensor<string, []>("op_5526"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5527_cast = reduce_mean(axes = var_5526, keep_dims = var_4938, x = zero_mean_sq_161_cast)[name = tensor<string, []>("op_5527_cast")];
            tensor<fp16, []> var_5528_to_fp16 = const()[name = tensor<string, []>("op_5528_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5529_cast = add(x = var_5527_cast, y = var_5528_to_fp16)[name = tensor<string, []>("op_5529_cast")];
            tensor<fp16, []> denom_161_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_161_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_161_cast = rsqrt(epsilon = denom_161_epsilon_0_to_fp16, x = var_5529_cast)[name = tensor<string, []>("denom_161_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_161_cast = mul(x = zero_mean_161_cast, y = denom_161_cast)[name = tensor<string, []>("out_161_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5533_to_fp16 = const()[name = tensor<string, []>("op_5533_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484203392)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5534_cast = add(x = out_161_cast, y = var_5533_to_fp16)[name = tensor<string, []>("op_5534_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5536_to_fp16 = const()[name = tensor<string, []>("op_5536_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484206016)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_347_cast = mul(x = var_5534_cast, y = var_5536_to_fp16)[name = tensor<string, []>("input_347_cast")];
            tensor<int32, [2]> var_5544 = const()[name = tensor<string, []>("op_5544"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5546 = const()[name = tensor<string, []>("op_5546"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5548_pad_type_0 = const()[name = tensor<string, []>("op_5548_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5548_pad_0 = const()[name = tensor<string, []>("op_5548_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484208640))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490762304))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490762432)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_5548_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_5546, groups = var_4943, pad = var_5548_pad_0, pad_type = var_5548_pad_type_0, strides = var_5544, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_347_cast)[name = tensor<string, []>("op_5548_cast")];
            tensor<int32, [2]> var_5549_split_sizes_0 = const()[name = tensor<string, []>("op_5549_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_5549_axis_0 = const()[name = tensor<string, []>("op_5549_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_5549_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_5549_cast_1 = split(axis = var_5549_axis_0, split_sizes = var_5549_split_sizes_0, x = var_5548_cast)[name = tensor<string, []>("op_5549_cast")];
            tensor<string, []> var_5551_mode_0 = const()[name = tensor<string, []>("op_5551_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_5551_cast = gelu(mode = var_5551_mode_0, x = var_5549_cast_1)[name = tensor<string, []>("op_5551_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_349_cast = mul(x = var_5549_cast_0, y = var_5551_cast)[name = tensor<string, []>("input_349_cast")];
            tensor<int32, [2]> var_5555 = const()[name = tensor<string, []>("op_5555"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5557 = const()[name = tensor<string, []>("op_5557"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5559_pad_type_0 = const()[name = tensor<string, []>("op_5559_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5559_pad_0 = const()[name = tensor<string, []>("op_5559_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490782976))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494059840))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494059968)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5559_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_5557, groups = var_4943, pad = var_5559_pad_0, pad_type = var_5559_pad_type_0, strides = var_5555, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_349_cast)[name = tensor<string, []>("op_5559_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_163_cast = add(x = var_5559_cast, y = inputs_161_cast)[name = tensor<string, []>("inputs_163_cast")];
            tensor<int32, [1]> var_5569 = const()[name = tensor<string, []>("op_5569"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_163_cast = reduce_mean(axes = var_5569, keep_dims = var_4938, x = inputs_163_cast)[name = tensor<string, []>("channels_mean_163_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_163_cast = sub(x = inputs_163_cast, y = channels_mean_163_cast)[name = tensor<string, []>("zero_mean_163_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_163_cast = mul(x = zero_mean_163_cast, y = zero_mean_163_cast)[name = tensor<string, []>("zero_mean_sq_163_cast")];
            tensor<int32, [1]> var_5573 = const()[name = tensor<string, []>("op_5573"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5574_cast = reduce_mean(axes = var_5573, keep_dims = var_4938, x = zero_mean_sq_163_cast)[name = tensor<string, []>("op_5574_cast")];
            tensor<fp16, []> var_5575_to_fp16 = const()[name = tensor<string, []>("op_5575_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5576_cast = add(x = var_5574_cast, y = var_5575_to_fp16)[name = tensor<string, []>("op_5576_cast")];
            tensor<fp16, []> denom_163_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_163_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_163_cast = rsqrt(epsilon = denom_163_epsilon_0_to_fp16, x = var_5576_cast)[name = tensor<string, []>("denom_163_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_163_cast = mul(x = zero_mean_163_cast, y = denom_163_cast)[name = tensor<string, []>("out_163_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5580_to_fp16 = const()[name = tensor<string, []>("op_5580_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494062592)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5581_cast = add(x = out_163_cast, y = var_5580_to_fp16)[name = tensor<string, []>("op_5581_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5583_to_fp16 = const()[name = tensor<string, []>("op_5583_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494065216)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_227_cast = mul(x = var_5581_cast, y = var_5583_to_fp16)[name = tensor<string, []>("hidden_states_227_cast")];
            tensor<int32, [2]> var_5590 = const()[name = tensor<string, []>("op_5590"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5592 = const()[name = tensor<string, []>("op_5592"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_109_pad_type_0 = const()[name = tensor<string, []>("q_109_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_109_pad_0 = const()[name = tensor<string, []>("q_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494067840))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494887104))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_109_cast = conv(dilations = var_5592, groups = var_4943, pad = q_109_pad_0, pad_type = q_109_pad_type_0, strides = var_5590, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor<string, []>("q_109_cast")];
            tensor<int32, [2]> var_5596 = const()[name = tensor<string, []>("op_5596"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5598 = const()[name = tensor<string, []>("op_5598"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_109_pad_type_0 = const()[name = tensor<string, []>("k_109_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_109_pad_0 = const()[name = tensor<string, []>("k_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494887232))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(495706496))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_109_cast = conv(dilations = var_5598, groups = var_4943, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_5596, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor<string, []>("k_109_cast")];
            tensor<int32, [2]> var_5602 = const()[name = tensor<string, []>("op_5602"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5604 = const()[name = tensor<string, []>("op_5604"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_109_pad_type_0 = const()[name = tensor<string, []>("v_109_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_109_pad_0 = const()[name = tensor<string, []>("v_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(495706624))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496525888))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_109_cast = conv(dilations = var_5604, groups = var_4943, pad = v_109_pad_0, pad_type = v_109_pad_type_0, strides = var_5602, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor<string, []>("v_109_cast")];
            tensor<int32, [4]> var_5608 = const()[name = tensor<string, []>("op_5608"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5609_cast = reshape(shape = var_5608, x = q_109_cast)[name = tensor<string, []>("op_5609_cast")];
            tensor<int32, [4]> var_5610 = const()[name = tensor<string, []>("op_5610"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5611_cast = reshape(shape = var_5610, x = k_109_cast)[name = tensor<string, []>("op_5611_cast")];
            tensor<int32, [4]> var_5612 = const()[name = tensor<string, []>("op_5612"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5613_cast = reshape(shape = var_5612, x = v_109_cast)[name = tensor<string, []>("op_5613_cast")];
            tensor<bool, []> attn_weights_217_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_217_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_217_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_217_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_217_cast = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5609_cast, y = var_5611_cast)[name = tensor<string, []>("attn_weights_217_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_219_cast = mul(x = attn_weights_217_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_219_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_5617_cast = softmax(axis = var_4927, x = attn_weights_219_cast)[name = tensor<string, []>("op_5617_cast")];
            tensor<bool, []> attn_109_transpose_x_0 = const()[name = tensor<string, []>("attn_109_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_109_transpose_y_0 = const()[name = tensor<string, []>("attn_109_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_109_cast = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_5613_cast, y = var_5617_cast)[name = tensor<string, []>("attn_109_cast")];
            tensor<int32, [4]> var_5621 = const()[name = tensor<string, []>("op_5621"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_351_cast = reshape(shape = var_5621, x = attn_109_cast)[name = tensor<string, []>("input_351_cast")];
            tensor<int32, [2]> var_5626 = const()[name = tensor<string, []>("op_5626"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5628 = const()[name = tensor<string, []>("op_5628"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5630_pad_type_0 = const()[name = tensor<string, []>("op_5630_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5630_pad_0 = const()[name = tensor<string, []>("op_5630_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496526016))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497345280))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497345408)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5630_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_5628, groups = var_4943, pad = var_5630_pad_0, pad_type = var_5630_pad_type_0, strides = var_5626, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_351_cast)[name = tensor<string, []>("op_5630_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_165_cast = add(x = var_5630_cast, y = inputs_163_cast)[name = tensor<string, []>("inputs_165_cast")];
            tensor<int32, [1]> var_5634 = const()[name = tensor<string, []>("op_5634"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_165_cast = reduce_mean(axes = var_5634, keep_dims = var_4938, x = inputs_165_cast)[name = tensor<string, []>("channels_mean_165_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_165_cast = sub(x = inputs_165_cast, y = channels_mean_165_cast)[name = tensor<string, []>("zero_mean_165_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_165_cast = mul(x = zero_mean_165_cast, y = zero_mean_165_cast)[name = tensor<string, []>("zero_mean_sq_165_cast")];
            tensor<int32, [1]> var_5638 = const()[name = tensor<string, []>("op_5638"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5639_cast = reduce_mean(axes = var_5638, keep_dims = var_4938, x = zero_mean_sq_165_cast)[name = tensor<string, []>("op_5639_cast")];
            tensor<fp16, []> var_5640_to_fp16 = const()[name = tensor<string, []>("op_5640_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5641_cast = add(x = var_5639_cast, y = var_5640_to_fp16)[name = tensor<string, []>("op_5641_cast")];
            tensor<fp16, []> denom_165_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_165_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_165_cast = rsqrt(epsilon = denom_165_epsilon_0_to_fp16, x = var_5641_cast)[name = tensor<string, []>("denom_165_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_165_cast = mul(x = zero_mean_165_cast, y = denom_165_cast)[name = tensor<string, []>("out_165_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5645_to_fp16 = const()[name = tensor<string, []>("op_5645_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497348032)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5646_cast = add(x = out_165_cast, y = var_5645_to_fp16)[name = tensor<string, []>("op_5646_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5648_to_fp16 = const()[name = tensor<string, []>("op_5648_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497350656)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_229_cast = mul(x = var_5646_cast, y = var_5648_to_fp16)[name = tensor<string, []>("hidden_states_229_cast")];
            tensor<int32, [2]> var_5655 = const()[name = tensor<string, []>("op_5655"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5657 = const()[name = tensor<string, []>("op_5657"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_111_pad_type_0 = const()[name = tensor<string, []>("q_111_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_111_pad_0 = const()[name = tensor<string, []>("q_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497353280))), lut = tensor<fp16, [2]>([-0x1.7b4p-7, 0x1.7a4p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_111_cast = conv(dilations = var_5657, groups = var_4943, pad = q_111_pad_0, pad_type = q_111_pad_type_0, strides = var_5655, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_229_cast)[name = tensor<string, []>("q_111_cast")];
            tensor<int32, [2]> var_5661 = const()[name = tensor<string, []>("op_5661"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5663 = const()[name = tensor<string, []>("op_5663"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_111_pad_type_0 = const()[name = tensor<string, []>("k_111_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_111_pad_0 = const()[name = tensor<string, []>("k_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497558144))), lut = tensor<fp16, [2]>([-0x1.038p-7, 0x1.044p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_111_cast = conv(dilations = var_5663, groups = var_4943, pad = k_111_pad_0, pad_type = k_111_pad_type_0, strides = var_5661, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_111_cast")];
            tensor<int32, [2]> var_5667 = const()[name = tensor<string, []>("op_5667"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5669 = const()[name = tensor<string, []>("op_5669"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_111_pad_type_0 = const()[name = tensor<string, []>("v_111_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_111_pad_0 = const()[name = tensor<string, []>("v_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497885888))), lut = tensor<fp16, [2]>([-0x1.354p-7, 0x1.35cp-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_111_cast = conv(dilations = var_5669, groups = var_4943, pad = v_111_pad_0, pad_type = v_111_pad_type_0, strides = var_5667, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_111_cast")];
            tensor<int32, [4]> var_5673 = const()[name = tensor<string, []>("op_5673"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5674_cast = reshape(shape = var_5673, x = q_111_cast)[name = tensor<string, []>("op_5674_cast")];
            tensor<int32, [4]> var_5675 = const()[name = tensor<string, []>("op_5675"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5676_cast = reshape(shape = var_5675, x = k_111_cast)[name = tensor<string, []>("op_5676_cast")];
            tensor<int32, [4]> var_5677 = const()[name = tensor<string, []>("op_5677"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5678_cast = reshape(shape = var_5677, x = v_111_cast)[name = tensor<string, []>("op_5678_cast")];
            tensor<bool, []> attn_weights_221_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_221_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_221_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_221_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_221_cast = matmul(transpose_x = attn_weights_221_transpose_x_0, transpose_y = attn_weights_221_transpose_y_0, x = var_5674_cast, y = var_5676_cast)[name = tensor<string, []>("attn_weights_221_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_223_cast = mul(x = attn_weights_221_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_223_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_5682_cast = softmax(axis = var_4927, x = attn_weights_223_cast)[name = tensor<string, []>("op_5682_cast")];
            tensor<bool, []> attn_111_transpose_x_0 = const()[name = tensor<string, []>("attn_111_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_111_transpose_y_0 = const()[name = tensor<string, []>("attn_111_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_111_cast = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_5678_cast, y = var_5682_cast)[name = tensor<string, []>("attn_111_cast")];
            tensor<int32, [4]> var_5686 = const()[name = tensor<string, []>("op_5686"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_353_cast = reshape(shape = var_5686, x = attn_111_cast)[name = tensor<string, []>("input_353_cast")];
            tensor<int32, [2]> var_5691 = const()[name = tensor<string, []>("op_5691"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5693 = const()[name = tensor<string, []>("op_5693"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5695_pad_type_0 = const()[name = tensor<string, []>("op_5695_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5695_pad_0 = const()[name = tensor<string, []>("op_5695_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498213632))), lut = tensor<fp16, [2]>([-0x1.5a4p-8, 0x1.5bcp-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498418496)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5695_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_5693, groups = var_4943, pad = var_5695_pad_0, pad_type = var_5695_pad_type_0, strides = var_5691, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_353_cast)[name = tensor<string, []>("op_5695_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_167_cast = add(x = var_5695_cast, y = inputs_165_cast)[name = tensor<string, []>("inputs_167_cast")];
            tensor<int32, [1]> var_5699 = const()[name = tensor<string, []>("op_5699"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_167_cast = reduce_mean(axes = var_5699, keep_dims = var_4938, x = inputs_167_cast)[name = tensor<string, []>("channels_mean_167_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_167_cast = sub(x = inputs_167_cast, y = channels_mean_167_cast)[name = tensor<string, []>("zero_mean_167_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_167_cast = mul(x = zero_mean_167_cast, y = zero_mean_167_cast)[name = tensor<string, []>("zero_mean_sq_167_cast")];
            tensor<int32, [1]> var_5703 = const()[name = tensor<string, []>("op_5703"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5704_cast = reduce_mean(axes = var_5703, keep_dims = var_4938, x = zero_mean_sq_167_cast)[name = tensor<string, []>("op_5704_cast")];
            tensor<fp16, []> var_5705_to_fp16 = const()[name = tensor<string, []>("op_5705_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5706_cast = add(x = var_5704_cast, y = var_5705_to_fp16)[name = tensor<string, []>("op_5706_cast")];
            tensor<fp16, []> denom_167_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_167_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_167_cast = rsqrt(epsilon = denom_167_epsilon_0_to_fp16, x = var_5706_cast)[name = tensor<string, []>("denom_167_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_167_cast = mul(x = zero_mean_167_cast, y = denom_167_cast)[name = tensor<string, []>("out_167_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5710_to_fp16 = const()[name = tensor<string, []>("op_5710_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498421120)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5711_cast = add(x = out_167_cast, y = var_5710_to_fp16)[name = tensor<string, []>("op_5711_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5713_to_fp16 = const()[name = tensor<string, []>("op_5713_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498423744)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_355_cast = mul(x = var_5711_cast, y = var_5713_to_fp16)[name = tensor<string, []>("input_355_cast")];
            tensor<int32, [2]> var_5721 = const()[name = tensor<string, []>("op_5721"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5723 = const()[name = tensor<string, []>("op_5723"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5725_pad_type_0 = const()[name = tensor<string, []>("op_5725_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5725_pad_0 = const()[name = tensor<string, []>("op_5725_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498426368))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504980032))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504980160)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_5725_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_5723, groups = var_4943, pad = var_5725_pad_0, pad_type = var_5725_pad_type_0, strides = var_5721, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_355_cast)[name = tensor<string, []>("op_5725_cast")];
            tensor<int32, [2]> var_5726_split_sizes_0 = const()[name = tensor<string, []>("op_5726_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_5726_axis_0 = const()[name = tensor<string, []>("op_5726_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_5726_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_5726_cast_1 = split(axis = var_5726_axis_0, split_sizes = var_5726_split_sizes_0, x = var_5725_cast)[name = tensor<string, []>("op_5726_cast")];
            tensor<string, []> var_5728_mode_0 = const()[name = tensor<string, []>("op_5728_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_5728_cast = gelu(mode = var_5728_mode_0, x = var_5726_cast_1)[name = tensor<string, []>("op_5728_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_357_cast = mul(x = var_5726_cast_0, y = var_5728_cast)[name = tensor<string, []>("input_357_cast")];
            tensor<int32, [2]> var_5732 = const()[name = tensor<string, []>("op_5732"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5734 = const()[name = tensor<string, []>("op_5734"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5736_pad_type_0 = const()[name = tensor<string, []>("op_5736_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5736_pad_0 = const()[name = tensor<string, []>("op_5736_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505000704))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508277568))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508277696)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5736_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_5734, groups = var_4943, pad = var_5736_pad_0, pad_type = var_5736_pad_type_0, strides = var_5732, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_357_cast)[name = tensor<string, []>("op_5736_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_169_cast = add(x = var_5736_cast, y = inputs_167_cast)[name = tensor<string, []>("inputs_169_cast")];
            tensor<int32, [1]> var_5746 = const()[name = tensor<string, []>("op_5746"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_169_cast = reduce_mean(axes = var_5746, keep_dims = var_4938, x = inputs_169_cast)[name = tensor<string, []>("channels_mean_169_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_169_cast = sub(x = inputs_169_cast, y = channels_mean_169_cast)[name = tensor<string, []>("zero_mean_169_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_169_cast = mul(x = zero_mean_169_cast, y = zero_mean_169_cast)[name = tensor<string, []>("zero_mean_sq_169_cast")];
            tensor<int32, [1]> var_5750 = const()[name = tensor<string, []>("op_5750"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5751_cast = reduce_mean(axes = var_5750, keep_dims = var_4938, x = zero_mean_sq_169_cast)[name = tensor<string, []>("op_5751_cast")];
            tensor<fp16, []> var_5752_to_fp16 = const()[name = tensor<string, []>("op_5752_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5753_cast = add(x = var_5751_cast, y = var_5752_to_fp16)[name = tensor<string, []>("op_5753_cast")];
            tensor<fp16, []> denom_169_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_169_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_169_cast = rsqrt(epsilon = denom_169_epsilon_0_to_fp16, x = var_5753_cast)[name = tensor<string, []>("denom_169_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_169_cast = mul(x = zero_mean_169_cast, y = denom_169_cast)[name = tensor<string, []>("out_169_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5757_to_fp16 = const()[name = tensor<string, []>("op_5757_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508280320)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5758_cast = add(x = out_169_cast, y = var_5757_to_fp16)[name = tensor<string, []>("op_5758_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5760_to_fp16 = const()[name = tensor<string, []>("op_5760_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508282944)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_233_cast = mul(x = var_5758_cast, y = var_5760_to_fp16)[name = tensor<string, []>("hidden_states_233_cast")];
            tensor<int32, [2]> var_5767 = const()[name = tensor<string, []>("op_5767"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5769 = const()[name = tensor<string, []>("op_5769"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_113_pad_type_0 = const()[name = tensor<string, []>("q_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_113_pad_0 = const()[name = tensor<string, []>("q_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508285568))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509104832))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_113_cast = conv(dilations = var_5769, groups = var_4943, pad = q_113_pad_0, pad_type = q_113_pad_type_0, strides = var_5767, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_233_cast)[name = tensor<string, []>("q_113_cast")];
            tensor<int32, [2]> var_5773 = const()[name = tensor<string, []>("op_5773"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5775 = const()[name = tensor<string, []>("op_5775"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_113_pad_type_0 = const()[name = tensor<string, []>("k_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_113_pad_0 = const()[name = tensor<string, []>("k_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509104960))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509924224))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_113_cast = conv(dilations = var_5775, groups = var_4943, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_5773, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_233_cast)[name = tensor<string, []>("k_113_cast")];
            tensor<int32, [2]> var_5779 = const()[name = tensor<string, []>("op_5779"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5781 = const()[name = tensor<string, []>("op_5781"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_113_pad_type_0 = const()[name = tensor<string, []>("v_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_113_pad_0 = const()[name = tensor<string, []>("v_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509924352))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510743616))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_113_cast = conv(dilations = var_5781, groups = var_4943, pad = v_113_pad_0, pad_type = v_113_pad_type_0, strides = var_5779, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_233_cast)[name = tensor<string, []>("v_113_cast")];
            tensor<int32, [4]> var_5785 = const()[name = tensor<string, []>("op_5785"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5786_cast = reshape(shape = var_5785, x = q_113_cast)[name = tensor<string, []>("op_5786_cast")];
            tensor<int32, [4]> var_5787 = const()[name = tensor<string, []>("op_5787"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5788_cast = reshape(shape = var_5787, x = k_113_cast)[name = tensor<string, []>("op_5788_cast")];
            tensor<int32, [4]> var_5789 = const()[name = tensor<string, []>("op_5789"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5790_cast = reshape(shape = var_5789, x = v_113_cast)[name = tensor<string, []>("op_5790_cast")];
            tensor<bool, []> attn_weights_225_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_225_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_225_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_225_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_225_cast = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5786_cast, y = var_5788_cast)[name = tensor<string, []>("attn_weights_225_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_227_cast = mul(x = attn_weights_225_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_227_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_5794_cast = softmax(axis = var_4927, x = attn_weights_227_cast)[name = tensor<string, []>("op_5794_cast")];
            tensor<bool, []> attn_113_transpose_x_0 = const()[name = tensor<string, []>("attn_113_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_113_transpose_y_0 = const()[name = tensor<string, []>("attn_113_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_113_cast = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_5790_cast, y = var_5794_cast)[name = tensor<string, []>("attn_113_cast")];
            tensor<int32, [4]> var_5798 = const()[name = tensor<string, []>("op_5798"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_359_cast = reshape(shape = var_5798, x = attn_113_cast)[name = tensor<string, []>("input_359_cast")];
            tensor<int32, [2]> var_5803 = const()[name = tensor<string, []>("op_5803"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5805 = const()[name = tensor<string, []>("op_5805"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5807_pad_type_0 = const()[name = tensor<string, []>("op_5807_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5807_pad_0 = const()[name = tensor<string, []>("op_5807_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510743744))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511972608))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511972800)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5807_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_5805, groups = var_4943, pad = var_5807_pad_0, pad_type = var_5807_pad_type_0, strides = var_5803, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_359_cast)[name = tensor<string, []>("op_5807_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_171_cast = add(x = var_5807_cast, y = inputs_169_cast)[name = tensor<string, []>("inputs_171_cast")];
            tensor<int32, [1]> var_5811 = const()[name = tensor<string, []>("op_5811"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_171_cast = reduce_mean(axes = var_5811, keep_dims = var_4938, x = inputs_171_cast)[name = tensor<string, []>("channels_mean_171_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_171_cast = sub(x = inputs_171_cast, y = channels_mean_171_cast)[name = tensor<string, []>("zero_mean_171_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_171_cast = mul(x = zero_mean_171_cast, y = zero_mean_171_cast)[name = tensor<string, []>("zero_mean_sq_171_cast")];
            tensor<int32, [1]> var_5815 = const()[name = tensor<string, []>("op_5815"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5816_cast = reduce_mean(axes = var_5815, keep_dims = var_4938, x = zero_mean_sq_171_cast)[name = tensor<string, []>("op_5816_cast")];
            tensor<fp16, []> var_5817_to_fp16 = const()[name = tensor<string, []>("op_5817_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5818_cast = add(x = var_5816_cast, y = var_5817_to_fp16)[name = tensor<string, []>("op_5818_cast")];
            tensor<fp16, []> denom_171_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_171_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_171_cast = rsqrt(epsilon = denom_171_epsilon_0_to_fp16, x = var_5818_cast)[name = tensor<string, []>("denom_171_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_171_cast = mul(x = zero_mean_171_cast, y = denom_171_cast)[name = tensor<string, []>("out_171_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5822_to_fp16 = const()[name = tensor<string, []>("op_5822_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511975424)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5823_cast = add(x = out_171_cast, y = var_5822_to_fp16)[name = tensor<string, []>("op_5823_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5825_to_fp16 = const()[name = tensor<string, []>("op_5825_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511978048)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_235_cast = mul(x = var_5823_cast, y = var_5825_to_fp16)[name = tensor<string, []>("hidden_states_235_cast")];
            tensor<int32, [2]> var_5832 = const()[name = tensor<string, []>("op_5832"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5834 = const()[name = tensor<string, []>("op_5834"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_115_pad_type_0 = const()[name = tensor<string, []>("q_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_115_pad_0 = const()[name = tensor<string, []>("q_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511980672))), lut = tensor<fp16, [2]>([-0x1.6f8p-7, 0x1.6f8p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_115_cast = conv(dilations = var_5834, groups = var_4943, pad = q_115_pad_0, pad_type = q_115_pad_type_0, strides = var_5832, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_235_cast)[name = tensor<string, []>("q_115_cast")];
            tensor<int32, [2]> var_5838 = const()[name = tensor<string, []>("op_5838"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5840 = const()[name = tensor<string, []>("op_5840"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_115_pad_type_0 = const()[name = tensor<string, []>("k_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_115_pad_0 = const()[name = tensor<string, []>("k_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512185536))), lut = tensor<fp16, [2]>([-0x1.e54p-8, 0x1.e5p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_115_cast = conv(dilations = var_5840, groups = var_4943, pad = k_115_pad_0, pad_type = k_115_pad_type_0, strides = var_5838, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_115_cast")];
            tensor<int32, [2]> var_5844 = const()[name = tensor<string, []>("op_5844"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5846 = const()[name = tensor<string, []>("op_5846"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_115_pad_type_0 = const()[name = tensor<string, []>("v_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_115_pad_0 = const()[name = tensor<string, []>("v_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512513280))), lut = tensor<fp16, [2]>([-0x1.1b4p-7, 0x1.1bp-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_115_cast = conv(dilations = var_5846, groups = var_4943, pad = v_115_pad_0, pad_type = v_115_pad_type_0, strides = var_5844, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_115_cast")];
            tensor<int32, [4]> var_5850 = const()[name = tensor<string, []>("op_5850"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5851_cast = reshape(shape = var_5850, x = q_115_cast)[name = tensor<string, []>("op_5851_cast")];
            tensor<int32, [4]> var_5852 = const()[name = tensor<string, []>("op_5852"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5853_cast = reshape(shape = var_5852, x = k_115_cast)[name = tensor<string, []>("op_5853_cast")];
            tensor<int32, [4]> var_5854 = const()[name = tensor<string, []>("op_5854"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_5855_cast = reshape(shape = var_5854, x = v_115_cast)[name = tensor<string, []>("op_5855_cast")];
            tensor<bool, []> attn_weights_229_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_229_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_229_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_229_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_229_cast = matmul(transpose_x = attn_weights_229_transpose_x_0, transpose_y = attn_weights_229_transpose_y_0, x = var_5851_cast, y = var_5853_cast)[name = tensor<string, []>("attn_weights_229_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_231_cast = mul(x = attn_weights_229_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_231_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_5859_cast = softmax(axis = var_4927, x = attn_weights_231_cast)[name = tensor<string, []>("op_5859_cast")];
            tensor<bool, []> attn_115_transpose_x_0 = const()[name = tensor<string, []>("attn_115_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_115_transpose_y_0 = const()[name = tensor<string, []>("attn_115_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_115_cast = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_5855_cast, y = var_5859_cast)[name = tensor<string, []>("attn_115_cast")];
            tensor<int32, [4]> var_5863 = const()[name = tensor<string, []>("op_5863"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_361_cast = reshape(shape = var_5863, x = attn_115_cast)[name = tensor<string, []>("input_361_cast")];
            tensor<int32, [2]> var_5868 = const()[name = tensor<string, []>("op_5868"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5870 = const()[name = tensor<string, []>("op_5870"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5872_pad_type_0 = const()[name = tensor<string, []>("op_5872_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5872_pad_0 = const()[name = tensor<string, []>("op_5872_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512841024))), lut = tensor<fp16, [2]>([-0x1.478p-8, 0x1.47p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513045888)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5872_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_5870, groups = var_4943, pad = var_5872_pad_0, pad_type = var_5872_pad_type_0, strides = var_5868, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_361_cast)[name = tensor<string, []>("op_5872_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_173_cast = add(x = var_5872_cast, y = inputs_171_cast)[name = tensor<string, []>("inputs_173_cast")];
            tensor<int32, [1]> var_5876 = const()[name = tensor<string, []>("op_5876"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_173_cast = reduce_mean(axes = var_5876, keep_dims = var_4938, x = inputs_173_cast)[name = tensor<string, []>("channels_mean_173_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_173_cast = sub(x = inputs_173_cast, y = channels_mean_173_cast)[name = tensor<string, []>("zero_mean_173_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_173_cast = mul(x = zero_mean_173_cast, y = zero_mean_173_cast)[name = tensor<string, []>("zero_mean_sq_173_cast")];
            tensor<int32, [1]> var_5880 = const()[name = tensor<string, []>("op_5880"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5881_cast = reduce_mean(axes = var_5880, keep_dims = var_4938, x = zero_mean_sq_173_cast)[name = tensor<string, []>("op_5881_cast")];
            tensor<fp16, []> var_5882_to_fp16 = const()[name = tensor<string, []>("op_5882_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5883_cast = add(x = var_5881_cast, y = var_5882_to_fp16)[name = tensor<string, []>("op_5883_cast")];
            tensor<fp16, []> denom_173_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_173_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_173_cast = rsqrt(epsilon = denom_173_epsilon_0_to_fp16, x = var_5883_cast)[name = tensor<string, []>("denom_173_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_173_cast = mul(x = zero_mean_173_cast, y = denom_173_cast)[name = tensor<string, []>("out_173_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5887_to_fp16 = const()[name = tensor<string, []>("op_5887_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513048512)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5888_cast = add(x = out_173_cast, y = var_5887_to_fp16)[name = tensor<string, []>("op_5888_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5890_to_fp16 = const()[name = tensor<string, []>("op_5890_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513051136)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_363_cast = mul(x = var_5888_cast, y = var_5890_to_fp16)[name = tensor<string, []>("input_363_cast")];
            tensor<int32, [2]> var_5898 = const()[name = tensor<string, []>("op_5898"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5900 = const()[name = tensor<string, []>("op_5900"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5902_pad_type_0 = const()[name = tensor<string, []>("op_5902_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5902_pad_0 = const()[name = tensor<string, []>("op_5902_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513053760))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519607424))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519607552)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_5902_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_5900, groups = var_4943, pad = var_5902_pad_0, pad_type = var_5902_pad_type_0, strides = var_5898, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_363_cast)[name = tensor<string, []>("op_5902_cast")];
            tensor<int32, [2]> var_5903_split_sizes_0 = const()[name = tensor<string, []>("op_5903_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_5903_axis_0 = const()[name = tensor<string, []>("op_5903_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_5903_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_5903_cast_1 = split(axis = var_5903_axis_0, split_sizes = var_5903_split_sizes_0, x = var_5902_cast)[name = tensor<string, []>("op_5903_cast")];
            tensor<string, []> var_5905_mode_0 = const()[name = tensor<string, []>("op_5905_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_5905_cast = gelu(mode = var_5905_mode_0, x = var_5903_cast_1)[name = tensor<string, []>("op_5905_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_365_cast = mul(x = var_5903_cast_0, y = var_5905_cast)[name = tensor<string, []>("input_365_cast")];
            tensor<int32, [2]> var_5909 = const()[name = tensor<string, []>("op_5909"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5911 = const()[name = tensor<string, []>("op_5911"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5913_pad_type_0 = const()[name = tensor<string, []>("op_5913_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5913_pad_0 = const()[name = tensor<string, []>("op_5913_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519628096))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522904960))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522905088)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5913_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_5911, groups = var_4943, pad = var_5913_pad_0, pad_type = var_5913_pad_type_0, strides = var_5909, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_365_cast)[name = tensor<string, []>("op_5913_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_175_cast = add(x = var_5913_cast, y = inputs_173_cast)[name = tensor<string, []>("inputs_175_cast")];
            tensor<int32, [1]> var_5923 = const()[name = tensor<string, []>("op_5923"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_175_cast = reduce_mean(axes = var_5923, keep_dims = var_4938, x = inputs_175_cast)[name = tensor<string, []>("channels_mean_175_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_175_cast = sub(x = inputs_175_cast, y = channels_mean_175_cast)[name = tensor<string, []>("zero_mean_175_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_175_cast = mul(x = zero_mean_175_cast, y = zero_mean_175_cast)[name = tensor<string, []>("zero_mean_sq_175_cast")];
            tensor<int32, [1]> var_5927 = const()[name = tensor<string, []>("op_5927"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5928_cast = reduce_mean(axes = var_5927, keep_dims = var_4938, x = zero_mean_sq_175_cast)[name = tensor<string, []>("op_5928_cast")];
            tensor<fp16, []> var_5929_to_fp16 = const()[name = tensor<string, []>("op_5929_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5930_cast = add(x = var_5928_cast, y = var_5929_to_fp16)[name = tensor<string, []>("op_5930_cast")];
            tensor<fp16, []> denom_175_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_175_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_175_cast = rsqrt(epsilon = denom_175_epsilon_0_to_fp16, x = var_5930_cast)[name = tensor<string, []>("denom_175_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_175_cast = mul(x = zero_mean_175_cast, y = denom_175_cast)[name = tensor<string, []>("out_175_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5934_to_fp16 = const()[name = tensor<string, []>("op_5934_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522907712)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5935_cast = add(x = out_175_cast, y = var_5934_to_fp16)[name = tensor<string, []>("op_5935_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5937_to_fp16 = const()[name = tensor<string, []>("op_5937_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522910336)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_239_cast = mul(x = var_5935_cast, y = var_5937_to_fp16)[name = tensor<string, []>("hidden_states_239_cast")];
            tensor<int32, [2]> var_5944 = const()[name = tensor<string, []>("op_5944"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5946 = const()[name = tensor<string, []>("op_5946"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_117_pad_type_0 = const()[name = tensor<string, []>("q_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_117_pad_0 = const()[name = tensor<string, []>("q_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522912960))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523732224))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_117_cast = conv(dilations = var_5946, groups = var_4943, pad = q_117_pad_0, pad_type = q_117_pad_type_0, strides = var_5944, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_239_cast)[name = tensor<string, []>("q_117_cast")];
            tensor<int32, [2]> var_5950 = const()[name = tensor<string, []>("op_5950"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5952 = const()[name = tensor<string, []>("op_5952"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_117_pad_type_0 = const()[name = tensor<string, []>("k_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_117_pad_0 = const()[name = tensor<string, []>("k_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523732352))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524551616))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_117_cast = conv(dilations = var_5952, groups = var_4943, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_5950, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_239_cast)[name = tensor<string, []>("k_117_cast")];
            tensor<int32, [2]> var_5956 = const()[name = tensor<string, []>("op_5956"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5958 = const()[name = tensor<string, []>("op_5958"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_117_pad_type_0 = const()[name = tensor<string, []>("v_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_117_pad_0 = const()[name = tensor<string, []>("v_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524551744))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525371008))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_117_cast = conv(dilations = var_5958, groups = var_4943, pad = v_117_pad_0, pad_type = v_117_pad_type_0, strides = var_5956, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_239_cast)[name = tensor<string, []>("v_117_cast")];
            tensor<int32, [4]> var_5962 = const()[name = tensor<string, []>("op_5962"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5963_cast = reshape(shape = var_5962, x = q_117_cast)[name = tensor<string, []>("op_5963_cast")];
            tensor<int32, [4]> var_5964 = const()[name = tensor<string, []>("op_5964"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5965_cast = reshape(shape = var_5964, x = k_117_cast)[name = tensor<string, []>("op_5965_cast")];
            tensor<int32, [4]> var_5966 = const()[name = tensor<string, []>("op_5966"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_5967_cast = reshape(shape = var_5966, x = v_117_cast)[name = tensor<string, []>("op_5967_cast")];
            tensor<bool, []> attn_weights_233_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_233_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_233_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_233_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_233_cast = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5963_cast, y = var_5965_cast)[name = tensor<string, []>("attn_weights_233_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_235_cast = mul(x = attn_weights_233_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_235_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_5971_cast = softmax(axis = var_4927, x = attn_weights_235_cast)[name = tensor<string, []>("op_5971_cast")];
            tensor<bool, []> attn_117_transpose_x_0 = const()[name = tensor<string, []>("attn_117_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_117_transpose_y_0 = const()[name = tensor<string, []>("attn_117_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_117_cast = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_5967_cast, y = var_5971_cast)[name = tensor<string, []>("attn_117_cast")];
            tensor<int32, [4]> var_5975 = const()[name = tensor<string, []>("op_5975"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_367_cast = reshape(shape = var_5975, x = attn_117_cast)[name = tensor<string, []>("input_367_cast")];
            tensor<int32, [2]> var_5980 = const()[name = tensor<string, []>("op_5980"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_5982 = const()[name = tensor<string, []>("op_5982"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_5984_pad_type_0 = const()[name = tensor<string, []>("op_5984_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_5984_pad_0 = const()[name = tensor<string, []>("op_5984_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(525371136))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526190400))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526190528)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_5984_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_5982, groups = var_4943, pad = var_5984_pad_0, pad_type = var_5984_pad_type_0, strides = var_5980, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_367_cast)[name = tensor<string, []>("op_5984_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_177_cast = add(x = var_5984_cast, y = inputs_175_cast)[name = tensor<string, []>("inputs_177_cast")];
            tensor<int32, [1]> var_5988 = const()[name = tensor<string, []>("op_5988"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_177_cast = reduce_mean(axes = var_5988, keep_dims = var_4938, x = inputs_177_cast)[name = tensor<string, []>("channels_mean_177_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_177_cast = sub(x = inputs_177_cast, y = channels_mean_177_cast)[name = tensor<string, []>("zero_mean_177_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_177_cast = mul(x = zero_mean_177_cast, y = zero_mean_177_cast)[name = tensor<string, []>("zero_mean_sq_177_cast")];
            tensor<int32, [1]> var_5992 = const()[name = tensor<string, []>("op_5992"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_5993_cast = reduce_mean(axes = var_5992, keep_dims = var_4938, x = zero_mean_sq_177_cast)[name = tensor<string, []>("op_5993_cast")];
            tensor<fp16, []> var_5994_to_fp16 = const()[name = tensor<string, []>("op_5994_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_5995_cast = add(x = var_5993_cast, y = var_5994_to_fp16)[name = tensor<string, []>("op_5995_cast")];
            tensor<fp16, []> denom_177_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_177_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_177_cast = rsqrt(epsilon = denom_177_epsilon_0_to_fp16, x = var_5995_cast)[name = tensor<string, []>("denom_177_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_177_cast = mul(x = zero_mean_177_cast, y = denom_177_cast)[name = tensor<string, []>("out_177_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_5999_to_fp16 = const()[name = tensor<string, []>("op_5999_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526193152)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6000_cast = add(x = out_177_cast, y = var_5999_to_fp16)[name = tensor<string, []>("op_6000_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6002_to_fp16 = const()[name = tensor<string, []>("op_6002_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526195776)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_241_cast = mul(x = var_6000_cast, y = var_6002_to_fp16)[name = tensor<string, []>("hidden_states_241_cast")];
            tensor<int32, [2]> var_6009 = const()[name = tensor<string, []>("op_6009"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6011 = const()[name = tensor<string, []>("op_6011"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_119_pad_type_0 = const()[name = tensor<string, []>("q_119_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_119_pad_0 = const()[name = tensor<string, []>("q_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526198400))), lut = tensor<fp16, [2]>([-0x1.6a4p-7, 0x1.6ap-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_119_cast = conv(dilations = var_6011, groups = var_4943, pad = q_119_pad_0, pad_type = q_119_pad_type_0, strides = var_6009, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_241_cast)[name = tensor<string, []>("q_119_cast")];
            tensor<int32, [2]> var_6015 = const()[name = tensor<string, []>("op_6015"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6017 = const()[name = tensor<string, []>("op_6017"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_119_pad_type_0 = const()[name = tensor<string, []>("k_119_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_119_pad_0 = const()[name = tensor<string, []>("k_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526403264))), lut = tensor<fp16, [2]>([-0x1.d9cp-8, 0x1.d8p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_119_cast = conv(dilations = var_6017, groups = var_4943, pad = k_119_pad_0, pad_type = k_119_pad_type_0, strides = var_6015, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_119_cast")];
            tensor<int32, [2]> var_6021 = const()[name = tensor<string, []>("op_6021"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6023 = const()[name = tensor<string, []>("op_6023"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_119_pad_type_0 = const()[name = tensor<string, []>("v_119_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_119_pad_0 = const()[name = tensor<string, []>("v_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526731008))), lut = tensor<fp16, [2]>([-0x1.128p-7, 0x1.13p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_119_cast = conv(dilations = var_6023, groups = var_4943, pad = v_119_pad_0, pad_type = v_119_pad_type_0, strides = var_6021, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_119_cast")];
            tensor<int32, [4]> var_6027 = const()[name = tensor<string, []>("op_6027"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6028_cast = reshape(shape = var_6027, x = q_119_cast)[name = tensor<string, []>("op_6028_cast")];
            tensor<int32, [4]> var_6029 = const()[name = tensor<string, []>("op_6029"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6030_cast = reshape(shape = var_6029, x = k_119_cast)[name = tensor<string, []>("op_6030_cast")];
            tensor<int32, [4]> var_6031 = const()[name = tensor<string, []>("op_6031"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6032_cast = reshape(shape = var_6031, x = v_119_cast)[name = tensor<string, []>("op_6032_cast")];
            tensor<bool, []> attn_weights_237_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_237_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_237_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_237_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_237_cast = matmul(transpose_x = attn_weights_237_transpose_x_0, transpose_y = attn_weights_237_transpose_y_0, x = var_6028_cast, y = var_6030_cast)[name = tensor<string, []>("attn_weights_237_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_239_cast = mul(x = attn_weights_237_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_239_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_6036_cast = softmax(axis = var_4927, x = attn_weights_239_cast)[name = tensor<string, []>("op_6036_cast")];
            tensor<bool, []> attn_119_transpose_x_0 = const()[name = tensor<string, []>("attn_119_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_119_transpose_y_0 = const()[name = tensor<string, []>("attn_119_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_119_cast = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_6032_cast, y = var_6036_cast)[name = tensor<string, []>("attn_119_cast")];
            tensor<int32, [4]> var_6040 = const()[name = tensor<string, []>("op_6040"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_369_cast = reshape(shape = var_6040, x = attn_119_cast)[name = tensor<string, []>("input_369_cast")];
            tensor<int32, [2]> var_6045 = const()[name = tensor<string, []>("op_6045"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6047 = const()[name = tensor<string, []>("op_6047"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6049_pad_type_0 = const()[name = tensor<string, []>("op_6049_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6049_pad_0 = const()[name = tensor<string, []>("op_6049_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527058752))), lut = tensor<fp16, [2]>([-0x1.45p-8, 0x1.46p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527263616)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6049_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_6047, groups = var_4943, pad = var_6049_pad_0, pad_type = var_6049_pad_type_0, strides = var_6045, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_369_cast)[name = tensor<string, []>("op_6049_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_179_cast = add(x = var_6049_cast, y = inputs_177_cast)[name = tensor<string, []>("inputs_179_cast")];
            tensor<int32, [1]> var_6053 = const()[name = tensor<string, []>("op_6053"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_179_cast = reduce_mean(axes = var_6053, keep_dims = var_4938, x = inputs_179_cast)[name = tensor<string, []>("channels_mean_179_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_179_cast = sub(x = inputs_179_cast, y = channels_mean_179_cast)[name = tensor<string, []>("zero_mean_179_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_179_cast = mul(x = zero_mean_179_cast, y = zero_mean_179_cast)[name = tensor<string, []>("zero_mean_sq_179_cast")];
            tensor<int32, [1]> var_6057 = const()[name = tensor<string, []>("op_6057"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6058_cast = reduce_mean(axes = var_6057, keep_dims = var_4938, x = zero_mean_sq_179_cast)[name = tensor<string, []>("op_6058_cast")];
            tensor<fp16, []> var_6059_to_fp16 = const()[name = tensor<string, []>("op_6059_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6060_cast = add(x = var_6058_cast, y = var_6059_to_fp16)[name = tensor<string, []>("op_6060_cast")];
            tensor<fp16, []> denom_179_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_179_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_179_cast = rsqrt(epsilon = denom_179_epsilon_0_to_fp16, x = var_6060_cast)[name = tensor<string, []>("denom_179_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_179_cast = mul(x = zero_mean_179_cast, y = denom_179_cast)[name = tensor<string, []>("out_179_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6064_to_fp16 = const()[name = tensor<string, []>("op_6064_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527266240)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6065_cast = add(x = out_179_cast, y = var_6064_to_fp16)[name = tensor<string, []>("op_6065_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6067_to_fp16 = const()[name = tensor<string, []>("op_6067_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527268864)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_371_cast = mul(x = var_6065_cast, y = var_6067_to_fp16)[name = tensor<string, []>("input_371_cast")];
            tensor<int32, [2]> var_6075 = const()[name = tensor<string, []>("op_6075"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6077 = const()[name = tensor<string, []>("op_6077"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6079_pad_type_0 = const()[name = tensor<string, []>("op_6079_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6079_pad_0 = const()[name = tensor<string, []>("op_6079_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527271488))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533825152))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533825280)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_6079_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_6077, groups = var_4943, pad = var_6079_pad_0, pad_type = var_6079_pad_type_0, strides = var_6075, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_371_cast)[name = tensor<string, []>("op_6079_cast")];
            tensor<int32, [2]> var_6080_split_sizes_0 = const()[name = tensor<string, []>("op_6080_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_6080_axis_0 = const()[name = tensor<string, []>("op_6080_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_6080_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_6080_cast_1 = split(axis = var_6080_axis_0, split_sizes = var_6080_split_sizes_0, x = var_6079_cast)[name = tensor<string, []>("op_6080_cast")];
            tensor<string, []> var_6082_mode_0 = const()[name = tensor<string, []>("op_6082_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_6082_cast = gelu(mode = var_6082_mode_0, x = var_6080_cast_1)[name = tensor<string, []>("op_6082_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_373_cast = mul(x = var_6080_cast_0, y = var_6082_cast)[name = tensor<string, []>("input_373_cast")];
            tensor<int32, [2]> var_6086 = const()[name = tensor<string, []>("op_6086"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6088 = const()[name = tensor<string, []>("op_6088"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6090_pad_type_0 = const()[name = tensor<string, []>("op_6090_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6090_pad_0 = const()[name = tensor<string, []>("op_6090_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533845824))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537122688))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537122816)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6090_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_6088, groups = var_4943, pad = var_6090_pad_0, pad_type = var_6090_pad_type_0, strides = var_6086, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_373_cast)[name = tensor<string, []>("op_6090_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_181_cast = add(x = var_6090_cast, y = inputs_179_cast)[name = tensor<string, []>("inputs_181_cast")];
            tensor<int32, [1]> var_6100 = const()[name = tensor<string, []>("op_6100"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_181_cast = reduce_mean(axes = var_6100, keep_dims = var_4938, x = inputs_181_cast)[name = tensor<string, []>("channels_mean_181_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_181_cast = sub(x = inputs_181_cast, y = channels_mean_181_cast)[name = tensor<string, []>("zero_mean_181_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_181_cast = mul(x = zero_mean_181_cast, y = zero_mean_181_cast)[name = tensor<string, []>("zero_mean_sq_181_cast")];
            tensor<int32, [1]> var_6104 = const()[name = tensor<string, []>("op_6104"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6105_cast = reduce_mean(axes = var_6104, keep_dims = var_4938, x = zero_mean_sq_181_cast)[name = tensor<string, []>("op_6105_cast")];
            tensor<fp16, []> var_6106_to_fp16 = const()[name = tensor<string, []>("op_6106_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6107_cast = add(x = var_6105_cast, y = var_6106_to_fp16)[name = tensor<string, []>("op_6107_cast")];
            tensor<fp16, []> denom_181_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_181_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_181_cast = rsqrt(epsilon = denom_181_epsilon_0_to_fp16, x = var_6107_cast)[name = tensor<string, []>("denom_181_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_181_cast = mul(x = zero_mean_181_cast, y = denom_181_cast)[name = tensor<string, []>("out_181_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6111_to_fp16 = const()[name = tensor<string, []>("op_6111_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537125440)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6112_cast = add(x = out_181_cast, y = var_6111_to_fp16)[name = tensor<string, []>("op_6112_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6114_to_fp16 = const()[name = tensor<string, []>("op_6114_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537128064)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_245_cast = mul(x = var_6112_cast, y = var_6114_to_fp16)[name = tensor<string, []>("hidden_states_245_cast")];
            tensor<int32, [2]> var_6121 = const()[name = tensor<string, []>("op_6121"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6123 = const()[name = tensor<string, []>("op_6123"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_121_pad_type_0 = const()[name = tensor<string, []>("q_121_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_121_pad_0 = const()[name = tensor<string, []>("q_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537130688))), lut = tensor<fp16, [4]>([-0x1.29cp-5, -0x1.694p-7, 0x1.64p-7, 0x1.28cp-5]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_121_cast = conv(dilations = var_6123, groups = var_4943, pad = q_121_pad_0, pad_type = q_121_pad_type_0, strides = var_6121, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_245_cast)[name = tensor<string, []>("q_121_cast")];
            tensor<int32, [2]> var_6127 = const()[name = tensor<string, []>("op_6127"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6129 = const()[name = tensor<string, []>("op_6129"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_121_pad_type_0 = const()[name = tensor<string, []>("k_121_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_121_pad_0 = const()[name = tensor<string, []>("k_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537540352))), lut = tensor<fp16, [4]>([-0x1.28p-5, -0x1.644p-7, 0x1.688p-7, 0x1.294p-5]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_121_cast = conv(dilations = var_6129, groups = var_4943, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_6127, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_245_cast)[name = tensor<string, []>("k_121_cast")];
            tensor<int32, [2]> var_6133 = const()[name = tensor<string, []>("op_6133"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6135 = const()[name = tensor<string, []>("op_6135"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_121_pad_type_0 = const()[name = tensor<string, []>("v_121_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_121_pad_0 = const()[name = tensor<string, []>("v_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537950016))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(538769280))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_121_cast = conv(dilations = var_6135, groups = var_4943, pad = v_121_pad_0, pad_type = v_121_pad_type_0, strides = var_6133, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_245_cast)[name = tensor<string, []>("v_121_cast")];
            tensor<int32, [4]> var_6139 = const()[name = tensor<string, []>("op_6139"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6140_cast = reshape(shape = var_6139, x = q_121_cast)[name = tensor<string, []>("op_6140_cast")];
            tensor<int32, [4]> var_6141 = const()[name = tensor<string, []>("op_6141"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6142_cast = reshape(shape = var_6141, x = k_121_cast)[name = tensor<string, []>("op_6142_cast")];
            tensor<int32, [4]> var_6143 = const()[name = tensor<string, []>("op_6143"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6144_cast = reshape(shape = var_6143, x = v_121_cast)[name = tensor<string, []>("op_6144_cast")];
            tensor<bool, []> attn_weights_241_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_241_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_241_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_241_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_241_cast = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_6140_cast, y = var_6142_cast)[name = tensor<string, []>("attn_weights_241_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_243_cast = mul(x = attn_weights_241_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_243_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_6148_cast = softmax(axis = var_4927, x = attn_weights_243_cast)[name = tensor<string, []>("op_6148_cast")];
            tensor<bool, []> attn_121_transpose_x_0 = const()[name = tensor<string, []>("attn_121_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_121_transpose_y_0 = const()[name = tensor<string, []>("attn_121_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_121_cast = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6144_cast, y = var_6148_cast)[name = tensor<string, []>("attn_121_cast")];
            tensor<int32, [4]> var_6152 = const()[name = tensor<string, []>("op_6152"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_375_cast = reshape(shape = var_6152, x = attn_121_cast)[name = tensor<string, []>("input_375_cast")];
            tensor<int32, [2]> var_6157 = const()[name = tensor<string, []>("op_6157"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6159 = const()[name = tensor<string, []>("op_6159"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6161_pad_type_0 = const()[name = tensor<string, []>("op_6161_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6161_pad_0 = const()[name = tensor<string, []>("op_6161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(538769408))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539588672))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539588800)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6161_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_6159, groups = var_4943, pad = var_6161_pad_0, pad_type = var_6161_pad_type_0, strides = var_6157, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_375_cast)[name = tensor<string, []>("op_6161_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_183_cast = add(x = var_6161_cast, y = inputs_181_cast)[name = tensor<string, []>("inputs_183_cast")];
            tensor<int32, [1]> var_6165 = const()[name = tensor<string, []>("op_6165"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_183_cast = reduce_mean(axes = var_6165, keep_dims = var_4938, x = inputs_183_cast)[name = tensor<string, []>("channels_mean_183_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_183_cast = sub(x = inputs_183_cast, y = channels_mean_183_cast)[name = tensor<string, []>("zero_mean_183_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_183_cast = mul(x = zero_mean_183_cast, y = zero_mean_183_cast)[name = tensor<string, []>("zero_mean_sq_183_cast")];
            tensor<int32, [1]> var_6169 = const()[name = tensor<string, []>("op_6169"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6170_cast = reduce_mean(axes = var_6169, keep_dims = var_4938, x = zero_mean_sq_183_cast)[name = tensor<string, []>("op_6170_cast")];
            tensor<fp16, []> var_6171_to_fp16 = const()[name = tensor<string, []>("op_6171_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6172_cast = add(x = var_6170_cast, y = var_6171_to_fp16)[name = tensor<string, []>("op_6172_cast")];
            tensor<fp16, []> denom_183_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_183_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_183_cast = rsqrt(epsilon = denom_183_epsilon_0_to_fp16, x = var_6172_cast)[name = tensor<string, []>("denom_183_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_183_cast = mul(x = zero_mean_183_cast, y = denom_183_cast)[name = tensor<string, []>("out_183_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6176_to_fp16 = const()[name = tensor<string, []>("op_6176_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539591424)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6177_cast = add(x = out_183_cast, y = var_6176_to_fp16)[name = tensor<string, []>("op_6177_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6179_to_fp16 = const()[name = tensor<string, []>("op_6179_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539594048)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_247_cast = mul(x = var_6177_cast, y = var_6179_to_fp16)[name = tensor<string, []>("hidden_states_247_cast")];
            tensor<int32, [2]> var_6186 = const()[name = tensor<string, []>("op_6186"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6188 = const()[name = tensor<string, []>("op_6188"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_123_pad_type_0 = const()[name = tensor<string, []>("q_123_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_123_pad_0 = const()[name = tensor<string, []>("q_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539596672))), lut = tensor<fp16, [2]>([-0x1.664p-7, 0x1.668p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_123_cast = conv(dilations = var_6188, groups = var_4943, pad = q_123_pad_0, pad_type = q_123_pad_type_0, strides = var_6186, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor<string, []>("q_123_cast")];
            tensor<int32, [2]> var_6192 = const()[name = tensor<string, []>("op_6192"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6194 = const()[name = tensor<string, []>("op_6194"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_123_pad_type_0 = const()[name = tensor<string, []>("k_123_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_123_pad_0 = const()[name = tensor<string, []>("k_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539801536))), lut = tensor<fp16, [2]>([-0x1.c18p-8, 0x1.c1p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_123_cast = conv(dilations = var_6194, groups = var_4943, pad = k_123_pad_0, pad_type = k_123_pad_type_0, strides = var_6192, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_123_cast")];
            tensor<int32, [2]> var_6198 = const()[name = tensor<string, []>("op_6198"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6200 = const()[name = tensor<string, []>("op_6200"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_123_pad_type_0 = const()[name = tensor<string, []>("v_123_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_123_pad_0 = const()[name = tensor<string, []>("v_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540129280))), lut = tensor<fp16, [2]>([-0x1.f8cp-8, 0x1.f88p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_123_cast = conv(dilations = var_6200, groups = var_4943, pad = v_123_pad_0, pad_type = v_123_pad_type_0, strides = var_6198, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_123_cast")];
            tensor<int32, [4]> var_6204 = const()[name = tensor<string, []>("op_6204"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6205_cast = reshape(shape = var_6204, x = q_123_cast)[name = tensor<string, []>("op_6205_cast")];
            tensor<int32, [4]> var_6206 = const()[name = tensor<string, []>("op_6206"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6207_cast = reshape(shape = var_6206, x = k_123_cast)[name = tensor<string, []>("op_6207_cast")];
            tensor<int32, [4]> var_6208 = const()[name = tensor<string, []>("op_6208"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6209_cast = reshape(shape = var_6208, x = v_123_cast)[name = tensor<string, []>("op_6209_cast")];
            tensor<bool, []> attn_weights_245_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_245_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_245_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_245_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_245_cast = matmul(transpose_x = attn_weights_245_transpose_x_0, transpose_y = attn_weights_245_transpose_y_0, x = var_6205_cast, y = var_6207_cast)[name = tensor<string, []>("attn_weights_245_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_247_cast = mul(x = attn_weights_245_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_247_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_6213_cast = softmax(axis = var_4927, x = attn_weights_247_cast)[name = tensor<string, []>("op_6213_cast")];
            tensor<bool, []> attn_123_transpose_x_0 = const()[name = tensor<string, []>("attn_123_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_123_transpose_y_0 = const()[name = tensor<string, []>("attn_123_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_123_cast = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6209_cast, y = var_6213_cast)[name = tensor<string, []>("attn_123_cast")];
            tensor<int32, [4]> var_6217 = const()[name = tensor<string, []>("op_6217"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_377_cast = reshape(shape = var_6217, x = attn_123_cast)[name = tensor<string, []>("input_377_cast")];
            tensor<int32, [2]> var_6222 = const()[name = tensor<string, []>("op_6222"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6224 = const()[name = tensor<string, []>("op_6224"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6226_pad_type_0 = const()[name = tensor<string, []>("op_6226_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6226_pad_0 = const()[name = tensor<string, []>("op_6226_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540457024))), lut = tensor<fp16, [2]>([-0x1.35p-8, 0x1.37p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540661888)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6226_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_6224, groups = var_4943, pad = var_6226_pad_0, pad_type = var_6226_pad_type_0, strides = var_6222, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_377_cast)[name = tensor<string, []>("op_6226_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_185_cast = add(x = var_6226_cast, y = inputs_183_cast)[name = tensor<string, []>("inputs_185_cast")];
            tensor<int32, [1]> var_6230 = const()[name = tensor<string, []>("op_6230"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_185_cast = reduce_mean(axes = var_6230, keep_dims = var_4938, x = inputs_185_cast)[name = tensor<string, []>("channels_mean_185_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_185_cast = sub(x = inputs_185_cast, y = channels_mean_185_cast)[name = tensor<string, []>("zero_mean_185_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_185_cast = mul(x = zero_mean_185_cast, y = zero_mean_185_cast)[name = tensor<string, []>("zero_mean_sq_185_cast")];
            tensor<int32, [1]> var_6234 = const()[name = tensor<string, []>("op_6234"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6235_cast = reduce_mean(axes = var_6234, keep_dims = var_4938, x = zero_mean_sq_185_cast)[name = tensor<string, []>("op_6235_cast")];
            tensor<fp16, []> var_6236_to_fp16 = const()[name = tensor<string, []>("op_6236_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6237_cast = add(x = var_6235_cast, y = var_6236_to_fp16)[name = tensor<string, []>("op_6237_cast")];
            tensor<fp16, []> denom_185_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_185_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_185_cast = rsqrt(epsilon = denom_185_epsilon_0_to_fp16, x = var_6237_cast)[name = tensor<string, []>("denom_185_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_185_cast = mul(x = zero_mean_185_cast, y = denom_185_cast)[name = tensor<string, []>("out_185_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6241_to_fp16 = const()[name = tensor<string, []>("op_6241_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540664512)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6242_cast = add(x = out_185_cast, y = var_6241_to_fp16)[name = tensor<string, []>("op_6242_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6244_to_fp16 = const()[name = tensor<string, []>("op_6244_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540667136)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_379_cast = mul(x = var_6242_cast, y = var_6244_to_fp16)[name = tensor<string, []>("input_379_cast")];
            tensor<int32, [2]> var_6252 = const()[name = tensor<string, []>("op_6252"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6254 = const()[name = tensor<string, []>("op_6254"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6256_pad_type_0 = const()[name = tensor<string, []>("op_6256_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6256_pad_0 = const()[name = tensor<string, []>("op_6256_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540669760))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547223424))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547223552)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_6256_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_6254, groups = var_4943, pad = var_6256_pad_0, pad_type = var_6256_pad_type_0, strides = var_6252, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_379_cast)[name = tensor<string, []>("op_6256_cast")];
            tensor<int32, [2]> var_6257_split_sizes_0 = const()[name = tensor<string, []>("op_6257_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_6257_axis_0 = const()[name = tensor<string, []>("op_6257_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_6257_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_6257_cast_1 = split(axis = var_6257_axis_0, split_sizes = var_6257_split_sizes_0, x = var_6256_cast)[name = tensor<string, []>("op_6257_cast")];
            tensor<string, []> var_6259_mode_0 = const()[name = tensor<string, []>("op_6259_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_6259_cast = gelu(mode = var_6259_mode_0, x = var_6257_cast_1)[name = tensor<string, []>("op_6259_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_381_cast = mul(x = var_6257_cast_0, y = var_6259_cast)[name = tensor<string, []>("input_381_cast")];
            tensor<int32, [2]> var_6263 = const()[name = tensor<string, []>("op_6263"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6265 = const()[name = tensor<string, []>("op_6265"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6267_pad_type_0 = const()[name = tensor<string, []>("op_6267_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6267_pad_0 = const()[name = tensor<string, []>("op_6267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547244096))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550520960))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550521088)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6267_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_6265, groups = var_4943, pad = var_6267_pad_0, pad_type = var_6267_pad_type_0, strides = var_6263, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_381_cast)[name = tensor<string, []>("op_6267_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_187_cast = add(x = var_6267_cast, y = inputs_185_cast)[name = tensor<string, []>("inputs_187_cast")];
            tensor<int32, [1]> var_6277 = const()[name = tensor<string, []>("op_6277"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_187_cast = reduce_mean(axes = var_6277, keep_dims = var_4938, x = inputs_187_cast)[name = tensor<string, []>("channels_mean_187_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_187_cast = sub(x = inputs_187_cast, y = channels_mean_187_cast)[name = tensor<string, []>("zero_mean_187_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_187_cast = mul(x = zero_mean_187_cast, y = zero_mean_187_cast)[name = tensor<string, []>("zero_mean_sq_187_cast")];
            tensor<int32, [1]> var_6281 = const()[name = tensor<string, []>("op_6281"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6282_cast = reduce_mean(axes = var_6281, keep_dims = var_4938, x = zero_mean_sq_187_cast)[name = tensor<string, []>("op_6282_cast")];
            tensor<fp16, []> var_6283_to_fp16 = const()[name = tensor<string, []>("op_6283_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6284_cast = add(x = var_6282_cast, y = var_6283_to_fp16)[name = tensor<string, []>("op_6284_cast")];
            tensor<fp16, []> denom_187_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_187_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_187_cast = rsqrt(epsilon = denom_187_epsilon_0_to_fp16, x = var_6284_cast)[name = tensor<string, []>("denom_187_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_187_cast = mul(x = zero_mean_187_cast, y = denom_187_cast)[name = tensor<string, []>("out_187_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6288_to_fp16 = const()[name = tensor<string, []>("op_6288_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550523712)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6289_cast = add(x = out_187_cast, y = var_6288_to_fp16)[name = tensor<string, []>("op_6289_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6291_to_fp16 = const()[name = tensor<string, []>("op_6291_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550526336)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_251_cast = mul(x = var_6289_cast, y = var_6291_to_fp16)[name = tensor<string, []>("hidden_states_251_cast")];
            tensor<int32, [2]> var_6298 = const()[name = tensor<string, []>("op_6298"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6300 = const()[name = tensor<string, []>("op_6300"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_125_pad_type_0 = const()[name = tensor<string, []>("q_125_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_125_pad_0 = const()[name = tensor<string, []>("q_125_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550528960))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551348224))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_125_cast = conv(dilations = var_6300, groups = var_4943, pad = q_125_pad_0, pad_type = q_125_pad_type_0, strides = var_6298, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_251_cast)[name = tensor<string, []>("q_125_cast")];
            tensor<int32, [2]> var_6304 = const()[name = tensor<string, []>("op_6304"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6306 = const()[name = tensor<string, []>("op_6306"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_125_pad_type_0 = const()[name = tensor<string, []>("k_125_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_125_pad_0 = const()[name = tensor<string, []>("k_125_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551348352))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552167616))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_125_cast = conv(dilations = var_6306, groups = var_4943, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_6304, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_251_cast)[name = tensor<string, []>("k_125_cast")];
            tensor<int32, [2]> var_6310 = const()[name = tensor<string, []>("op_6310"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6312 = const()[name = tensor<string, []>("op_6312"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_125_pad_type_0 = const()[name = tensor<string, []>("v_125_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_125_pad_0 = const()[name = tensor<string, []>("v_125_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552167744))), lut = tensor<fp16, [4]>([-0x1.454p-5, -0x1.88p-7, 0x1.864p-7, 0x1.44cp-5]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_125_cast = conv(dilations = var_6312, groups = var_4943, pad = v_125_pad_0, pad_type = v_125_pad_type_0, strides = var_6310, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_251_cast)[name = tensor<string, []>("v_125_cast")];
            tensor<int32, [4]> var_6316 = const()[name = tensor<string, []>("op_6316"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6317_cast = reshape(shape = var_6316, x = q_125_cast)[name = tensor<string, []>("op_6317_cast")];
            tensor<int32, [4]> var_6318 = const()[name = tensor<string, []>("op_6318"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6319_cast = reshape(shape = var_6318, x = k_125_cast)[name = tensor<string, []>("op_6319_cast")];
            tensor<int32, [4]> var_6320 = const()[name = tensor<string, []>("op_6320"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6321_cast = reshape(shape = var_6320, x = v_125_cast)[name = tensor<string, []>("op_6321_cast")];
            tensor<bool, []> attn_weights_249_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_249_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_249_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_249_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_249_cast = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_6317_cast, y = var_6319_cast)[name = tensor<string, []>("attn_weights_249_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_251_cast = mul(x = attn_weights_249_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_251_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_6325_cast = softmax(axis = var_4927, x = attn_weights_251_cast)[name = tensor<string, []>("op_6325_cast")];
            tensor<bool, []> attn_125_transpose_x_0 = const()[name = tensor<string, []>("attn_125_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_125_transpose_y_0 = const()[name = tensor<string, []>("attn_125_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_125_cast = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6321_cast, y = var_6325_cast)[name = tensor<string, []>("attn_125_cast")];
            tensor<int32, [4]> var_6329 = const()[name = tensor<string, []>("op_6329"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_383_cast = reshape(shape = var_6329, x = attn_125_cast)[name = tensor<string, []>("input_383_cast")];
            tensor<int32, [2]> var_6334 = const()[name = tensor<string, []>("op_6334"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6336 = const()[name = tensor<string, []>("op_6336"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6338_pad_type_0 = const()[name = tensor<string, []>("op_6338_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6338_pad_0 = const()[name = tensor<string, []>("op_6338_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552577408))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553396672))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553396800)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6338_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_6336, groups = var_4943, pad = var_6338_pad_0, pad_type = var_6338_pad_type_0, strides = var_6334, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_383_cast)[name = tensor<string, []>("op_6338_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_189_cast = add(x = var_6338_cast, y = inputs_187_cast)[name = tensor<string, []>("inputs_189_cast")];
            tensor<int32, [1]> var_6342 = const()[name = tensor<string, []>("op_6342"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_189_cast = reduce_mean(axes = var_6342, keep_dims = var_4938, x = inputs_189_cast)[name = tensor<string, []>("channels_mean_189_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_189_cast = sub(x = inputs_189_cast, y = channels_mean_189_cast)[name = tensor<string, []>("zero_mean_189_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_189_cast = mul(x = zero_mean_189_cast, y = zero_mean_189_cast)[name = tensor<string, []>("zero_mean_sq_189_cast")];
            tensor<int32, [1]> var_6346 = const()[name = tensor<string, []>("op_6346"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6347_cast = reduce_mean(axes = var_6346, keep_dims = var_4938, x = zero_mean_sq_189_cast)[name = tensor<string, []>("op_6347_cast")];
            tensor<fp16, []> var_6348_to_fp16 = const()[name = tensor<string, []>("op_6348_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6349_cast = add(x = var_6347_cast, y = var_6348_to_fp16)[name = tensor<string, []>("op_6349_cast")];
            tensor<fp16, []> denom_189_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_189_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_189_cast = rsqrt(epsilon = denom_189_epsilon_0_to_fp16, x = var_6349_cast)[name = tensor<string, []>("denom_189_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_189_cast = mul(x = zero_mean_189_cast, y = denom_189_cast)[name = tensor<string, []>("out_189_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6353_to_fp16 = const()[name = tensor<string, []>("op_6353_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553399424)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6354_cast = add(x = out_189_cast, y = var_6353_to_fp16)[name = tensor<string, []>("op_6354_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6356_to_fp16 = const()[name = tensor<string, []>("op_6356_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553402048)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_253_cast = mul(x = var_6354_cast, y = var_6356_to_fp16)[name = tensor<string, []>("hidden_states_253_cast")];
            tensor<int32, [2]> var_6363 = const()[name = tensor<string, []>("op_6363"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6365 = const()[name = tensor<string, []>("op_6365"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_127_pad_type_0 = const()[name = tensor<string, []>("q_127_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_127_pad_0 = const()[name = tensor<string, []>("q_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553404672))), lut = tensor<fp16, [2]>([-0x1.61p-7, 0x1.61p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_127_cast = conv(dilations = var_6365, groups = var_4943, pad = q_127_pad_0, pad_type = q_127_pad_type_0, strides = var_6363, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_253_cast)[name = tensor<string, []>("q_127_cast")];
            tensor<int32, [2]> var_6369 = const()[name = tensor<string, []>("op_6369"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6371 = const()[name = tensor<string, []>("op_6371"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_127_pad_type_0 = const()[name = tensor<string, []>("k_127_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_127_pad_0 = const()[name = tensor<string, []>("k_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553609536))), lut = tensor<fp16, [2]>([-0x1.b7p-8, 0x1.b6cp-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_127_cast = conv(dilations = var_6371, groups = var_4943, pad = k_127_pad_0, pad_type = k_127_pad_type_0, strides = var_6369, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_127_cast")];
            tensor<int32, [2]> var_6375 = const()[name = tensor<string, []>("op_6375"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6377 = const()[name = tensor<string, []>("op_6377"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_127_pad_type_0 = const()[name = tensor<string, []>("v_127_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_127_pad_0 = const()[name = tensor<string, []>("v_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553937280))), lut = tensor<fp16, [2]>([-0x1.e78p-8, 0x1.e74p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_127_cast = conv(dilations = var_6377, groups = var_4943, pad = v_127_pad_0, pad_type = v_127_pad_type_0, strides = var_6375, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_127_cast")];
            tensor<int32, [4]> var_6381 = const()[name = tensor<string, []>("op_6381"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6382_cast = reshape(shape = var_6381, x = q_127_cast)[name = tensor<string, []>("op_6382_cast")];
            tensor<int32, [4]> var_6383 = const()[name = tensor<string, []>("op_6383"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6384_cast = reshape(shape = var_6383, x = k_127_cast)[name = tensor<string, []>("op_6384_cast")];
            tensor<int32, [4]> var_6385 = const()[name = tensor<string, []>("op_6385"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6386_cast = reshape(shape = var_6385, x = v_127_cast)[name = tensor<string, []>("op_6386_cast")];
            tensor<bool, []> attn_weights_253_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_253_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_253_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_253_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_253_cast = matmul(transpose_x = attn_weights_253_transpose_x_0, transpose_y = attn_weights_253_transpose_y_0, x = var_6382_cast, y = var_6384_cast)[name = tensor<string, []>("attn_weights_253_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_255_cast = mul(x = attn_weights_253_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_255_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_6390_cast = softmax(axis = var_4927, x = attn_weights_255_cast)[name = tensor<string, []>("op_6390_cast")];
            tensor<bool, []> attn_127_transpose_x_0 = const()[name = tensor<string, []>("attn_127_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_127_transpose_y_0 = const()[name = tensor<string, []>("attn_127_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_127_cast = matmul(transpose_x = attn_127_transpose_x_0, transpose_y = attn_127_transpose_y_0, x = var_6386_cast, y = var_6390_cast)[name = tensor<string, []>("attn_127_cast")];
            tensor<int32, [4]> var_6394 = const()[name = tensor<string, []>("op_6394"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_385_cast = reshape(shape = var_6394, x = attn_127_cast)[name = tensor<string, []>("input_385_cast")];
            tensor<int32, [2]> var_6399 = const()[name = tensor<string, []>("op_6399"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6401 = const()[name = tensor<string, []>("op_6401"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6403_pad_type_0 = const()[name = tensor<string, []>("op_6403_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6403_pad_0 = const()[name = tensor<string, []>("op_6403_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554265024))), lut = tensor<fp16, [2]>([-0x1.37cp-8, 0x1.364p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554469888)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6403_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_6401, groups = var_4943, pad = var_6403_pad_0, pad_type = var_6403_pad_type_0, strides = var_6399, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_385_cast)[name = tensor<string, []>("op_6403_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_191_cast = add(x = var_6403_cast, y = inputs_189_cast)[name = tensor<string, []>("inputs_191_cast")];
            tensor<int32, [1]> var_6407 = const()[name = tensor<string, []>("op_6407"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_191_cast = reduce_mean(axes = var_6407, keep_dims = var_4938, x = inputs_191_cast)[name = tensor<string, []>("channels_mean_191_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_191_cast = sub(x = inputs_191_cast, y = channels_mean_191_cast)[name = tensor<string, []>("zero_mean_191_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_191_cast = mul(x = zero_mean_191_cast, y = zero_mean_191_cast)[name = tensor<string, []>("zero_mean_sq_191_cast")];
            tensor<int32, [1]> var_6411 = const()[name = tensor<string, []>("op_6411"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6412_cast = reduce_mean(axes = var_6411, keep_dims = var_4938, x = zero_mean_sq_191_cast)[name = tensor<string, []>("op_6412_cast")];
            tensor<fp16, []> var_6413_to_fp16 = const()[name = tensor<string, []>("op_6413_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6414_cast = add(x = var_6412_cast, y = var_6413_to_fp16)[name = tensor<string, []>("op_6414_cast")];
            tensor<fp16, []> denom_191_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_191_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_191_cast = rsqrt(epsilon = denom_191_epsilon_0_to_fp16, x = var_6414_cast)[name = tensor<string, []>("denom_191_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_191_cast = mul(x = zero_mean_191_cast, y = denom_191_cast)[name = tensor<string, []>("out_191_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6418_to_fp16 = const()[name = tensor<string, []>("op_6418_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554472512)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6419_cast = add(x = out_191_cast, y = var_6418_to_fp16)[name = tensor<string, []>("op_6419_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6421_to_fp16 = const()[name = tensor<string, []>("op_6421_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554475136)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_387_cast = mul(x = var_6419_cast, y = var_6421_to_fp16)[name = tensor<string, []>("input_387_cast")];
            tensor<int32, [2]> var_6429 = const()[name = tensor<string, []>("op_6429"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6431 = const()[name = tensor<string, []>("op_6431"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6433_pad_type_0 = const()[name = tensor<string, []>("op_6433_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6433_pad_0 = const()[name = tensor<string, []>("op_6433_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554477760))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561031424))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561031552)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_6433_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_6431, groups = var_4943, pad = var_6433_pad_0, pad_type = var_6433_pad_type_0, strides = var_6429, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_387_cast)[name = tensor<string, []>("op_6433_cast")];
            tensor<int32, [2]> var_6434_split_sizes_0 = const()[name = tensor<string, []>("op_6434_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_6434_axis_0 = const()[name = tensor<string, []>("op_6434_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_6434_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_6434_cast_1 = split(axis = var_6434_axis_0, split_sizes = var_6434_split_sizes_0, x = var_6433_cast)[name = tensor<string, []>("op_6434_cast")];
            tensor<string, []> var_6436_mode_0 = const()[name = tensor<string, []>("op_6436_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_6436_cast = gelu(mode = var_6436_mode_0, x = var_6434_cast_1)[name = tensor<string, []>("op_6436_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_389_cast = mul(x = var_6434_cast_0, y = var_6436_cast)[name = tensor<string, []>("input_389_cast")];
            tensor<int32, [2]> var_6440 = const()[name = tensor<string, []>("op_6440"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6442 = const()[name = tensor<string, []>("op_6442"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6444_pad_type_0 = const()[name = tensor<string, []>("op_6444_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6444_pad_0 = const()[name = tensor<string, []>("op_6444_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561052096))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564328960))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564329088)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6444_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_6442, groups = var_4943, pad = var_6444_pad_0, pad_type = var_6444_pad_type_0, strides = var_6440, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_389_cast)[name = tensor<string, []>("op_6444_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_193_cast = add(x = var_6444_cast, y = inputs_191_cast)[name = tensor<string, []>("inputs_193_cast")];
            tensor<int32, [1]> var_6454 = const()[name = tensor<string, []>("op_6454"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_193_cast = reduce_mean(axes = var_6454, keep_dims = var_4938, x = inputs_193_cast)[name = tensor<string, []>("channels_mean_193_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_193_cast = sub(x = inputs_193_cast, y = channels_mean_193_cast)[name = tensor<string, []>("zero_mean_193_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_193_cast = mul(x = zero_mean_193_cast, y = zero_mean_193_cast)[name = tensor<string, []>("zero_mean_sq_193_cast")];
            tensor<int32, [1]> var_6458 = const()[name = tensor<string, []>("op_6458"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6459_cast = reduce_mean(axes = var_6458, keep_dims = var_4938, x = zero_mean_sq_193_cast)[name = tensor<string, []>("op_6459_cast")];
            tensor<fp16, []> var_6460_to_fp16 = const()[name = tensor<string, []>("op_6460_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6461_cast = add(x = var_6459_cast, y = var_6460_to_fp16)[name = tensor<string, []>("op_6461_cast")];
            tensor<fp16, []> denom_193_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_193_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_193_cast = rsqrt(epsilon = denom_193_epsilon_0_to_fp16, x = var_6461_cast)[name = tensor<string, []>("denom_193_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_193_cast = mul(x = zero_mean_193_cast, y = denom_193_cast)[name = tensor<string, []>("out_193_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6465_to_fp16 = const()[name = tensor<string, []>("op_6465_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564331712)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6466_cast = add(x = out_193_cast, y = var_6465_to_fp16)[name = tensor<string, []>("op_6466_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6468_to_fp16 = const()[name = tensor<string, []>("op_6468_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564334336)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_257_cast = mul(x = var_6466_cast, y = var_6468_to_fp16)[name = tensor<string, []>("hidden_states_257_cast")];
            tensor<int32, [2]> var_6475 = const()[name = tensor<string, []>("op_6475"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6477 = const()[name = tensor<string, []>("op_6477"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_129_pad_type_0 = const()[name = tensor<string, []>("q_129_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_129_pad_0 = const()[name = tensor<string, []>("q_129_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564336960))), lut = tensor<fp16, [4]>([-0x1.32cp-5, -0x1.6ecp-7, 0x1.738p-7, 0x1.338p-5]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_129_cast = conv(dilations = var_6477, groups = var_4943, pad = q_129_pad_0, pad_type = q_129_pad_type_0, strides = var_6475, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_257_cast)[name = tensor<string, []>("q_129_cast")];
            tensor<int32, [2]> var_6481 = const()[name = tensor<string, []>("op_6481"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6483 = const()[name = tensor<string, []>("op_6483"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_129_pad_type_0 = const()[name = tensor<string, []>("k_129_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_129_pad_0 = const()[name = tensor<string, []>("k_129_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564746624))), lut = tensor<fp16, [4]>([-0x1.328p-5, -0x1.71p-7, 0x1.724p-7, 0x1.32cp-5]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_129_cast = conv(dilations = var_6483, groups = var_4943, pad = k_129_pad_0, pad_type = k_129_pad_type_0, strides = var_6481, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_257_cast)[name = tensor<string, []>("k_129_cast")];
            tensor<int32, [2]> var_6487 = const()[name = tensor<string, []>("op_6487"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6489 = const()[name = tensor<string, []>("op_6489"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_129_pad_type_0 = const()[name = tensor<string, []>("v_129_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_129_pad_0 = const()[name = tensor<string, []>("v_129_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(565156288))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(565975552))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_129_cast = conv(dilations = var_6489, groups = var_4943, pad = v_129_pad_0, pad_type = v_129_pad_type_0, strides = var_6487, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_257_cast)[name = tensor<string, []>("v_129_cast")];
            tensor<int32, [4]> var_6493 = const()[name = tensor<string, []>("op_6493"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6494_cast = reshape(shape = var_6493, x = q_129_cast)[name = tensor<string, []>("op_6494_cast")];
            tensor<int32, [4]> var_6495 = const()[name = tensor<string, []>("op_6495"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6496_cast = reshape(shape = var_6495, x = k_129_cast)[name = tensor<string, []>("op_6496_cast")];
            tensor<int32, [4]> var_6497 = const()[name = tensor<string, []>("op_6497"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6498_cast = reshape(shape = var_6497, x = v_129_cast)[name = tensor<string, []>("op_6498_cast")];
            tensor<bool, []> attn_weights_257_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_257_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_257_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_257_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_257_cast = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6494_cast, y = var_6496_cast)[name = tensor<string, []>("attn_weights_257_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_259_cast = mul(x = attn_weights_257_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_259_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_6502_cast = softmax(axis = var_4927, x = attn_weights_259_cast)[name = tensor<string, []>("op_6502_cast")];
            tensor<bool, []> attn_129_transpose_x_0 = const()[name = tensor<string, []>("attn_129_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_129_transpose_y_0 = const()[name = tensor<string, []>("attn_129_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_129_cast = matmul(transpose_x = attn_129_transpose_x_0, transpose_y = attn_129_transpose_y_0, x = var_6498_cast, y = var_6502_cast)[name = tensor<string, []>("attn_129_cast")];
            tensor<int32, [4]> var_6506 = const()[name = tensor<string, []>("op_6506"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_391_cast = reshape(shape = var_6506, x = attn_129_cast)[name = tensor<string, []>("input_391_cast")];
            tensor<int32, [2]> var_6511 = const()[name = tensor<string, []>("op_6511"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6513 = const()[name = tensor<string, []>("op_6513"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6515_pad_type_0 = const()[name = tensor<string, []>("op_6515_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6515_pad_0 = const()[name = tensor<string, []>("op_6515_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(565975680))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566794944))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566795072)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6515_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_6513, groups = var_4943, pad = var_6515_pad_0, pad_type = var_6515_pad_type_0, strides = var_6511, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_391_cast)[name = tensor<string, []>("op_6515_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_195_cast = add(x = var_6515_cast, y = inputs_193_cast)[name = tensor<string, []>("inputs_195_cast")];
            tensor<int32, [1]> var_6519 = const()[name = tensor<string, []>("op_6519"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_195_cast = reduce_mean(axes = var_6519, keep_dims = var_4938, x = inputs_195_cast)[name = tensor<string, []>("channels_mean_195_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_195_cast = sub(x = inputs_195_cast, y = channels_mean_195_cast)[name = tensor<string, []>("zero_mean_195_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_195_cast = mul(x = zero_mean_195_cast, y = zero_mean_195_cast)[name = tensor<string, []>("zero_mean_sq_195_cast")];
            tensor<int32, [1]> var_6523 = const()[name = tensor<string, []>("op_6523"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6524_cast = reduce_mean(axes = var_6523, keep_dims = var_4938, x = zero_mean_sq_195_cast)[name = tensor<string, []>("op_6524_cast")];
            tensor<fp16, []> var_6525_to_fp16 = const()[name = tensor<string, []>("op_6525_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6526_cast = add(x = var_6524_cast, y = var_6525_to_fp16)[name = tensor<string, []>("op_6526_cast")];
            tensor<fp16, []> denom_195_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_195_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_195_cast = rsqrt(epsilon = denom_195_epsilon_0_to_fp16, x = var_6526_cast)[name = tensor<string, []>("denom_195_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_195_cast = mul(x = zero_mean_195_cast, y = denom_195_cast)[name = tensor<string, []>("out_195_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6530_to_fp16 = const()[name = tensor<string, []>("op_6530_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566797696)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6531_cast = add(x = out_195_cast, y = var_6530_to_fp16)[name = tensor<string, []>("op_6531_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6533_to_fp16 = const()[name = tensor<string, []>("op_6533_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566800320)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_259_cast = mul(x = var_6531_cast, y = var_6533_to_fp16)[name = tensor<string, []>("hidden_states_259_cast")];
            tensor<int32, [2]> var_6540 = const()[name = tensor<string, []>("op_6540"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6542 = const()[name = tensor<string, []>("op_6542"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_131_pad_type_0 = const()[name = tensor<string, []>("q_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_131_pad_0 = const()[name = tensor<string, []>("q_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566802944))), lut = tensor<fp16, [2]>([-0x1.60cp-7, 0x1.604p-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_131_cast = conv(dilations = var_6542, groups = var_4943, pad = q_131_pad_0, pad_type = q_131_pad_type_0, strides = var_6540, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_259_cast)[name = tensor<string, []>("q_131_cast")];
            tensor<int32, [2]> var_6546 = const()[name = tensor<string, []>("op_6546"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6548 = const()[name = tensor<string, []>("op_6548"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_131_pad_type_0 = const()[name = tensor<string, []>("k_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_131_pad_0 = const()[name = tensor<string, []>("k_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567007808))), lut = tensor<fp16, [2]>([-0x1.b4p-8, 0x1.b3cp-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_131_cast = conv(dilations = var_6548, groups = var_4943, pad = k_131_pad_0, pad_type = k_131_pad_type_0, strides = var_6546, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_131_cast")];
            tensor<int32, [2]> var_6552 = const()[name = tensor<string, []>("op_6552"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6554 = const()[name = tensor<string, []>("op_6554"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_131_pad_type_0 = const()[name = tensor<string, []>("v_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_131_pad_0 = const()[name = tensor<string, []>("v_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567335552))), lut = tensor<fp16, [2]>([-0x1.e54p-8, 0x1.e38p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_131_cast = conv(dilations = var_6554, groups = var_4943, pad = v_131_pad_0, pad_type = v_131_pad_type_0, strides = var_6552, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_131_cast")];
            tensor<int32, [4]> var_6558 = const()[name = tensor<string, []>("op_6558"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6559_cast = reshape(shape = var_6558, x = q_131_cast)[name = tensor<string, []>("op_6559_cast")];
            tensor<int32, [4]> var_6560 = const()[name = tensor<string, []>("op_6560"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6561_cast = reshape(shape = var_6560, x = k_131_cast)[name = tensor<string, []>("op_6561_cast")];
            tensor<int32, [4]> var_6562 = const()[name = tensor<string, []>("op_6562"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6563_cast = reshape(shape = var_6562, x = v_131_cast)[name = tensor<string, []>("op_6563_cast")];
            tensor<bool, []> attn_weights_261_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_261_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_261_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_261_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_261_cast = matmul(transpose_x = attn_weights_261_transpose_x_0, transpose_y = attn_weights_261_transpose_y_0, x = var_6559_cast, y = var_6561_cast)[name = tensor<string, []>("attn_weights_261_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_263_cast = mul(x = attn_weights_261_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_263_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_6567_cast = softmax(axis = var_4927, x = attn_weights_263_cast)[name = tensor<string, []>("op_6567_cast")];
            tensor<bool, []> attn_131_transpose_x_0 = const()[name = tensor<string, []>("attn_131_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_131_transpose_y_0 = const()[name = tensor<string, []>("attn_131_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_131_cast = matmul(transpose_x = attn_131_transpose_x_0, transpose_y = attn_131_transpose_y_0, x = var_6563_cast, y = var_6567_cast)[name = tensor<string, []>("attn_131_cast")];
            tensor<int32, [4]> var_6571 = const()[name = tensor<string, []>("op_6571"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_393_cast = reshape(shape = var_6571, x = attn_131_cast)[name = tensor<string, []>("input_393_cast")];
            tensor<int32, [2]> var_6576 = const()[name = tensor<string, []>("op_6576"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6578 = const()[name = tensor<string, []>("op_6578"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6580_pad_type_0 = const()[name = tensor<string, []>("op_6580_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6580_pad_0 = const()[name = tensor<string, []>("op_6580_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567663296))), lut = tensor<fp16, [2]>([-0x1.42p-8, 0x1.41cp-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567868160)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6580_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_6578, groups = var_4943, pad = var_6580_pad_0, pad_type = var_6580_pad_type_0, strides = var_6576, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_393_cast)[name = tensor<string, []>("op_6580_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_197_cast = add(x = var_6580_cast, y = inputs_195_cast)[name = tensor<string, []>("inputs_197_cast")];
            tensor<int32, [1]> var_6584 = const()[name = tensor<string, []>("op_6584"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_197_cast = reduce_mean(axes = var_6584, keep_dims = var_4938, x = inputs_197_cast)[name = tensor<string, []>("channels_mean_197_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_197_cast = sub(x = inputs_197_cast, y = channels_mean_197_cast)[name = tensor<string, []>("zero_mean_197_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_197_cast = mul(x = zero_mean_197_cast, y = zero_mean_197_cast)[name = tensor<string, []>("zero_mean_sq_197_cast")];
            tensor<int32, [1]> var_6588 = const()[name = tensor<string, []>("op_6588"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6589_cast = reduce_mean(axes = var_6588, keep_dims = var_4938, x = zero_mean_sq_197_cast)[name = tensor<string, []>("op_6589_cast")];
            tensor<fp16, []> var_6590_to_fp16 = const()[name = tensor<string, []>("op_6590_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6591_cast = add(x = var_6589_cast, y = var_6590_to_fp16)[name = tensor<string, []>("op_6591_cast")];
            tensor<fp16, []> denom_197_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_197_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_197_cast = rsqrt(epsilon = denom_197_epsilon_0_to_fp16, x = var_6591_cast)[name = tensor<string, []>("denom_197_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_197_cast = mul(x = zero_mean_197_cast, y = denom_197_cast)[name = tensor<string, []>("out_197_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6595_to_fp16 = const()[name = tensor<string, []>("op_6595_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567870784)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6596_cast = add(x = out_197_cast, y = var_6595_to_fp16)[name = tensor<string, []>("op_6596_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6598_to_fp16 = const()[name = tensor<string, []>("op_6598_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567873408)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_395_cast = mul(x = var_6596_cast, y = var_6598_to_fp16)[name = tensor<string, []>("input_395_cast")];
            tensor<int32, [2]> var_6606 = const()[name = tensor<string, []>("op_6606"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6608 = const()[name = tensor<string, []>("op_6608"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6610_pad_type_0 = const()[name = tensor<string, []>("op_6610_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6610_pad_0 = const()[name = tensor<string, []>("op_6610_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567876032))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(574429696))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(574429824)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_6610_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_6608, groups = var_4943, pad = var_6610_pad_0, pad_type = var_6610_pad_type_0, strides = var_6606, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_395_cast)[name = tensor<string, []>("op_6610_cast")];
            tensor<int32, [2]> var_6611_split_sizes_0 = const()[name = tensor<string, []>("op_6611_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_6611_axis_0 = const()[name = tensor<string, []>("op_6611_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_6611_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_6611_cast_1 = split(axis = var_6611_axis_0, split_sizes = var_6611_split_sizes_0, x = var_6610_cast)[name = tensor<string, []>("op_6611_cast")];
            tensor<string, []> var_6613_mode_0 = const()[name = tensor<string, []>("op_6613_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_6613_cast = gelu(mode = var_6613_mode_0, x = var_6611_cast_1)[name = tensor<string, []>("op_6613_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_397_cast = mul(x = var_6611_cast_0, y = var_6613_cast)[name = tensor<string, []>("input_397_cast")];
            tensor<int32, [2]> var_6617 = const()[name = tensor<string, []>("op_6617"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6619 = const()[name = tensor<string, []>("op_6619"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6621_pad_type_0 = const()[name = tensor<string, []>("op_6621_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6621_pad_0 = const()[name = tensor<string, []>("op_6621_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(574450368))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577727232))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577727360)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6621_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_6619, groups = var_4943, pad = var_6621_pad_0, pad_type = var_6621_pad_type_0, strides = var_6617, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_397_cast)[name = tensor<string, []>("op_6621_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_199_cast = add(x = var_6621_cast, y = inputs_197_cast)[name = tensor<string, []>("inputs_199_cast")];
            tensor<int32, [1]> var_6631 = const()[name = tensor<string, []>("op_6631"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_199_cast = reduce_mean(axes = var_6631, keep_dims = var_4938, x = inputs_199_cast)[name = tensor<string, []>("channels_mean_199_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_199_cast = sub(x = inputs_199_cast, y = channels_mean_199_cast)[name = tensor<string, []>("zero_mean_199_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_199_cast = mul(x = zero_mean_199_cast, y = zero_mean_199_cast)[name = tensor<string, []>("zero_mean_sq_199_cast")];
            tensor<int32, [1]> var_6635 = const()[name = tensor<string, []>("op_6635"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6636_cast = reduce_mean(axes = var_6635, keep_dims = var_4938, x = zero_mean_sq_199_cast)[name = tensor<string, []>("op_6636_cast")];
            tensor<fp16, []> var_6637_to_fp16 = const()[name = tensor<string, []>("op_6637_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6638_cast = add(x = var_6636_cast, y = var_6637_to_fp16)[name = tensor<string, []>("op_6638_cast")];
            tensor<fp16, []> denom_199_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_199_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_199_cast = rsqrt(epsilon = denom_199_epsilon_0_to_fp16, x = var_6638_cast)[name = tensor<string, []>("denom_199_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_199_cast = mul(x = zero_mean_199_cast, y = denom_199_cast)[name = tensor<string, []>("out_199_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6642_to_fp16 = const()[name = tensor<string, []>("op_6642_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577729984)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6643_cast = add(x = out_199_cast, y = var_6642_to_fp16)[name = tensor<string, []>("op_6643_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6645_to_fp16 = const()[name = tensor<string, []>("op_6645_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577732608)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_263_cast = mul(x = var_6643_cast, y = var_6645_to_fp16)[name = tensor<string, []>("hidden_states_263_cast")];
            tensor<int32, [2]> var_6652 = const()[name = tensor<string, []>("op_6652"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6654 = const()[name = tensor<string, []>("op_6654"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_133_pad_type_0 = const()[name = tensor<string, []>("q_133_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_133_pad_0 = const()[name = tensor<string, []>("q_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577735232))), lut = tensor<fp16, [4]>([-0x1.338p-5, -0x1.718p-7, 0x1.724p-7, 0x1.334p-5]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_133_cast = conv(dilations = var_6654, groups = var_4943, pad = q_133_pad_0, pad_type = q_133_pad_type_0, strides = var_6652, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_263_cast)[name = tensor<string, []>("q_133_cast")];
            tensor<int32, [2]> var_6658 = const()[name = tensor<string, []>("op_6658"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6660 = const()[name = tensor<string, []>("op_6660"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_133_pad_type_0 = const()[name = tensor<string, []>("k_133_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_133_pad_0 = const()[name = tensor<string, []>("k_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578144896))), lut = tensor<fp16, [4]>([-0x1.318p-5, -0x1.6e8p-7, 0x1.734p-7, 0x1.328p-5]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_133_cast = conv(dilations = var_6660, groups = var_4943, pad = k_133_pad_0, pad_type = k_133_pad_type_0, strides = var_6658, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_263_cast)[name = tensor<string, []>("k_133_cast")];
            tensor<int32, [2]> var_6664 = const()[name = tensor<string, []>("op_6664"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6666 = const()[name = tensor<string, []>("op_6666"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_133_pad_type_0 = const()[name = tensor<string, []>("v_133_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_133_pad_0 = const()[name = tensor<string, []>("v_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578554560))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579373824))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_133_cast = conv(dilations = var_6666, groups = var_4943, pad = v_133_pad_0, pad_type = v_133_pad_type_0, strides = var_6664, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_263_cast)[name = tensor<string, []>("v_133_cast")];
            tensor<int32, [4]> var_6670 = const()[name = tensor<string, []>("op_6670"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6671_cast = reshape(shape = var_6670, x = q_133_cast)[name = tensor<string, []>("op_6671_cast")];
            tensor<int32, [4]> var_6672 = const()[name = tensor<string, []>("op_6672"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6673_cast = reshape(shape = var_6672, x = k_133_cast)[name = tensor<string, []>("op_6673_cast")];
            tensor<int32, [4]> var_6674 = const()[name = tensor<string, []>("op_6674"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6675_cast = reshape(shape = var_6674, x = v_133_cast)[name = tensor<string, []>("op_6675_cast")];
            tensor<bool, []> attn_weights_265_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_265_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_265_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_265_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_265_cast = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6671_cast, y = var_6673_cast)[name = tensor<string, []>("attn_weights_265_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_267_cast = mul(x = attn_weights_265_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_267_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_6679_cast = softmax(axis = var_4927, x = attn_weights_267_cast)[name = tensor<string, []>("op_6679_cast")];
            tensor<bool, []> attn_133_transpose_x_0 = const()[name = tensor<string, []>("attn_133_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_133_transpose_y_0 = const()[name = tensor<string, []>("attn_133_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_133_cast = matmul(transpose_x = attn_133_transpose_x_0, transpose_y = attn_133_transpose_y_0, x = var_6675_cast, y = var_6679_cast)[name = tensor<string, []>("attn_133_cast")];
            tensor<int32, [4]> var_6683 = const()[name = tensor<string, []>("op_6683"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_399_cast = reshape(shape = var_6683, x = attn_133_cast)[name = tensor<string, []>("input_399_cast")];
            tensor<int32, [2]> var_6688 = const()[name = tensor<string, []>("op_6688"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6690 = const()[name = tensor<string, []>("op_6690"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6692_pad_type_0 = const()[name = tensor<string, []>("op_6692_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6692_pad_0 = const()[name = tensor<string, []>("op_6692_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579373952))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580193216))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580193344)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6692_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_6690, groups = var_4943, pad = var_6692_pad_0, pad_type = var_6692_pad_type_0, strides = var_6688, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_399_cast)[name = tensor<string, []>("op_6692_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_201_cast = add(x = var_6692_cast, y = inputs_199_cast)[name = tensor<string, []>("inputs_201_cast")];
            tensor<int32, [1]> var_6696 = const()[name = tensor<string, []>("op_6696"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_201_cast = reduce_mean(axes = var_6696, keep_dims = var_4938, x = inputs_201_cast)[name = tensor<string, []>("channels_mean_201_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_201_cast = sub(x = inputs_201_cast, y = channels_mean_201_cast)[name = tensor<string, []>("zero_mean_201_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_201_cast = mul(x = zero_mean_201_cast, y = zero_mean_201_cast)[name = tensor<string, []>("zero_mean_sq_201_cast")];
            tensor<int32, [1]> var_6700 = const()[name = tensor<string, []>("op_6700"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6701_cast = reduce_mean(axes = var_6700, keep_dims = var_4938, x = zero_mean_sq_201_cast)[name = tensor<string, []>("op_6701_cast")];
            tensor<fp16, []> var_6702_to_fp16 = const()[name = tensor<string, []>("op_6702_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6703_cast = add(x = var_6701_cast, y = var_6702_to_fp16)[name = tensor<string, []>("op_6703_cast")];
            tensor<fp16, []> denom_201_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_201_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_201_cast = rsqrt(epsilon = denom_201_epsilon_0_to_fp16, x = var_6703_cast)[name = tensor<string, []>("denom_201_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_201_cast = mul(x = zero_mean_201_cast, y = denom_201_cast)[name = tensor<string, []>("out_201_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6707_to_fp16 = const()[name = tensor<string, []>("op_6707_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580195968)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6708_cast = add(x = out_201_cast, y = var_6707_to_fp16)[name = tensor<string, []>("op_6708_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6710_to_fp16 = const()[name = tensor<string, []>("op_6710_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580198592)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_265_cast = mul(x = var_6708_cast, y = var_6710_to_fp16)[name = tensor<string, []>("hidden_states_265_cast")];
            tensor<int32, [2]> var_6717 = const()[name = tensor<string, []>("op_6717"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6719 = const()[name = tensor<string, []>("op_6719"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_135_pad_type_0 = const()[name = tensor<string, []>("q_135_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_135_pad_0 = const()[name = tensor<string, []>("q_135_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580201216))), lut = tensor<fp16, [2]>([-0x1.5ccp-7, 0x1.5ccp-7]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_135_cast = conv(dilations = var_6719, groups = var_4943, pad = q_135_pad_0, pad_type = q_135_pad_type_0, strides = var_6717, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_265_cast)[name = tensor<string, []>("q_135_cast")];
            tensor<int32, [2]> var_6723 = const()[name = tensor<string, []>("op_6723"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6725 = const()[name = tensor<string, []>("op_6725"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_135_pad_type_0 = const()[name = tensor<string, []>("k_135_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_135_pad_0 = const()[name = tensor<string, []>("k_135_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580406080))), lut = tensor<fp16, [2]>([-0x1.aa4p-8, 0x1.abp-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_135_cast = conv(dilations = var_6725, groups = var_4943, pad = k_135_pad_0, pad_type = k_135_pad_type_0, strides = var_6723, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_135_cast")];
            tensor<int32, [2]> var_6729 = const()[name = tensor<string, []>("op_6729"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6731 = const()[name = tensor<string, []>("op_6731"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_135_pad_type_0 = const()[name = tensor<string, []>("v_135_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_135_pad_0 = const()[name = tensor<string, []>("v_135_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580733824))), lut = tensor<fp16, [2]>([-0x1.cdcp-8, 0x1.cd4p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_135_cast = conv(dilations = var_6731, groups = var_4943, pad = v_135_pad_0, pad_type = v_135_pad_type_0, strides = var_6729, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_135_cast")];
            tensor<int32, [4]> var_6735 = const()[name = tensor<string, []>("op_6735"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_6736_cast = reshape(shape = var_6735, x = q_135_cast)[name = tensor<string, []>("op_6736_cast")];
            tensor<int32, [4]> var_6737 = const()[name = tensor<string, []>("op_6737"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6738_cast = reshape(shape = var_6737, x = k_135_cast)[name = tensor<string, []>("op_6738_cast")];
            tensor<int32, [4]> var_6739 = const()[name = tensor<string, []>("op_6739"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_6740_cast = reshape(shape = var_6739, x = v_135_cast)[name = tensor<string, []>("op_6740_cast")];
            tensor<bool, []> attn_weights_269_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_269_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_269_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_269_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_269_cast = matmul(transpose_x = attn_weights_269_transpose_x_0, transpose_y = attn_weights_269_transpose_y_0, x = var_6736_cast, y = var_6738_cast)[name = tensor<string, []>("attn_weights_269_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_271_cast = mul(x = attn_weights_269_cast, y = var_4934_to_fp16)[name = tensor<string, []>("attn_weights_271_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_6744_cast = softmax(axis = var_4927, x = attn_weights_271_cast)[name = tensor<string, []>("op_6744_cast")];
            tensor<bool, []> attn_135_transpose_x_0 = const()[name = tensor<string, []>("attn_135_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_135_transpose_y_0 = const()[name = tensor<string, []>("attn_135_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_135_cast = matmul(transpose_x = attn_135_transpose_x_0, transpose_y = attn_135_transpose_y_0, x = var_6740_cast, y = var_6744_cast)[name = tensor<string, []>("attn_135_cast")];
            tensor<int32, [4]> var_6748 = const()[name = tensor<string, []>("op_6748"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_401_cast = reshape(shape = var_6748, x = attn_135_cast)[name = tensor<string, []>("input_401_cast")];
            tensor<int32, [2]> var_6753 = const()[name = tensor<string, []>("op_6753"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6755 = const()[name = tensor<string, []>("op_6755"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6757_pad_type_0 = const()[name = tensor<string, []>("op_6757_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6757_pad_0 = const()[name = tensor<string, []>("op_6757_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581061568))), lut = tensor<fp16, [2]>([-0x1.48cp-8, 0x1.49p-8]), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581266432)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6757_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_6755, groups = var_4943, pad = var_6757_pad_0, pad_type = var_6757_pad_type_0, strides = var_6753, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_401_cast)[name = tensor<string, []>("op_6757_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_203_cast = add(x = var_6757_cast, y = inputs_201_cast)[name = tensor<string, []>("inputs_203_cast")];
            tensor<int32, [1]> var_6761 = const()[name = tensor<string, []>("op_6761"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_203_cast = reduce_mean(axes = var_6761, keep_dims = var_4938, x = inputs_203_cast)[name = tensor<string, []>("channels_mean_203_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_203_cast = sub(x = inputs_203_cast, y = channels_mean_203_cast)[name = tensor<string, []>("zero_mean_203_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_203_cast = mul(x = zero_mean_203_cast, y = zero_mean_203_cast)[name = tensor<string, []>("zero_mean_sq_203_cast")];
            tensor<int32, [1]> var_6765 = const()[name = tensor<string, []>("op_6765"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6766_cast = reduce_mean(axes = var_6765, keep_dims = var_4938, x = zero_mean_sq_203_cast)[name = tensor<string, []>("op_6766_cast")];
            tensor<fp16, []> var_6767_to_fp16 = const()[name = tensor<string, []>("op_6767_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6768_cast = add(x = var_6766_cast, y = var_6767_to_fp16)[name = tensor<string, []>("op_6768_cast")];
            tensor<fp16, []> denom_203_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_203_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_203_cast = rsqrt(epsilon = denom_203_epsilon_0_to_fp16, x = var_6768_cast)[name = tensor<string, []>("denom_203_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_203_cast = mul(x = zero_mean_203_cast, y = denom_203_cast)[name = tensor<string, []>("out_203_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6772_to_fp16 = const()[name = tensor<string, []>("op_6772_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581269056)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6773_cast = add(x = out_203_cast, y = var_6772_to_fp16)[name = tensor<string, []>("op_6773_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6775_to_fp16 = const()[name = tensor<string, []>("op_6775_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581271680)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_403_cast = mul(x = var_6773_cast, y = var_6775_to_fp16)[name = tensor<string, []>("input_403_cast")];
            tensor<int32, [2]> var_6783 = const()[name = tensor<string, []>("op_6783"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6785 = const()[name = tensor<string, []>("op_6785"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6787_pad_type_0 = const()[name = tensor<string, []>("op_6787_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6787_pad_0 = const()[name = tensor<string, []>("op_6787_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581274304))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587827968))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587828096)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_6787_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_6785, groups = var_4943, pad = var_6787_pad_0, pad_type = var_6787_pad_type_0, strides = var_6783, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_403_cast)[name = tensor<string, []>("op_6787_cast")];
            tensor<int32, [2]> var_6788_split_sizes_0 = const()[name = tensor<string, []>("op_6788_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_6788_axis_0 = const()[name = tensor<string, []>("op_6788_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_6788_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_6788_cast_1 = split(axis = var_6788_axis_0, split_sizes = var_6788_split_sizes_0, x = var_6787_cast)[name = tensor<string, []>("op_6788_cast")];
            tensor<string, []> var_6790_mode_0 = const()[name = tensor<string, []>("op_6790_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_6790_cast = gelu(mode = var_6790_mode_0, x = var_6788_cast_1)[name = tensor<string, []>("op_6790_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_405_cast = mul(x = var_6788_cast_0, y = var_6790_cast)[name = tensor<string, []>("input_405_cast")];
            tensor<int32, [2]> var_6794 = const()[name = tensor<string, []>("op_6794"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6796 = const()[name = tensor<string, []>("op_6796"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_6798_pad_type_0 = const()[name = tensor<string, []>("op_6798_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_6798_pad_0 = const()[name = tensor<string, []>("op_6798_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(587848640))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591125504))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591125632)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6798_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_6796, groups = var_4943, pad = var_6798_pad_0, pad_type = var_6798_pad_type_0, strides = var_6794, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_405_cast)[name = tensor<string, []>("op_6798_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_269_cast = add(x = var_6798_cast, y = inputs_203_cast)[name = tensor<string, []>("hidden_states_269_cast")];
            tensor<int32, [4]> var_6800 = const()[name = tensor<string, []>("op_6800"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> input_407_cast = reshape(shape = var_6800, x = hidden_states_269_cast)[name = tensor<string, []>("input_407_cast")];
            tensor<int32, [2]> var_6804 = const()[name = tensor<string, []>("op_6804"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6806 = const()[name = tensor<string, []>("op_6806"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_271_pad_type_0 = const()[name = tensor<string, []>("hidden_states_271_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_271_pad_0 = const()[name = tensor<string, []>("hidden_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591128256))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591947520))), name = tensor<string, []>("mid_block_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591947648)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_271_cast = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_6806, groups = var_4943, pad = hidden_states_271_pad_0, pad_type = hidden_states_271_pad_type_0, strides = var_6804, weight = mid_block_attentions_0_proj_out_weight_to_fp16_palettized, x = input_407_cast)[name = tensor<string, []>("hidden_states_271_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_409_cast = add(x = hidden_states_271_cast, y = hidden_states_205_cast)[name = tensor<string, []>("input_409_cast")];
            tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_409_cast)[name = tensor<string, []>("reshape_76_cast")];
            tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = tensor<string, []>("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_57_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_57_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_57_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor<string, []>("reduce_mean_57_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor<string, []>("sub_38_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_19_cast = square(x = sub_38_cast)[name = tensor<string, []>("square_19_cast")];
            tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = tensor<string, []>("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_59_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_59_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_59_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor<string, []>("reduce_mean_59_cast")];
            tensor<fp16, []> add_38_y_0_to_fp16 = const()[name = tensor<string, []>("add_38_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor<string, []>("add_38_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor<string, []>("sqrt_19_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor<string, []>("real_div_19_cast")];
            tensor<int32, [4]> reshape_77_shape_0 = const()[name = tensor<string, []>("reshape_77_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor<string, []>("reshape_77_cast")];
            tensor<fp16, [1280]> add_39_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_39_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591950272)))];
            tensor<fp16, [1280]> add_39_beta_0_to_fp16 = const()[name = tensor<string, []>("add_39_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591952896)))];
            tensor<fp16, []> add_39_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_39_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_39_cast = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_77_cast)[name = tensor<string, []>("add_39_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_413_cast = silu(x = add_39_cast)[name = tensor<string, []>("input_413_cast")];
            tensor<int32, [2]> var_6821 = const()[name = tensor<string, []>("op_6821"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6823 = const()[name = tensor<string, []>("op_6823"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_273_pad_type_0 = const()[name = tensor<string, []>("hidden_states_273_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = tensor<string, []>("hidden_states_273_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591955520))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603014784))), name = tensor<string, []>("mid_block_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603014976)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_273_cast = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_6823, groups = var_4943, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = var_6821, weight = mid_block_resnets_1_conv1_weight_to_fp16_palettized, x = input_413_cast)[name = tensor<string, []>("hidden_states_273_cast")];
            tensor<int32, [2]> var_6829 = const()[name = tensor<string, []>("op_6829"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6831 = const()[name = tensor<string, []>("op_6831"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_15_pad_type_0 = const()[name = tensor<string, []>("temb_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_15_pad_0 = const()[name = tensor<string, []>("temb_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603017600))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603836864))), name = tensor<string, []>("mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603836992)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_15_cast = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_6831, groups = var_4943, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_6829, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_15_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_417_cast = add(x = hidden_states_273_cast, y = temb_15_cast)[name = tensor<string, []>("input_417_cast")];
            tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_417_cast)[name = tensor<string, []>("reshape_80_cast")];
            tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = tensor<string, []>("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_60_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_60_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_60_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor<string, []>("reduce_mean_60_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor<string, []>("sub_40_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_20_cast = square(x = sub_40_cast)[name = tensor<string, []>("square_20_cast")];
            tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = tensor<string, []>("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_62_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_62_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_62_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor<string, []>("reduce_mean_62_cast")];
            tensor<fp16, []> add_40_y_0_to_fp16 = const()[name = tensor<string, []>("add_40_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor<string, []>("add_40_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor<string, []>("sqrt_20_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor<string, []>("real_div_20_cast")];
            tensor<int32, [4]> reshape_81_shape_0 = const()[name = tensor<string, []>("reshape_81_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor<string, []>("reshape_81_cast")];
            tensor<fp16, [1280]> add_41_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_41_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603839616)))];
            tensor<fp16, [1280]> add_41_beta_0_to_fp16 = const()[name = tensor<string, []>("add_41_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603842240)))];
            tensor<fp16, []> add_41_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_41_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_41_cast = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_81_cast)[name = tensor<string, []>("add_41_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_421_cast = silu(x = add_41_cast)[name = tensor<string, []>("input_421_cast")];
            tensor<int32, [2]> var_6841 = const()[name = tensor<string, []>("op_6841"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6843 = const()[name = tensor<string, []>("op_6843"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_275_pad_type_0 = const()[name = tensor<string, []>("hidden_states_275_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_275_pad_0 = const()[name = tensor<string, []>("hidden_states_275_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603844864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614904128))), name = tensor<string, []>("mid_block_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614904320)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_275_cast = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_6843, groups = var_4943, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_6841, weight = mid_block_resnets_1_conv2_weight_to_fp16_palettized, x = input_421_cast)[name = tensor<string, []>("hidden_states_275_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_277_cast = add(x = input_409_cast, y = hidden_states_275_cast)[name = tensor<string, []>("hidden_states_277_cast")];
            tensor<int32, []> var_6849 = const()[name = tensor<string, []>("op_6849"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_6860 = const()[name = tensor<string, []>("op_6860"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_6865 = const()[name = tensor<string, []>("op_6865"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_423_interleave_0 = const()[name = tensor<string, []>("input_423_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 32, 32]> input_423_cast = concat(axis = var_6865, interleave = input_423_interleave_0, values = (hidden_states_277_cast, input_311_cast))[name = tensor<string, []>("input_423_cast")];
            tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 32, 32])];
            tensor<fp16, [2, 32, 80, 32, 32]> reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_423_cast)[name = tensor<string, []>("reshape_84_cast")];
            tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = tensor<string, []>("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_63_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_63_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_63_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor<string, []>("reduce_mean_63_cast")];
            tensor<fp16, [2, 32, 80, 32, 32]> sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor<string, []>("sub_42_cast")];
            tensor<fp16, [2, 32, 80, 32, 32]> square_21_cast = square(x = sub_42_cast)[name = tensor<string, []>("square_21_cast")];
            tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = tensor<string, []>("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_65_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_65_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_65_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor<string, []>("reduce_mean_65_cast")];
            tensor<fp16, []> add_42_y_0_to_fp16 = const()[name = tensor<string, []>("add_42_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor<string, []>("add_42_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor<string, []>("sqrt_21_cast")];
            tensor<fp16, [2, 32, 80, 32, 32]> real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor<string, []>("real_div_21_cast")];
            tensor<int32, [4]> reshape_85_shape_0 = const()[name = tensor<string, []>("reshape_85_shape_0"), val = tensor<int32, [4]>([2, 2560, 32, 32])];
            tensor<fp16, [2, 2560, 32, 32]> reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor<string, []>("reshape_85_cast")];
            tensor<fp16, [2560]> add_43_mean_0_to_fp16 = const()[name = tensor<string, []>("add_43_mean_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614906944)))];
            tensor<fp16, [2560]> add_43_variance_0_to_fp16 = const()[name = tensor<string, []>("add_43_variance_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614912128)))];
            tensor<fp16, [2560]> add_43_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_43_gamma_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614917312)))];
            tensor<fp16, [2560]> add_43_beta_0_to_fp16 = const()[name = tensor<string, []>("add_43_beta_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614922496)))];
            tensor<fp16, []> add_43_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_43_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 2560, 32, 32]> add_43_cast = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_85_cast)[name = tensor<string, []>("add_43_cast")];
            tensor<fp16, [2, 2560, 32, 32]> input_427_cast = silu(x = add_43_cast)[name = tensor<string, []>("input_427_cast")];
            tensor<int32, [2]> var_6894 = const()[name = tensor<string, []>("op_6894"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6896 = const()[name = tensor<string, []>("op_6896"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_279_pad_type_0 = const()[name = tensor<string, []>("hidden_states_279_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_279_pad_0 = const()[name = tensor<string, []>("hidden_states_279_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 2560, 3, 3]> up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [22118400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614927680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637046144))), name = tensor<string, []>("up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 3, 3])];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637046336)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_279_cast = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_6896, groups = var_6865, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_6894, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_427_cast)[name = tensor<string, []>("hidden_states_279_cast")];
            tensor<int32, [2]> var_6902 = const()[name = tensor<string, []>("op_6902"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6904 = const()[name = tensor<string, []>("op_6904"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_17_pad_type_0 = const()[name = tensor<string, []>("temb_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_17_pad_0 = const()[name = tensor<string, []>("temb_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637048960))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637868224))), name = tensor<string, []>("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637868352)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_17_cast = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_6904, groups = var_6865, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_6902, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_17_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_431_cast = add(x = hidden_states_279_cast, y = temb_17_cast)[name = tensor<string, []>("input_431_cast")];
            tensor<int32, [5]> reshape_88_shape_0 = const()[name = tensor<string, []>("reshape_88_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_431_cast)[name = tensor<string, []>("reshape_88_cast")];
            tensor<int32, [3]> reduce_mean_66_axes_0 = const()[name = tensor<string, []>("reduce_mean_66_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_66_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_66_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_66_cast = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast)[name = tensor<string, []>("reduce_mean_66_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor<string, []>("sub_44_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_22_cast = square(x = sub_44_cast)[name = tensor<string, []>("square_22_cast")];
            tensor<int32, [3]> reduce_mean_68_axes_0 = const()[name = tensor<string, []>("reduce_mean_68_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_68_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_68_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_68_cast = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast)[name = tensor<string, []>("reduce_mean_68_cast")];
            tensor<fp16, []> add_44_y_0_to_fp16 = const()[name = tensor<string, []>("add_44_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_44_cast = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16)[name = tensor<string, []>("add_44_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_22_cast = sqrt(x = add_44_cast)[name = tensor<string, []>("sqrt_22_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast)[name = tensor<string, []>("real_div_22_cast")];
            tensor<int32, [4]> reshape_89_shape_0 = const()[name = tensor<string, []>("reshape_89_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast)[name = tensor<string, []>("reshape_89_cast")];
            tensor<fp16, [1280]> add_45_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_45_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637870976)))];
            tensor<fp16, [1280]> add_45_beta_0_to_fp16 = const()[name = tensor<string, []>("add_45_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637873600)))];
            tensor<fp16, []> add_45_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_45_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_45_cast = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_89_cast)[name = tensor<string, []>("add_45_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_435_cast = silu(x = add_45_cast)[name = tensor<string, []>("input_435_cast")];
            tensor<int32, [2]> var_6914 = const()[name = tensor<string, []>("op_6914"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6916 = const()[name = tensor<string, []>("op_6916"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_281_pad_type_0 = const()[name = tensor<string, []>("hidden_states_281_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_281_pad_0 = const()[name = tensor<string, []>("hidden_states_281_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637876224))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648935488))), name = tensor<string, []>("up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648935680)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_281_cast = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_6916, groups = var_6865, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_6914, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_435_cast)[name = tensor<string, []>("hidden_states_281_cast")];
            tensor<int32, [2]> var_6921 = const()[name = tensor<string, []>("op_6921"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6923 = const()[name = tensor<string, []>("op_6923"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_5_pad_type_0 = const()[name = tensor<string, []>("x_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_5_pad_0 = const()[name = tensor<string, []>("x_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2560, 1, 1]> up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648938304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651395968))), name = tensor<string, []>("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651396160)))];
            tensor<fp16, [2, 1280, 32, 32]> x_5_cast = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_6923, groups = var_6865, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_6921, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_423_cast)[name = tensor<string, []>("x_5_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_283_cast = add(x = x_5_cast, y = hidden_states_281_cast)[name = tensor<string, []>("hidden_states_283_cast")];
            tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_92_cast = reshape(shape = reshape_92_shape_0, x = hidden_states_283_cast)[name = tensor<string, []>("reshape_92_cast")];
            tensor<int32, [3]> reduce_mean_69_axes_0 = const()[name = tensor<string, []>("reduce_mean_69_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_69_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_69_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_69_cast = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast)[name = tensor<string, []>("reduce_mean_69_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor<string, []>("sub_46_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_23_cast = square(x = sub_46_cast)[name = tensor<string, []>("square_23_cast")];
            tensor<int32, [3]> reduce_mean_71_axes_0 = const()[name = tensor<string, []>("reduce_mean_71_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_71_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_71_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_71_cast = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast)[name = tensor<string, []>("reduce_mean_71_cast")];
            tensor<fp16, []> add_46_y_0_to_fp16 = const()[name = tensor<string, []>("add_46_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_46_cast = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16)[name = tensor<string, []>("add_46_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_23_cast = sqrt(x = add_46_cast)[name = tensor<string, []>("sqrt_23_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast)[name = tensor<string, []>("real_div_23_cast")];
            tensor<int32, [4]> reshape_93_shape_0 = const()[name = tensor<string, []>("reshape_93_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast)[name = tensor<string, []>("reshape_93_cast")];
            tensor<fp16, [1280]> add_47_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_47_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651398784)))];
            tensor<fp16, [1280]> add_47_beta_0_to_fp16 = const()[name = tensor<string, []>("add_47_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651401408)))];
            tensor<fp16, []> add_47_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_47_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_47_cast = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_93_cast)[name = tensor<string, []>("add_47_cast")];
            tensor<int32, [2]> var_6961 = const()[name = tensor<string, []>("op_6961"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_6963 = const()[name = tensor<string, []>("op_6963"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_285_pad_type_0 = const()[name = tensor<string, []>("hidden_states_285_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_285_pad_0 = const()[name = tensor<string, []>("hidden_states_285_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651404032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652632896))), name = tensor<string, []>("up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652633088)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_285_cast = conv(bias = up_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_6963, groups = var_6865, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_6961, weight = up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized, x = add_47_cast)[name = tensor<string, []>("hidden_states_285_cast")];
            tensor<int32, [4]> var_6968 = const()[name = tensor<string, []>("op_6968"), val = tensor<int32, [4]>([2, 1280, 1, 1024])];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_205_cast = reshape(shape = var_6968, x = hidden_states_285_cast)[name = tensor<string, []>("inputs_205_cast")];
            tensor<int32, [1]> var_6978 = const()[name = tensor<string, []>("op_6978"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_205_cast = reduce_mean(axes = var_6978, keep_dims = var_6860, x = inputs_205_cast)[name = tensor<string, []>("channels_mean_205_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_205_cast = sub(x = inputs_205_cast, y = channels_mean_205_cast)[name = tensor<string, []>("zero_mean_205_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_205_cast = mul(x = zero_mean_205_cast, y = zero_mean_205_cast)[name = tensor<string, []>("zero_mean_sq_205_cast")];
            tensor<int32, [1]> var_6982 = const()[name = tensor<string, []>("op_6982"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_6983_cast = reduce_mean(axes = var_6982, keep_dims = var_6860, x = zero_mean_sq_205_cast)[name = tensor<string, []>("op_6983_cast")];
            tensor<fp16, []> var_6984_to_fp16 = const()[name = tensor<string, []>("op_6984_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_6985_cast = add(x = var_6983_cast, y = var_6984_to_fp16)[name = tensor<string, []>("op_6985_cast")];
            tensor<fp16, []> denom_205_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_205_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_205_cast = rsqrt(epsilon = denom_205_epsilon_0_to_fp16, x = var_6985_cast)[name = tensor<string, []>("denom_205_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_205_cast = mul(x = zero_mean_205_cast, y = denom_205_cast)[name = tensor<string, []>("out_205_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6989_to_fp16 = const()[name = tensor<string, []>("op_6989_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652635712)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_6990_cast = add(x = out_205_cast, y = var_6989_to_fp16)[name = tensor<string, []>("op_6990_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_6992_to_fp16 = const()[name = tensor<string, []>("op_6992_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652638336)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_287_cast = mul(x = var_6990_cast, y = var_6992_to_fp16)[name = tensor<string, []>("hidden_states_287_cast")];
            tensor<int32, [2]> var_6999 = const()[name = tensor<string, []>("op_6999"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7001 = const()[name = tensor<string, []>("op_7001"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_137_pad_type_0 = const()[name = tensor<string, []>("q_137_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_137_pad_0 = const()[name = tensor<string, []>("q_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652640960))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(653460224))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_137_cast = conv(dilations = var_7001, groups = var_6865, pad = q_137_pad_0, pad_type = q_137_pad_type_0, strides = var_6999, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor<string, []>("q_137_cast")];
            tensor<int32, [2]> var_7005 = const()[name = tensor<string, []>("op_7005"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7007 = const()[name = tensor<string, []>("op_7007"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_137_pad_type_0 = const()[name = tensor<string, []>("k_137_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_137_pad_0 = const()[name = tensor<string, []>("k_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(653460352))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(654279616))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_137_cast = conv(dilations = var_7007, groups = var_6865, pad = k_137_pad_0, pad_type = k_137_pad_type_0, strides = var_7005, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor<string, []>("k_137_cast")];
            tensor<int32, [2]> var_7011 = const()[name = tensor<string, []>("op_7011"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7013 = const()[name = tensor<string, []>("op_7013"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_137_pad_type_0 = const()[name = tensor<string, []>("v_137_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_137_pad_0 = const()[name = tensor<string, []>("v_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(654279744))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(655099008))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_137_cast = conv(dilations = var_7013, groups = var_6865, pad = v_137_pad_0, pad_type = v_137_pad_type_0, strides = var_7011, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor<string, []>("v_137_cast")];
            tensor<int32, [4]> var_7017 = const()[name = tensor<string, []>("op_7017"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7018_cast = reshape(shape = var_7017, x = q_137_cast)[name = tensor<string, []>("op_7018_cast")];
            tensor<int32, [4]> var_7019 = const()[name = tensor<string, []>("op_7019"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7020_cast = reshape(shape = var_7019, x = k_137_cast)[name = tensor<string, []>("op_7020_cast")];
            tensor<int32, [4]> var_7021 = const()[name = tensor<string, []>("op_7021"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7022_cast = reshape(shape = var_7021, x = v_137_cast)[name = tensor<string, []>("op_7022_cast")];
            tensor<bool, []> attn_weights_273_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_273_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_273_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_273_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_273_cast = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_7018_cast, y = var_7020_cast)[name = tensor<string, []>("attn_weights_273_cast")];
            tensor<fp16, []> var_6856_to_fp16 = const()[name = tensor<string, []>("op_6856_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_275_cast = mul(x = attn_weights_273_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_275_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_7026_cast = softmax(axis = var_6849, x = attn_weights_275_cast)[name = tensor<string, []>("op_7026_cast")];
            tensor<bool, []> attn_137_transpose_x_0 = const()[name = tensor<string, []>("attn_137_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_137_transpose_y_0 = const()[name = tensor<string, []>("attn_137_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_137_cast = matmul(transpose_x = attn_137_transpose_x_0, transpose_y = attn_137_transpose_y_0, x = var_7022_cast, y = var_7026_cast)[name = tensor<string, []>("attn_137_cast")];
            tensor<int32, [4]> var_7030 = const()[name = tensor<string, []>("op_7030"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_439_cast = reshape(shape = var_7030, x = attn_137_cast)[name = tensor<string, []>("input_439_cast")];
            tensor<int32, [2]> var_7035 = const()[name = tensor<string, []>("op_7035"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7037 = const()[name = tensor<string, []>("op_7037"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7039_pad_type_0 = const()[name = tensor<string, []>("op_7039_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7039_pad_0 = const()[name = tensor<string, []>("op_7039_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(655099136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656328000))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656328192)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7039_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_7037, groups = var_6865, pad = var_7039_pad_0, pad_type = var_7039_pad_type_0, strides = var_7035, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_439_cast)[name = tensor<string, []>("op_7039_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_207_cast = add(x = var_7039_cast, y = inputs_205_cast)[name = tensor<string, []>("inputs_207_cast")];
            tensor<int32, [1]> var_7043 = const()[name = tensor<string, []>("op_7043"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_207_cast = reduce_mean(axes = var_7043, keep_dims = var_6860, x = inputs_207_cast)[name = tensor<string, []>("channels_mean_207_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_207_cast = sub(x = inputs_207_cast, y = channels_mean_207_cast)[name = tensor<string, []>("zero_mean_207_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_207_cast = mul(x = zero_mean_207_cast, y = zero_mean_207_cast)[name = tensor<string, []>("zero_mean_sq_207_cast")];
            tensor<int32, [1]> var_7047 = const()[name = tensor<string, []>("op_7047"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7048_cast = reduce_mean(axes = var_7047, keep_dims = var_6860, x = zero_mean_sq_207_cast)[name = tensor<string, []>("op_7048_cast")];
            tensor<fp16, []> var_7049_to_fp16 = const()[name = tensor<string, []>("op_7049_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7050_cast = add(x = var_7048_cast, y = var_7049_to_fp16)[name = tensor<string, []>("op_7050_cast")];
            tensor<fp16, []> denom_207_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_207_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_207_cast = rsqrt(epsilon = denom_207_epsilon_0_to_fp16, x = var_7050_cast)[name = tensor<string, []>("denom_207_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_207_cast = mul(x = zero_mean_207_cast, y = denom_207_cast)[name = tensor<string, []>("out_207_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7054_to_fp16 = const()[name = tensor<string, []>("op_7054_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656330816)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7055_cast = add(x = out_207_cast, y = var_7054_to_fp16)[name = tensor<string, []>("op_7055_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7057_to_fp16 = const()[name = tensor<string, []>("op_7057_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656333440)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_289_cast = mul(x = var_7055_cast, y = var_7057_to_fp16)[name = tensor<string, []>("hidden_states_289_cast")];
            tensor<int32, [2]> var_7064 = const()[name = tensor<string, []>("op_7064"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7066 = const()[name = tensor<string, []>("op_7066"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_139_pad_type_0 = const()[name = tensor<string, []>("q_139_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_139_pad_0 = const()[name = tensor<string, []>("q_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656336064))), lut = tensor<fp16, [2]>([-0x1.f74p-7, 0x1.f98p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_139_cast = conv(dilations = var_7066, groups = var_6865, pad = q_139_pad_0, pad_type = q_139_pad_type_0, strides = var_7064, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_289_cast)[name = tensor<string, []>("q_139_cast")];
            tensor<int32, [2]> var_7070 = const()[name = tensor<string, []>("op_7070"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7072 = const()[name = tensor<string, []>("op_7072"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_139_pad_type_0 = const()[name = tensor<string, []>("k_139_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_139_pad_0 = const()[name = tensor<string, []>("k_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656540928))), lut = tensor<fp16, [2]>([-0x1.d24p-7, 0x1.d68p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_139_cast = conv(dilations = var_7072, groups = var_6865, pad = k_139_pad_0, pad_type = k_139_pad_type_0, strides = var_7070, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_139_cast")];
            tensor<int32, [2]> var_7076 = const()[name = tensor<string, []>("op_7076"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7078 = const()[name = tensor<string, []>("op_7078"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_139_pad_type_0 = const()[name = tensor<string, []>("v_139_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_139_pad_0 = const()[name = tensor<string, []>("v_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656868672))), lut = tensor<fp16, [4]>([-0x1.ce8p-6, -0x1.0bcp-7, 0x1.09cp-7, 0x1.cd8p-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_139_cast = conv(dilations = var_7078, groups = var_6865, pad = v_139_pad_0, pad_type = v_139_pad_type_0, strides = var_7076, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_139_cast")];
            tensor<int32, [4]> var_7082 = const()[name = tensor<string, []>("op_7082"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7083_cast = reshape(shape = var_7082, x = q_139_cast)[name = tensor<string, []>("op_7083_cast")];
            tensor<int32, [4]> var_7084 = const()[name = tensor<string, []>("op_7084"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7085_cast = reshape(shape = var_7084, x = k_139_cast)[name = tensor<string, []>("op_7085_cast")];
            tensor<int32, [4]> var_7086 = const()[name = tensor<string, []>("op_7086"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7087_cast = reshape(shape = var_7086, x = v_139_cast)[name = tensor<string, []>("op_7087_cast")];
            tensor<bool, []> attn_weights_277_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_277_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_277_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_277_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_277_cast = matmul(transpose_x = attn_weights_277_transpose_x_0, transpose_y = attn_weights_277_transpose_y_0, x = var_7083_cast, y = var_7085_cast)[name = tensor<string, []>("attn_weights_277_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_279_cast = mul(x = attn_weights_277_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_279_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_7091_cast = softmax(axis = var_6849, x = attn_weights_279_cast)[name = tensor<string, []>("op_7091_cast")];
            tensor<bool, []> attn_139_transpose_x_0 = const()[name = tensor<string, []>("attn_139_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_139_transpose_y_0 = const()[name = tensor<string, []>("attn_139_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_139_cast = matmul(transpose_x = attn_139_transpose_x_0, transpose_y = attn_139_transpose_y_0, x = var_7087_cast, y = var_7091_cast)[name = tensor<string, []>("attn_139_cast")];
            tensor<int32, [4]> var_7095 = const()[name = tensor<string, []>("op_7095"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_441_cast = reshape(shape = var_7095, x = attn_139_cast)[name = tensor<string, []>("input_441_cast")];
            tensor<int32, [2]> var_7100 = const()[name = tensor<string, []>("op_7100"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7102 = const()[name = tensor<string, []>("op_7102"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7104_pad_type_0 = const()[name = tensor<string, []>("op_7104_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7104_pad_0 = const()[name = tensor<string, []>("op_7104_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(657524096))), lut = tensor<fp16, [2]>([-0x1.f6cp-8, 0x1.f54p-8]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(657728960)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7104_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_7102, groups = var_6865, pad = var_7104_pad_0, pad_type = var_7104_pad_type_0, strides = var_7100, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_441_cast)[name = tensor<string, []>("op_7104_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_209_cast = add(x = var_7104_cast, y = inputs_207_cast)[name = tensor<string, []>("inputs_209_cast")];
            tensor<int32, [1]> var_7108 = const()[name = tensor<string, []>("op_7108"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_209_cast = reduce_mean(axes = var_7108, keep_dims = var_6860, x = inputs_209_cast)[name = tensor<string, []>("channels_mean_209_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_209_cast = sub(x = inputs_209_cast, y = channels_mean_209_cast)[name = tensor<string, []>("zero_mean_209_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_209_cast = mul(x = zero_mean_209_cast, y = zero_mean_209_cast)[name = tensor<string, []>("zero_mean_sq_209_cast")];
            tensor<int32, [1]> var_7112 = const()[name = tensor<string, []>("op_7112"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7113_cast = reduce_mean(axes = var_7112, keep_dims = var_6860, x = zero_mean_sq_209_cast)[name = tensor<string, []>("op_7113_cast")];
            tensor<fp16, []> var_7114_to_fp16 = const()[name = tensor<string, []>("op_7114_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7115_cast = add(x = var_7113_cast, y = var_7114_to_fp16)[name = tensor<string, []>("op_7115_cast")];
            tensor<fp16, []> denom_209_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_209_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_209_cast = rsqrt(epsilon = denom_209_epsilon_0_to_fp16, x = var_7115_cast)[name = tensor<string, []>("denom_209_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_209_cast = mul(x = zero_mean_209_cast, y = denom_209_cast)[name = tensor<string, []>("out_209_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7119_to_fp16 = const()[name = tensor<string, []>("op_7119_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(657731584)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7120_cast = add(x = out_209_cast, y = var_7119_to_fp16)[name = tensor<string, []>("op_7120_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7122_to_fp16 = const()[name = tensor<string, []>("op_7122_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(657734208)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_443_cast = mul(x = var_7120_cast, y = var_7122_to_fp16)[name = tensor<string, []>("input_443_cast")];
            tensor<int32, [2]> var_7130 = const()[name = tensor<string, []>("op_7130"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7132 = const()[name = tensor<string, []>("op_7132"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7134_pad_type_0 = const()[name = tensor<string, []>("op_7134_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7134_pad_0 = const()[name = tensor<string, []>("op_7134_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(657736832))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(667567296))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(667567488)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_7134_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_7132, groups = var_6865, pad = var_7134_pad_0, pad_type = var_7134_pad_type_0, strides = var_7130, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_443_cast)[name = tensor<string, []>("op_7134_cast")];
            tensor<int32, [2]> var_7135_split_sizes_0 = const()[name = tensor<string, []>("op_7135_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_7135_axis_0 = const()[name = tensor<string, []>("op_7135_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_7135_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_7135_cast_1 = split(axis = var_7135_axis_0, split_sizes = var_7135_split_sizes_0, x = var_7134_cast)[name = tensor<string, []>("op_7135_cast")];
            tensor<string, []> var_7137_mode_0 = const()[name = tensor<string, []>("op_7137_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_7137_cast = gelu(mode = var_7137_mode_0, x = var_7135_cast_1)[name = tensor<string, []>("op_7137_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_445_cast = mul(x = var_7135_cast_0, y = var_7137_cast)[name = tensor<string, []>("input_445_cast")];
            tensor<int32, [2]> var_7141 = const()[name = tensor<string, []>("op_7141"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7143 = const()[name = tensor<string, []>("op_7143"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7145_pad_type_0 = const()[name = tensor<string, []>("op_7145_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7145_pad_0 = const()[name = tensor<string, []>("op_7145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(667588032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(672503296))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(672503488)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7145_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7143, groups = var_6865, pad = var_7145_pad_0, pad_type = var_7145_pad_type_0, strides = var_7141, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_445_cast)[name = tensor<string, []>("op_7145_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_211_cast = add(x = var_7145_cast, y = inputs_209_cast)[name = tensor<string, []>("inputs_211_cast")];
            tensor<int32, [1]> var_7155 = const()[name = tensor<string, []>("op_7155"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_211_cast = reduce_mean(axes = var_7155, keep_dims = var_6860, x = inputs_211_cast)[name = tensor<string, []>("channels_mean_211_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_211_cast = sub(x = inputs_211_cast, y = channels_mean_211_cast)[name = tensor<string, []>("zero_mean_211_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_211_cast = mul(x = zero_mean_211_cast, y = zero_mean_211_cast)[name = tensor<string, []>("zero_mean_sq_211_cast")];
            tensor<int32, [1]> var_7159 = const()[name = tensor<string, []>("op_7159"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7160_cast = reduce_mean(axes = var_7159, keep_dims = var_6860, x = zero_mean_sq_211_cast)[name = tensor<string, []>("op_7160_cast")];
            tensor<fp16, []> var_7161_to_fp16 = const()[name = tensor<string, []>("op_7161_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7162_cast = add(x = var_7160_cast, y = var_7161_to_fp16)[name = tensor<string, []>("op_7162_cast")];
            tensor<fp16, []> denom_211_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_211_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_211_cast = rsqrt(epsilon = denom_211_epsilon_0_to_fp16, x = var_7162_cast)[name = tensor<string, []>("denom_211_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_211_cast = mul(x = zero_mean_211_cast, y = denom_211_cast)[name = tensor<string, []>("out_211_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7166_to_fp16 = const()[name = tensor<string, []>("op_7166_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(672506112)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7167_cast = add(x = out_211_cast, y = var_7166_to_fp16)[name = tensor<string, []>("op_7167_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7169_to_fp16 = const()[name = tensor<string, []>("op_7169_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(672508736)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_293_cast = mul(x = var_7167_cast, y = var_7169_to_fp16)[name = tensor<string, []>("hidden_states_293_cast")];
            tensor<int32, [2]> var_7176 = const()[name = tensor<string, []>("op_7176"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7178 = const()[name = tensor<string, []>("op_7178"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_141_pad_type_0 = const()[name = tensor<string, []>("q_141_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_141_pad_0 = const()[name = tensor<string, []>("q_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(672511360))), lut = tensor<fp16, [4]>([-0x1.5b4p-5, -0x1.a14p-7, 0x1.a2cp-7, 0x1.5cp-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_141_cast = conv(dilations = var_7178, groups = var_6865, pad = q_141_pad_0, pad_type = q_141_pad_type_0, strides = var_7176, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_293_cast)[name = tensor<string, []>("q_141_cast")];
            tensor<int32, [2]> var_7182 = const()[name = tensor<string, []>("op_7182"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7184 = const()[name = tensor<string, []>("op_7184"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_141_pad_type_0 = const()[name = tensor<string, []>("k_141_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_141_pad_0 = const()[name = tensor<string, []>("k_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(672921024))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673740288))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_141_cast = conv(dilations = var_7184, groups = var_6865, pad = k_141_pad_0, pad_type = k_141_pad_type_0, strides = var_7182, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_293_cast)[name = tensor<string, []>("k_141_cast")];
            tensor<int32, [2]> var_7188 = const()[name = tensor<string, []>("op_7188"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7190 = const()[name = tensor<string, []>("op_7190"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_141_pad_type_0 = const()[name = tensor<string, []>("v_141_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_141_pad_0 = const()[name = tensor<string, []>("v_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673740416))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(674559680))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_141_cast = conv(dilations = var_7190, groups = var_6865, pad = v_141_pad_0, pad_type = v_141_pad_type_0, strides = var_7188, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_293_cast)[name = tensor<string, []>("v_141_cast")];
            tensor<int32, [4]> var_7194 = const()[name = tensor<string, []>("op_7194"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7195_cast = reshape(shape = var_7194, x = q_141_cast)[name = tensor<string, []>("op_7195_cast")];
            tensor<int32, [4]> var_7196 = const()[name = tensor<string, []>("op_7196"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7197_cast = reshape(shape = var_7196, x = k_141_cast)[name = tensor<string, []>("op_7197_cast")];
            tensor<int32, [4]> var_7198 = const()[name = tensor<string, []>("op_7198"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7199_cast = reshape(shape = var_7198, x = v_141_cast)[name = tensor<string, []>("op_7199_cast")];
            tensor<bool, []> attn_weights_281_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_281_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_281_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_281_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_281_cast = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_7195_cast, y = var_7197_cast)[name = tensor<string, []>("attn_weights_281_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_283_cast = mul(x = attn_weights_281_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_283_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_7203_cast = softmax(axis = var_6849, x = attn_weights_283_cast)[name = tensor<string, []>("op_7203_cast")];
            tensor<bool, []> attn_141_transpose_x_0 = const()[name = tensor<string, []>("attn_141_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_141_transpose_y_0 = const()[name = tensor<string, []>("attn_141_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_141_cast = matmul(transpose_x = attn_141_transpose_x_0, transpose_y = attn_141_transpose_y_0, x = var_7199_cast, y = var_7203_cast)[name = tensor<string, []>("attn_141_cast")];
            tensor<int32, [4]> var_7207 = const()[name = tensor<string, []>("op_7207"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_447_cast = reshape(shape = var_7207, x = attn_141_cast)[name = tensor<string, []>("input_447_cast")];
            tensor<int32, [2]> var_7212 = const()[name = tensor<string, []>("op_7212"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7214 = const()[name = tensor<string, []>("op_7214"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7216_pad_type_0 = const()[name = tensor<string, []>("op_7216_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7216_pad_0 = const()[name = tensor<string, []>("op_7216_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(674559808))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675379072))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675379200)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7216_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_7214, groups = var_6865, pad = var_7216_pad_0, pad_type = var_7216_pad_type_0, strides = var_7212, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_447_cast)[name = tensor<string, []>("op_7216_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_213_cast = add(x = var_7216_cast, y = inputs_211_cast)[name = tensor<string, []>("inputs_213_cast")];
            tensor<int32, [1]> var_7220 = const()[name = tensor<string, []>("op_7220"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_213_cast = reduce_mean(axes = var_7220, keep_dims = var_6860, x = inputs_213_cast)[name = tensor<string, []>("channels_mean_213_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_213_cast = sub(x = inputs_213_cast, y = channels_mean_213_cast)[name = tensor<string, []>("zero_mean_213_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_213_cast = mul(x = zero_mean_213_cast, y = zero_mean_213_cast)[name = tensor<string, []>("zero_mean_sq_213_cast")];
            tensor<int32, [1]> var_7224 = const()[name = tensor<string, []>("op_7224"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7225_cast = reduce_mean(axes = var_7224, keep_dims = var_6860, x = zero_mean_sq_213_cast)[name = tensor<string, []>("op_7225_cast")];
            tensor<fp16, []> var_7226_to_fp16 = const()[name = tensor<string, []>("op_7226_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7227_cast = add(x = var_7225_cast, y = var_7226_to_fp16)[name = tensor<string, []>("op_7227_cast")];
            tensor<fp16, []> denom_213_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_213_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_213_cast = rsqrt(epsilon = denom_213_epsilon_0_to_fp16, x = var_7227_cast)[name = tensor<string, []>("denom_213_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_213_cast = mul(x = zero_mean_213_cast, y = denom_213_cast)[name = tensor<string, []>("out_213_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7231_to_fp16 = const()[name = tensor<string, []>("op_7231_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675381824)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7232_cast = add(x = out_213_cast, y = var_7231_to_fp16)[name = tensor<string, []>("op_7232_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7234_to_fp16 = const()[name = tensor<string, []>("op_7234_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675384448)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_295_cast = mul(x = var_7232_cast, y = var_7234_to_fp16)[name = tensor<string, []>("hidden_states_295_cast")];
            tensor<int32, [2]> var_7241 = const()[name = tensor<string, []>("op_7241"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7243 = const()[name = tensor<string, []>("op_7243"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_143_pad_type_0 = const()[name = tensor<string, []>("q_143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_143_pad_0 = const()[name = tensor<string, []>("q_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675387072))), lut = tensor<fp16, [2]>([-0x1.298p-6, 0x1.28cp-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_143_cast = conv(dilations = var_7243, groups = var_6865, pad = q_143_pad_0, pad_type = q_143_pad_type_0, strides = var_7241, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_295_cast)[name = tensor<string, []>("q_143_cast")];
            tensor<int32, [2]> var_7247 = const()[name = tensor<string, []>("op_7247"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7249 = const()[name = tensor<string, []>("op_7249"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_143_pad_type_0 = const()[name = tensor<string, []>("k_143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_143_pad_0 = const()[name = tensor<string, []>("k_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675591936))), lut = tensor<fp16, [2]>([-0x1.0c4p-6, 0x1.0bp-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_143_cast = conv(dilations = var_7249, groups = var_6865, pad = k_143_pad_0, pad_type = k_143_pad_type_0, strides = var_7247, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_143_cast")];
            tensor<int32, [2]> var_7253 = const()[name = tensor<string, []>("op_7253"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7255 = const()[name = tensor<string, []>("op_7255"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_143_pad_type_0 = const()[name = tensor<string, []>("v_143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_143_pad_0 = const()[name = tensor<string, []>("v_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675919680))), lut = tensor<fp16, [4]>([-0x1.0ecp-5, -0x1.35cp-7, 0x1.35cp-7, 0x1.0e8p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_143_cast = conv(dilations = var_7255, groups = var_6865, pad = v_143_pad_0, pad_type = v_143_pad_type_0, strides = var_7253, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_143_cast")];
            tensor<int32, [4]> var_7259 = const()[name = tensor<string, []>("op_7259"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7260_cast = reshape(shape = var_7259, x = q_143_cast)[name = tensor<string, []>("op_7260_cast")];
            tensor<int32, [4]> var_7261 = const()[name = tensor<string, []>("op_7261"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7262_cast = reshape(shape = var_7261, x = k_143_cast)[name = tensor<string, []>("op_7262_cast")];
            tensor<int32, [4]> var_7263 = const()[name = tensor<string, []>("op_7263"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7264_cast = reshape(shape = var_7263, x = v_143_cast)[name = tensor<string, []>("op_7264_cast")];
            tensor<bool, []> attn_weights_285_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_285_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_285_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_285_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_285_cast = matmul(transpose_x = attn_weights_285_transpose_x_0, transpose_y = attn_weights_285_transpose_y_0, x = var_7260_cast, y = var_7262_cast)[name = tensor<string, []>("attn_weights_285_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_287_cast = mul(x = attn_weights_285_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_287_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_7268_cast = softmax(axis = var_6849, x = attn_weights_287_cast)[name = tensor<string, []>("op_7268_cast")];
            tensor<bool, []> attn_143_transpose_x_0 = const()[name = tensor<string, []>("attn_143_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_143_transpose_y_0 = const()[name = tensor<string, []>("attn_143_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_143_cast = matmul(transpose_x = attn_143_transpose_x_0, transpose_y = attn_143_transpose_y_0, x = var_7264_cast, y = var_7268_cast)[name = tensor<string, []>("attn_143_cast")];
            tensor<int32, [4]> var_7272 = const()[name = tensor<string, []>("op_7272"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_449_cast = reshape(shape = var_7272, x = attn_143_cast)[name = tensor<string, []>("input_449_cast")];
            tensor<int32, [2]> var_7277 = const()[name = tensor<string, []>("op_7277"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7279 = const()[name = tensor<string, []>("op_7279"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7281_pad_type_0 = const()[name = tensor<string, []>("op_7281_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7281_pad_0 = const()[name = tensor<string, []>("op_7281_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(676575104))), lut = tensor<fp16, [2]>([-0x1.2fp-7, 0x1.2f4p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(676779968)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7281_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_7279, groups = var_6865, pad = var_7281_pad_0, pad_type = var_7281_pad_type_0, strides = var_7277, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_449_cast)[name = tensor<string, []>("op_7281_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_215_cast = add(x = var_7281_cast, y = inputs_213_cast)[name = tensor<string, []>("inputs_215_cast")];
            tensor<int32, [1]> var_7285 = const()[name = tensor<string, []>("op_7285"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_215_cast = reduce_mean(axes = var_7285, keep_dims = var_6860, x = inputs_215_cast)[name = tensor<string, []>("channels_mean_215_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_215_cast = sub(x = inputs_215_cast, y = channels_mean_215_cast)[name = tensor<string, []>("zero_mean_215_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_215_cast = mul(x = zero_mean_215_cast, y = zero_mean_215_cast)[name = tensor<string, []>("zero_mean_sq_215_cast")];
            tensor<int32, [1]> var_7289 = const()[name = tensor<string, []>("op_7289"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7290_cast = reduce_mean(axes = var_7289, keep_dims = var_6860, x = zero_mean_sq_215_cast)[name = tensor<string, []>("op_7290_cast")];
            tensor<fp16, []> var_7291_to_fp16 = const()[name = tensor<string, []>("op_7291_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7292_cast = add(x = var_7290_cast, y = var_7291_to_fp16)[name = tensor<string, []>("op_7292_cast")];
            tensor<fp16, []> denom_215_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_215_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_215_cast = rsqrt(epsilon = denom_215_epsilon_0_to_fp16, x = var_7292_cast)[name = tensor<string, []>("denom_215_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_215_cast = mul(x = zero_mean_215_cast, y = denom_215_cast)[name = tensor<string, []>("out_215_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7296_to_fp16 = const()[name = tensor<string, []>("op_7296_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(676782592)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7297_cast = add(x = out_215_cast, y = var_7296_to_fp16)[name = tensor<string, []>("op_7297_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7299_to_fp16 = const()[name = tensor<string, []>("op_7299_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(676785216)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_451_cast = mul(x = var_7297_cast, y = var_7299_to_fp16)[name = tensor<string, []>("input_451_cast")];
            tensor<int32, [2]> var_7307 = const()[name = tensor<string, []>("op_7307"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7309 = const()[name = tensor<string, []>("op_7309"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7311_pad_type_0 = const()[name = tensor<string, []>("op_7311_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = tensor<string, []>("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(676787840))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(683341504))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(683341632)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_7311_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_7309, groups = var_6865, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7307, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_451_cast)[name = tensor<string, []>("op_7311_cast")];
            tensor<int32, [2]> var_7312_split_sizes_0 = const()[name = tensor<string, []>("op_7312_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_7312_axis_0 = const()[name = tensor<string, []>("op_7312_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_7312_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_7312_cast_1 = split(axis = var_7312_axis_0, split_sizes = var_7312_split_sizes_0, x = var_7311_cast)[name = tensor<string, []>("op_7312_cast")];
            tensor<string, []> var_7314_mode_0 = const()[name = tensor<string, []>("op_7314_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_7314_cast = gelu(mode = var_7314_mode_0, x = var_7312_cast_1)[name = tensor<string, []>("op_7314_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_453_cast = mul(x = var_7312_cast_0, y = var_7314_cast)[name = tensor<string, []>("input_453_cast")];
            tensor<int32, [2]> var_7318 = const()[name = tensor<string, []>("op_7318"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7320 = const()[name = tensor<string, []>("op_7320"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7322_pad_type_0 = const()[name = tensor<string, []>("op_7322_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7322_pad_0 = const()[name = tensor<string, []>("op_7322_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(683362176))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(686639040))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(686639168)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7322_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_7320, groups = var_6865, pad = var_7322_pad_0, pad_type = var_7322_pad_type_0, strides = var_7318, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_453_cast)[name = tensor<string, []>("op_7322_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_217_cast = add(x = var_7322_cast, y = inputs_215_cast)[name = tensor<string, []>("inputs_217_cast")];
            tensor<int32, [1]> var_7332 = const()[name = tensor<string, []>("op_7332"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_217_cast = reduce_mean(axes = var_7332, keep_dims = var_6860, x = inputs_217_cast)[name = tensor<string, []>("channels_mean_217_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_217_cast = sub(x = inputs_217_cast, y = channels_mean_217_cast)[name = tensor<string, []>("zero_mean_217_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_217_cast = mul(x = zero_mean_217_cast, y = zero_mean_217_cast)[name = tensor<string, []>("zero_mean_sq_217_cast")];
            tensor<int32, [1]> var_7336 = const()[name = tensor<string, []>("op_7336"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7337_cast = reduce_mean(axes = var_7336, keep_dims = var_6860, x = zero_mean_sq_217_cast)[name = tensor<string, []>("op_7337_cast")];
            tensor<fp16, []> var_7338_to_fp16 = const()[name = tensor<string, []>("op_7338_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7339_cast = add(x = var_7337_cast, y = var_7338_to_fp16)[name = tensor<string, []>("op_7339_cast")];
            tensor<fp16, []> denom_217_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_217_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_217_cast = rsqrt(epsilon = denom_217_epsilon_0_to_fp16, x = var_7339_cast)[name = tensor<string, []>("denom_217_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_217_cast = mul(x = zero_mean_217_cast, y = denom_217_cast)[name = tensor<string, []>("out_217_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7343_to_fp16 = const()[name = tensor<string, []>("op_7343_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(686641792)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7344_cast = add(x = out_217_cast, y = var_7343_to_fp16)[name = tensor<string, []>("op_7344_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7346_to_fp16 = const()[name = tensor<string, []>("op_7346_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(686644416)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_299_cast = mul(x = var_7344_cast, y = var_7346_to_fp16)[name = tensor<string, []>("hidden_states_299_cast")];
            tensor<int32, [2]> var_7353 = const()[name = tensor<string, []>("op_7353"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7355 = const()[name = tensor<string, []>("op_7355"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_145_pad_type_0 = const()[name = tensor<string, []>("q_145_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_145_pad_0 = const()[name = tensor<string, []>("q_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(686647040))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(687466304))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_145_cast = conv(dilations = var_7355, groups = var_6865, pad = q_145_pad_0, pad_type = q_145_pad_type_0, strides = var_7353, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_299_cast)[name = tensor<string, []>("q_145_cast")];
            tensor<int32, [2]> var_7359 = const()[name = tensor<string, []>("op_7359"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7361 = const()[name = tensor<string, []>("op_7361"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_145_pad_type_0 = const()[name = tensor<string, []>("k_145_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_145_pad_0 = const()[name = tensor<string, []>("k_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(687466432))), lut = tensor<fp16, [4]>([-0x1.628p-5, -0x1.aa4p-7, 0x1.abcp-7, 0x1.638p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_145_cast = conv(dilations = var_7361, groups = var_6865, pad = k_145_pad_0, pad_type = k_145_pad_type_0, strides = var_7359, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_299_cast)[name = tensor<string, []>("k_145_cast")];
            tensor<int32, [2]> var_7365 = const()[name = tensor<string, []>("op_7365"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7367 = const()[name = tensor<string, []>("op_7367"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_145_pad_type_0 = const()[name = tensor<string, []>("v_145_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_145_pad_0 = const()[name = tensor<string, []>("v_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(687876096))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(688695360))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_145_cast = conv(dilations = var_7367, groups = var_6865, pad = v_145_pad_0, pad_type = v_145_pad_type_0, strides = var_7365, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_299_cast)[name = tensor<string, []>("v_145_cast")];
            tensor<int32, [4]> var_7371 = const()[name = tensor<string, []>("op_7371"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7372_cast = reshape(shape = var_7371, x = q_145_cast)[name = tensor<string, []>("op_7372_cast")];
            tensor<int32, [4]> var_7373 = const()[name = tensor<string, []>("op_7373"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7374_cast = reshape(shape = var_7373, x = k_145_cast)[name = tensor<string, []>("op_7374_cast")];
            tensor<int32, [4]> var_7375 = const()[name = tensor<string, []>("op_7375"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7376_cast = reshape(shape = var_7375, x = v_145_cast)[name = tensor<string, []>("op_7376_cast")];
            tensor<bool, []> attn_weights_289_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_289_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_289_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_289_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_289_cast = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_7372_cast, y = var_7374_cast)[name = tensor<string, []>("attn_weights_289_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_291_cast = mul(x = attn_weights_289_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_291_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_7380_cast = softmax(axis = var_6849, x = attn_weights_291_cast)[name = tensor<string, []>("op_7380_cast")];
            tensor<bool, []> attn_145_transpose_x_0 = const()[name = tensor<string, []>("attn_145_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_145_transpose_y_0 = const()[name = tensor<string, []>("attn_145_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_145_cast = matmul(transpose_x = attn_145_transpose_x_0, transpose_y = attn_145_transpose_y_0, x = var_7376_cast, y = var_7380_cast)[name = tensor<string, []>("attn_145_cast")];
            tensor<int32, [4]> var_7384 = const()[name = tensor<string, []>("op_7384"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_455_cast = reshape(shape = var_7384, x = attn_145_cast)[name = tensor<string, []>("input_455_cast")];
            tensor<int32, [2]> var_7389 = const()[name = tensor<string, []>("op_7389"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7391 = const()[name = tensor<string, []>("op_7391"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7393_pad_type_0 = const()[name = tensor<string, []>("op_7393_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7393_pad_0 = const()[name = tensor<string, []>("op_7393_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(688695488))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(689924352))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(689924544)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7393_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_7391, groups = var_6865, pad = var_7393_pad_0, pad_type = var_7393_pad_type_0, strides = var_7389, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_455_cast)[name = tensor<string, []>("op_7393_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_219_cast = add(x = var_7393_cast, y = inputs_217_cast)[name = tensor<string, []>("inputs_219_cast")];
            tensor<int32, [1]> var_7397 = const()[name = tensor<string, []>("op_7397"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_219_cast = reduce_mean(axes = var_7397, keep_dims = var_6860, x = inputs_219_cast)[name = tensor<string, []>("channels_mean_219_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_219_cast = sub(x = inputs_219_cast, y = channels_mean_219_cast)[name = tensor<string, []>("zero_mean_219_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_219_cast = mul(x = zero_mean_219_cast, y = zero_mean_219_cast)[name = tensor<string, []>("zero_mean_sq_219_cast")];
            tensor<int32, [1]> var_7401 = const()[name = tensor<string, []>("op_7401"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7402_cast = reduce_mean(axes = var_7401, keep_dims = var_6860, x = zero_mean_sq_219_cast)[name = tensor<string, []>("op_7402_cast")];
            tensor<fp16, []> var_7403_to_fp16 = const()[name = tensor<string, []>("op_7403_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7404_cast = add(x = var_7402_cast, y = var_7403_to_fp16)[name = tensor<string, []>("op_7404_cast")];
            tensor<fp16, []> denom_219_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_219_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_219_cast = rsqrt(epsilon = denom_219_epsilon_0_to_fp16, x = var_7404_cast)[name = tensor<string, []>("denom_219_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_219_cast = mul(x = zero_mean_219_cast, y = denom_219_cast)[name = tensor<string, []>("out_219_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7408_to_fp16 = const()[name = tensor<string, []>("op_7408_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(689927168)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7409_cast = add(x = out_219_cast, y = var_7408_to_fp16)[name = tensor<string, []>("op_7409_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7411_to_fp16 = const()[name = tensor<string, []>("op_7411_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(689929792)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_301_cast = mul(x = var_7409_cast, y = var_7411_to_fp16)[name = tensor<string, []>("hidden_states_301_cast")];
            tensor<int32, [2]> var_7418 = const()[name = tensor<string, []>("op_7418"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7420 = const()[name = tensor<string, []>("op_7420"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_147_pad_type_0 = const()[name = tensor<string, []>("q_147_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_147_pad_0 = const()[name = tensor<string, []>("q_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(689932416))), lut = tensor<fp16, [4]>([-0x1.39cp-5, -0x1.76cp-7, 0x1.744p-7, 0x1.38cp-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_147_cast = conv(dilations = var_7420, groups = var_6865, pad = q_147_pad_0, pad_type = q_147_pad_type_0, strides = var_7418, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_301_cast)[name = tensor<string, []>("q_147_cast")];
            tensor<int32, [2]> var_7424 = const()[name = tensor<string, []>("op_7424"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7426 = const()[name = tensor<string, []>("op_7426"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_147_pad_type_0 = const()[name = tensor<string, []>("k_147_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_147_pad_0 = const()[name = tensor<string, []>("k_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(690342080))), lut = tensor<fp16, [4]>([-0x1.1a8p-5, -0x1.4e4p-7, 0x1.4acp-7, 0x1.19cp-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_147_cast = conv(dilations = var_7426, groups = var_6865, pad = k_147_pad_0, pad_type = k_147_pad_type_0, strides = var_7424, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_147_cast")];
            tensor<int32, [2]> var_7430 = const()[name = tensor<string, []>("op_7430"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7432 = const()[name = tensor<string, []>("op_7432"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_147_pad_type_0 = const()[name = tensor<string, []>("v_147_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_147_pad_0 = const()[name = tensor<string, []>("v_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(690997504))), lut = tensor<fp16, [4]>([-0x1.25cp-5, -0x1.544p-7, 0x1.518p-7, 0x1.254p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_147_cast = conv(dilations = var_7432, groups = var_6865, pad = v_147_pad_0, pad_type = v_147_pad_type_0, strides = var_7430, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_147_cast")];
            tensor<int32, [4]> var_7436 = const()[name = tensor<string, []>("op_7436"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7437_cast = reshape(shape = var_7436, x = q_147_cast)[name = tensor<string, []>("op_7437_cast")];
            tensor<int32, [4]> var_7438 = const()[name = tensor<string, []>("op_7438"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7439_cast = reshape(shape = var_7438, x = k_147_cast)[name = tensor<string, []>("op_7439_cast")];
            tensor<int32, [4]> var_7440 = const()[name = tensor<string, []>("op_7440"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7441_cast = reshape(shape = var_7440, x = v_147_cast)[name = tensor<string, []>("op_7441_cast")];
            tensor<bool, []> attn_weights_293_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_293_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_293_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_293_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_293_cast = matmul(transpose_x = attn_weights_293_transpose_x_0, transpose_y = attn_weights_293_transpose_y_0, x = var_7437_cast, y = var_7439_cast)[name = tensor<string, []>("attn_weights_293_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_295_cast = mul(x = attn_weights_293_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_295_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_7445_cast = softmax(axis = var_6849, x = attn_weights_295_cast)[name = tensor<string, []>("op_7445_cast")];
            tensor<bool, []> attn_147_transpose_x_0 = const()[name = tensor<string, []>("attn_147_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_147_transpose_y_0 = const()[name = tensor<string, []>("attn_147_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_147_cast = matmul(transpose_x = attn_147_transpose_x_0, transpose_y = attn_147_transpose_y_0, x = var_7441_cast, y = var_7445_cast)[name = tensor<string, []>("attn_147_cast")];
            tensor<int32, [4]> var_7449 = const()[name = tensor<string, []>("op_7449"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_457_cast = reshape(shape = var_7449, x = attn_147_cast)[name = tensor<string, []>("input_457_cast")];
            tensor<int32, [2]> var_7454 = const()[name = tensor<string, []>("op_7454"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7456 = const()[name = tensor<string, []>("op_7456"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7458_pad_type_0 = const()[name = tensor<string, []>("op_7458_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7458_pad_0 = const()[name = tensor<string, []>("op_7458_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691652928))), lut = tensor<fp16, [2]>([-0x1.52cp-7, 0x1.548p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691857792)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7458_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_7456, groups = var_6865, pad = var_7458_pad_0, pad_type = var_7458_pad_type_0, strides = var_7454, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_457_cast)[name = tensor<string, []>("op_7458_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_221_cast = add(x = var_7458_cast, y = inputs_219_cast)[name = tensor<string, []>("inputs_221_cast")];
            tensor<int32, [1]> var_7462 = const()[name = tensor<string, []>("op_7462"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_221_cast = reduce_mean(axes = var_7462, keep_dims = var_6860, x = inputs_221_cast)[name = tensor<string, []>("channels_mean_221_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_221_cast = sub(x = inputs_221_cast, y = channels_mean_221_cast)[name = tensor<string, []>("zero_mean_221_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_221_cast = mul(x = zero_mean_221_cast, y = zero_mean_221_cast)[name = tensor<string, []>("zero_mean_sq_221_cast")];
            tensor<int32, [1]> var_7466 = const()[name = tensor<string, []>("op_7466"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7467_cast = reduce_mean(axes = var_7466, keep_dims = var_6860, x = zero_mean_sq_221_cast)[name = tensor<string, []>("op_7467_cast")];
            tensor<fp16, []> var_7468_to_fp16 = const()[name = tensor<string, []>("op_7468_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7469_cast = add(x = var_7467_cast, y = var_7468_to_fp16)[name = tensor<string, []>("op_7469_cast")];
            tensor<fp16, []> denom_221_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_221_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_221_cast = rsqrt(epsilon = denom_221_epsilon_0_to_fp16, x = var_7469_cast)[name = tensor<string, []>("denom_221_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_221_cast = mul(x = zero_mean_221_cast, y = denom_221_cast)[name = tensor<string, []>("out_221_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7473_to_fp16 = const()[name = tensor<string, []>("op_7473_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691860416)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7474_cast = add(x = out_221_cast, y = var_7473_to_fp16)[name = tensor<string, []>("op_7474_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7476_to_fp16 = const()[name = tensor<string, []>("op_7476_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691863040)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_459_cast = mul(x = var_7474_cast, y = var_7476_to_fp16)[name = tensor<string, []>("input_459_cast")];
            tensor<int32, [2]> var_7484 = const()[name = tensor<string, []>("op_7484"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7486 = const()[name = tensor<string, []>("op_7486"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7488_pad_type_0 = const()[name = tensor<string, []>("op_7488_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7488_pad_0 = const()[name = tensor<string, []>("op_7488_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(691865664))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(698419328))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(698419456)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_7488_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_7486, groups = var_6865, pad = var_7488_pad_0, pad_type = var_7488_pad_type_0, strides = var_7484, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_459_cast)[name = tensor<string, []>("op_7488_cast")];
            tensor<int32, [2]> var_7489_split_sizes_0 = const()[name = tensor<string, []>("op_7489_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_7489_axis_0 = const()[name = tensor<string, []>("op_7489_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_7489_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_7489_cast_1 = split(axis = var_7489_axis_0, split_sizes = var_7489_split_sizes_0, x = var_7488_cast)[name = tensor<string, []>("op_7489_cast")];
            tensor<string, []> var_7491_mode_0 = const()[name = tensor<string, []>("op_7491_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_7491_cast = gelu(mode = var_7491_mode_0, x = var_7489_cast_1)[name = tensor<string, []>("op_7491_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_461_cast = mul(x = var_7489_cast_0, y = var_7491_cast)[name = tensor<string, []>("input_461_cast")];
            tensor<int32, [2]> var_7495 = const()[name = tensor<string, []>("op_7495"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7497 = const()[name = tensor<string, []>("op_7497"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7499_pad_type_0 = const()[name = tensor<string, []>("op_7499_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7499_pad_0 = const()[name = tensor<string, []>("op_7499_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(698440000))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703355264))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703355456)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7499_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_7497, groups = var_6865, pad = var_7499_pad_0, pad_type = var_7499_pad_type_0, strides = var_7495, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_461_cast)[name = tensor<string, []>("op_7499_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_223_cast = add(x = var_7499_cast, y = inputs_221_cast)[name = tensor<string, []>("inputs_223_cast")];
            tensor<int32, [1]> var_7509 = const()[name = tensor<string, []>("op_7509"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_223_cast = reduce_mean(axes = var_7509, keep_dims = var_6860, x = inputs_223_cast)[name = tensor<string, []>("channels_mean_223_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_223_cast = sub(x = inputs_223_cast, y = channels_mean_223_cast)[name = tensor<string, []>("zero_mean_223_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_223_cast = mul(x = zero_mean_223_cast, y = zero_mean_223_cast)[name = tensor<string, []>("zero_mean_sq_223_cast")];
            tensor<int32, [1]> var_7513 = const()[name = tensor<string, []>("op_7513"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7514_cast = reduce_mean(axes = var_7513, keep_dims = var_6860, x = zero_mean_sq_223_cast)[name = tensor<string, []>("op_7514_cast")];
            tensor<fp16, []> var_7515_to_fp16 = const()[name = tensor<string, []>("op_7515_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7516_cast = add(x = var_7514_cast, y = var_7515_to_fp16)[name = tensor<string, []>("op_7516_cast")];
            tensor<fp16, []> denom_223_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_223_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_223_cast = rsqrt(epsilon = denom_223_epsilon_0_to_fp16, x = var_7516_cast)[name = tensor<string, []>("denom_223_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_223_cast = mul(x = zero_mean_223_cast, y = denom_223_cast)[name = tensor<string, []>("out_223_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7520_to_fp16 = const()[name = tensor<string, []>("op_7520_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703358080)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7521_cast = add(x = out_223_cast, y = var_7520_to_fp16)[name = tensor<string, []>("op_7521_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7523_to_fp16 = const()[name = tensor<string, []>("op_7523_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703360704)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_305_cast = mul(x = var_7521_cast, y = var_7523_to_fp16)[name = tensor<string, []>("hidden_states_305_cast")];
            tensor<int32, [2]> var_7530 = const()[name = tensor<string, []>("op_7530"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7532 = const()[name = tensor<string, []>("op_7532"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_149_pad_type_0 = const()[name = tensor<string, []>("q_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_149_pad_0 = const()[name = tensor<string, []>("q_149_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703363328))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704182592))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_149_cast = conv(dilations = var_7532, groups = var_6865, pad = q_149_pad_0, pad_type = q_149_pad_type_0, strides = var_7530, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_305_cast)[name = tensor<string, []>("q_149_cast")];
            tensor<int32, [2]> var_7536 = const()[name = tensor<string, []>("op_7536"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7538 = const()[name = tensor<string, []>("op_7538"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_149_pad_type_0 = const()[name = tensor<string, []>("k_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_149_pad_0 = const()[name = tensor<string, []>("k_149_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704182720))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(705001984))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_149_cast = conv(dilations = var_7538, groups = var_6865, pad = k_149_pad_0, pad_type = k_149_pad_type_0, strides = var_7536, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_305_cast)[name = tensor<string, []>("k_149_cast")];
            tensor<int32, [2]> var_7542 = const()[name = tensor<string, []>("op_7542"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7544 = const()[name = tensor<string, []>("op_7544"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_149_pad_type_0 = const()[name = tensor<string, []>("v_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_149_pad_0 = const()[name = tensor<string, []>("v_149_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(705002112))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(705821376))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_149_cast = conv(dilations = var_7544, groups = var_6865, pad = v_149_pad_0, pad_type = v_149_pad_type_0, strides = var_7542, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_305_cast)[name = tensor<string, []>("v_149_cast")];
            tensor<int32, [4]> var_7548 = const()[name = tensor<string, []>("op_7548"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7549_cast = reshape(shape = var_7548, x = q_149_cast)[name = tensor<string, []>("op_7549_cast")];
            tensor<int32, [4]> var_7550 = const()[name = tensor<string, []>("op_7550"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7551_cast = reshape(shape = var_7550, x = k_149_cast)[name = tensor<string, []>("op_7551_cast")];
            tensor<int32, [4]> var_7552 = const()[name = tensor<string, []>("op_7552"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7553_cast = reshape(shape = var_7552, x = v_149_cast)[name = tensor<string, []>("op_7553_cast")];
            tensor<bool, []> attn_weights_297_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_297_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_297_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_297_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_297_cast = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_7549_cast, y = var_7551_cast)[name = tensor<string, []>("attn_weights_297_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_299_cast = mul(x = attn_weights_297_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_299_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_7557_cast = softmax(axis = var_6849, x = attn_weights_299_cast)[name = tensor<string, []>("op_7557_cast")];
            tensor<bool, []> attn_149_transpose_x_0 = const()[name = tensor<string, []>("attn_149_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_149_transpose_y_0 = const()[name = tensor<string, []>("attn_149_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_149_cast = matmul(transpose_x = attn_149_transpose_x_0, transpose_y = attn_149_transpose_y_0, x = var_7553_cast, y = var_7557_cast)[name = tensor<string, []>("attn_149_cast")];
            tensor<int32, [4]> var_7561 = const()[name = tensor<string, []>("op_7561"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_463_cast = reshape(shape = var_7561, x = attn_149_cast)[name = tensor<string, []>("input_463_cast")];
            tensor<int32, [2]> var_7566 = const()[name = tensor<string, []>("op_7566"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7568 = const()[name = tensor<string, []>("op_7568"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7570_pad_type_0 = const()[name = tensor<string, []>("op_7570_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7570_pad_0 = const()[name = tensor<string, []>("op_7570_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(705821504))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707050368))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707050560)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7570_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_7568, groups = var_6865, pad = var_7570_pad_0, pad_type = var_7570_pad_type_0, strides = var_7566, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_463_cast)[name = tensor<string, []>("op_7570_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_225_cast = add(x = var_7570_cast, y = inputs_223_cast)[name = tensor<string, []>("inputs_225_cast")];
            tensor<int32, [1]> var_7574 = const()[name = tensor<string, []>("op_7574"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_225_cast = reduce_mean(axes = var_7574, keep_dims = var_6860, x = inputs_225_cast)[name = tensor<string, []>("channels_mean_225_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_225_cast = sub(x = inputs_225_cast, y = channels_mean_225_cast)[name = tensor<string, []>("zero_mean_225_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_225_cast = mul(x = zero_mean_225_cast, y = zero_mean_225_cast)[name = tensor<string, []>("zero_mean_sq_225_cast")];
            tensor<int32, [1]> var_7578 = const()[name = tensor<string, []>("op_7578"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7579_cast = reduce_mean(axes = var_7578, keep_dims = var_6860, x = zero_mean_sq_225_cast)[name = tensor<string, []>("op_7579_cast")];
            tensor<fp16, []> var_7580_to_fp16 = const()[name = tensor<string, []>("op_7580_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7581_cast = add(x = var_7579_cast, y = var_7580_to_fp16)[name = tensor<string, []>("op_7581_cast")];
            tensor<fp16, []> denom_225_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_225_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_225_cast = rsqrt(epsilon = denom_225_epsilon_0_to_fp16, x = var_7581_cast)[name = tensor<string, []>("denom_225_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_225_cast = mul(x = zero_mean_225_cast, y = denom_225_cast)[name = tensor<string, []>("out_225_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7585_to_fp16 = const()[name = tensor<string, []>("op_7585_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707053184)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7586_cast = add(x = out_225_cast, y = var_7585_to_fp16)[name = tensor<string, []>("op_7586_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7588_to_fp16 = const()[name = tensor<string, []>("op_7588_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707055808)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_307_cast = mul(x = var_7586_cast, y = var_7588_to_fp16)[name = tensor<string, []>("hidden_states_307_cast")];
            tensor<int32, [2]> var_7595 = const()[name = tensor<string, []>("op_7595"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7597 = const()[name = tensor<string, []>("op_7597"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_151_pad_type_0 = const()[name = tensor<string, []>("q_151_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_151_pad_0 = const()[name = tensor<string, []>("q_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707058432))), lut = tensor<fp16, [2]>([-0x1.3fcp-6, 0x1.3fcp-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_151_cast = conv(dilations = var_7597, groups = var_6865, pad = q_151_pad_0, pad_type = q_151_pad_type_0, strides = var_7595, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor<string, []>("q_151_cast")];
            tensor<int32, [2]> var_7601 = const()[name = tensor<string, []>("op_7601"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7603 = const()[name = tensor<string, []>("op_7603"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_151_pad_type_0 = const()[name = tensor<string, []>("k_151_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_151_pad_0 = const()[name = tensor<string, []>("k_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707263296))), lut = tensor<fp16, [2]>([-0x1.114p-6, 0x1.114p-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_151_cast = conv(dilations = var_7603, groups = var_6865, pad = k_151_pad_0, pad_type = k_151_pad_type_0, strides = var_7601, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_151_cast")];
            tensor<int32, [2]> var_7607 = const()[name = tensor<string, []>("op_7607"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7609 = const()[name = tensor<string, []>("op_7609"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_151_pad_type_0 = const()[name = tensor<string, []>("v_151_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_151_pad_0 = const()[name = tensor<string, []>("v_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707591040))), lut = tensor<fp16, [2]>([-0x1.24p-6, 0x1.228p-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_151_cast = conv(dilations = var_7609, groups = var_6865, pad = v_151_pad_0, pad_type = v_151_pad_type_0, strides = var_7607, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_151_cast")];
            tensor<int32, [4]> var_7613 = const()[name = tensor<string, []>("op_7613"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7614_cast = reshape(shape = var_7613, x = q_151_cast)[name = tensor<string, []>("op_7614_cast")];
            tensor<int32, [4]> var_7615 = const()[name = tensor<string, []>("op_7615"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7616_cast = reshape(shape = var_7615, x = k_151_cast)[name = tensor<string, []>("op_7616_cast")];
            tensor<int32, [4]> var_7617 = const()[name = tensor<string, []>("op_7617"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7618_cast = reshape(shape = var_7617, x = v_151_cast)[name = tensor<string, []>("op_7618_cast")];
            tensor<bool, []> attn_weights_301_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_301_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_301_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_301_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_301_cast = matmul(transpose_x = attn_weights_301_transpose_x_0, transpose_y = attn_weights_301_transpose_y_0, x = var_7614_cast, y = var_7616_cast)[name = tensor<string, []>("attn_weights_301_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_303_cast = mul(x = attn_weights_301_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_303_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_7622_cast = softmax(axis = var_6849, x = attn_weights_303_cast)[name = tensor<string, []>("op_7622_cast")];
            tensor<bool, []> attn_151_transpose_x_0 = const()[name = tensor<string, []>("attn_151_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_151_transpose_y_0 = const()[name = tensor<string, []>("attn_151_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_151_cast = matmul(transpose_x = attn_151_transpose_x_0, transpose_y = attn_151_transpose_y_0, x = var_7618_cast, y = var_7622_cast)[name = tensor<string, []>("attn_151_cast")];
            tensor<int32, [4]> var_7626 = const()[name = tensor<string, []>("op_7626"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_465_cast = reshape(shape = var_7626, x = attn_151_cast)[name = tensor<string, []>("input_465_cast")];
            tensor<int32, [2]> var_7631 = const()[name = tensor<string, []>("op_7631"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7633 = const()[name = tensor<string, []>("op_7633"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7635_pad_type_0 = const()[name = tensor<string, []>("op_7635_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7635_pad_0 = const()[name = tensor<string, []>("op_7635_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(707918784))), lut = tensor<fp16, [2]>([-0x1.574p-7, 0x1.56cp-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(708123648)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7635_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_7633, groups = var_6865, pad = var_7635_pad_0, pad_type = var_7635_pad_type_0, strides = var_7631, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_465_cast)[name = tensor<string, []>("op_7635_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_227_cast = add(x = var_7635_cast, y = inputs_225_cast)[name = tensor<string, []>("inputs_227_cast")];
            tensor<int32, [1]> var_7639 = const()[name = tensor<string, []>("op_7639"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_227_cast = reduce_mean(axes = var_7639, keep_dims = var_6860, x = inputs_227_cast)[name = tensor<string, []>("channels_mean_227_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_227_cast = sub(x = inputs_227_cast, y = channels_mean_227_cast)[name = tensor<string, []>("zero_mean_227_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_227_cast = mul(x = zero_mean_227_cast, y = zero_mean_227_cast)[name = tensor<string, []>("zero_mean_sq_227_cast")];
            tensor<int32, [1]> var_7643 = const()[name = tensor<string, []>("op_7643"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7644_cast = reduce_mean(axes = var_7643, keep_dims = var_6860, x = zero_mean_sq_227_cast)[name = tensor<string, []>("op_7644_cast")];
            tensor<fp16, []> var_7645_to_fp16 = const()[name = tensor<string, []>("op_7645_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7646_cast = add(x = var_7644_cast, y = var_7645_to_fp16)[name = tensor<string, []>("op_7646_cast")];
            tensor<fp16, []> denom_227_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_227_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_227_cast = rsqrt(epsilon = denom_227_epsilon_0_to_fp16, x = var_7646_cast)[name = tensor<string, []>("denom_227_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_227_cast = mul(x = zero_mean_227_cast, y = denom_227_cast)[name = tensor<string, []>("out_227_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7650_to_fp16 = const()[name = tensor<string, []>("op_7650_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(708126272)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7651_cast = add(x = out_227_cast, y = var_7650_to_fp16)[name = tensor<string, []>("op_7651_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7653_to_fp16 = const()[name = tensor<string, []>("op_7653_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(708128896)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_467_cast = mul(x = var_7651_cast, y = var_7653_to_fp16)[name = tensor<string, []>("input_467_cast")];
            tensor<int32, [2]> var_7661 = const()[name = tensor<string, []>("op_7661"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7663 = const()[name = tensor<string, []>("op_7663"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7665_pad_type_0 = const()[name = tensor<string, []>("op_7665_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7665_pad_0 = const()[name = tensor<string, []>("op_7665_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(708131520))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(714685184))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(714685312)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_7665_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_7663, groups = var_6865, pad = var_7665_pad_0, pad_type = var_7665_pad_type_0, strides = var_7661, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_467_cast)[name = tensor<string, []>("op_7665_cast")];
            tensor<int32, [2]> var_7666_split_sizes_0 = const()[name = tensor<string, []>("op_7666_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_7666_axis_0 = const()[name = tensor<string, []>("op_7666_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_7666_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_7666_cast_1 = split(axis = var_7666_axis_0, split_sizes = var_7666_split_sizes_0, x = var_7665_cast)[name = tensor<string, []>("op_7666_cast")];
            tensor<string, []> var_7668_mode_0 = const()[name = tensor<string, []>("op_7668_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_7668_cast = gelu(mode = var_7668_mode_0, x = var_7666_cast_1)[name = tensor<string, []>("op_7668_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_469_cast = mul(x = var_7666_cast_0, y = var_7668_cast)[name = tensor<string, []>("input_469_cast")];
            tensor<int32, [2]> var_7672 = const()[name = tensor<string, []>("op_7672"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7674 = const()[name = tensor<string, []>("op_7674"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7676_pad_type_0 = const()[name = tensor<string, []>("op_7676_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7676_pad_0 = const()[name = tensor<string, []>("op_7676_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(714705856))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717982720))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717982848)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7676_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_7674, groups = var_6865, pad = var_7676_pad_0, pad_type = var_7676_pad_type_0, strides = var_7672, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_469_cast)[name = tensor<string, []>("op_7676_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_229_cast = add(x = var_7676_cast, y = inputs_227_cast)[name = tensor<string, []>("inputs_229_cast")];
            tensor<int32, [1]> var_7686 = const()[name = tensor<string, []>("op_7686"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_229_cast = reduce_mean(axes = var_7686, keep_dims = var_6860, x = inputs_229_cast)[name = tensor<string, []>("channels_mean_229_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_229_cast = sub(x = inputs_229_cast, y = channels_mean_229_cast)[name = tensor<string, []>("zero_mean_229_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_229_cast = mul(x = zero_mean_229_cast, y = zero_mean_229_cast)[name = tensor<string, []>("zero_mean_sq_229_cast")];
            tensor<int32, [1]> var_7690 = const()[name = tensor<string, []>("op_7690"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7691_cast = reduce_mean(axes = var_7690, keep_dims = var_6860, x = zero_mean_sq_229_cast)[name = tensor<string, []>("op_7691_cast")];
            tensor<fp16, []> var_7692_to_fp16 = const()[name = tensor<string, []>("op_7692_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7693_cast = add(x = var_7691_cast, y = var_7692_to_fp16)[name = tensor<string, []>("op_7693_cast")];
            tensor<fp16, []> denom_229_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_229_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_229_cast = rsqrt(epsilon = denom_229_epsilon_0_to_fp16, x = var_7693_cast)[name = tensor<string, []>("denom_229_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_229_cast = mul(x = zero_mean_229_cast, y = denom_229_cast)[name = tensor<string, []>("out_229_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7697_to_fp16 = const()[name = tensor<string, []>("op_7697_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717985472)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7698_cast = add(x = out_229_cast, y = var_7697_to_fp16)[name = tensor<string, []>("op_7698_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7700_to_fp16 = const()[name = tensor<string, []>("op_7700_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717988096)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_311_cast = mul(x = var_7698_cast, y = var_7700_to_fp16)[name = tensor<string, []>("hidden_states_311_cast")];
            tensor<int32, [2]> var_7707 = const()[name = tensor<string, []>("op_7707"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7709 = const()[name = tensor<string, []>("op_7709"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_153_pad_type_0 = const()[name = tensor<string, []>("q_153_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_153_pad_0 = const()[name = tensor<string, []>("q_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717990720))), lut = tensor<fp16, [4]>([-0x1.6ap-5, -0x1.b3cp-7, 0x1.b1p-7, 0x1.69p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_153_cast = conv(dilations = var_7709, groups = var_6865, pad = q_153_pad_0, pad_type = q_153_pad_type_0, strides = var_7707, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_311_cast)[name = tensor<string, []>("q_153_cast")];
            tensor<int32, [2]> var_7713 = const()[name = tensor<string, []>("op_7713"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7715 = const()[name = tensor<string, []>("op_7715"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_153_pad_type_0 = const()[name = tensor<string, []>("k_153_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_153_pad_0 = const()[name = tensor<string, []>("k_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718400384))), lut = tensor<fp16, [4]>([-0x1.6ap-5, -0x1.b1cp-7, 0x1.b64p-7, 0x1.6bp-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_153_cast = conv(dilations = var_7715, groups = var_6865, pad = k_153_pad_0, pad_type = k_153_pad_type_0, strides = var_7713, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_311_cast)[name = tensor<string, []>("k_153_cast")];
            tensor<int32, [2]> var_7719 = const()[name = tensor<string, []>("op_7719"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7721 = const()[name = tensor<string, []>("op_7721"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_153_pad_type_0 = const()[name = tensor<string, []>("v_153_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_153_pad_0 = const()[name = tensor<string, []>("v_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718810048))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719629312))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_153_cast = conv(dilations = var_7721, groups = var_6865, pad = v_153_pad_0, pad_type = v_153_pad_type_0, strides = var_7719, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_311_cast)[name = tensor<string, []>("v_153_cast")];
            tensor<int32, [4]> var_7725 = const()[name = tensor<string, []>("op_7725"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7726_cast = reshape(shape = var_7725, x = q_153_cast)[name = tensor<string, []>("op_7726_cast")];
            tensor<int32, [4]> var_7727 = const()[name = tensor<string, []>("op_7727"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7728_cast = reshape(shape = var_7727, x = k_153_cast)[name = tensor<string, []>("op_7728_cast")];
            tensor<int32, [4]> var_7729 = const()[name = tensor<string, []>("op_7729"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7730_cast = reshape(shape = var_7729, x = v_153_cast)[name = tensor<string, []>("op_7730_cast")];
            tensor<bool, []> attn_weights_305_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_305_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_305_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_305_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_305_cast = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7726_cast, y = var_7728_cast)[name = tensor<string, []>("attn_weights_305_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_307_cast = mul(x = attn_weights_305_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_307_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_7734_cast = softmax(axis = var_6849, x = attn_weights_307_cast)[name = tensor<string, []>("op_7734_cast")];
            tensor<bool, []> attn_153_transpose_x_0 = const()[name = tensor<string, []>("attn_153_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_153_transpose_y_0 = const()[name = tensor<string, []>("attn_153_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_153_cast = matmul(transpose_x = attn_153_transpose_x_0, transpose_y = attn_153_transpose_y_0, x = var_7730_cast, y = var_7734_cast)[name = tensor<string, []>("attn_153_cast")];
            tensor<int32, [4]> var_7738 = const()[name = tensor<string, []>("op_7738"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_471_cast = reshape(shape = var_7738, x = attn_153_cast)[name = tensor<string, []>("input_471_cast")];
            tensor<int32, [2]> var_7743 = const()[name = tensor<string, []>("op_7743"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7745 = const()[name = tensor<string, []>("op_7745"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7747_pad_type_0 = const()[name = tensor<string, []>("op_7747_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7747_pad_0 = const()[name = tensor<string, []>("op_7747_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(719629440))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720858304))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720858496)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7747_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_7745, groups = var_6865, pad = var_7747_pad_0, pad_type = var_7747_pad_type_0, strides = var_7743, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_471_cast)[name = tensor<string, []>("op_7747_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_231_cast = add(x = var_7747_cast, y = inputs_229_cast)[name = tensor<string, []>("inputs_231_cast")];
            tensor<int32, [1]> var_7751 = const()[name = tensor<string, []>("op_7751"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_231_cast = reduce_mean(axes = var_7751, keep_dims = var_6860, x = inputs_231_cast)[name = tensor<string, []>("channels_mean_231_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_231_cast = sub(x = inputs_231_cast, y = channels_mean_231_cast)[name = tensor<string, []>("zero_mean_231_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_231_cast = mul(x = zero_mean_231_cast, y = zero_mean_231_cast)[name = tensor<string, []>("zero_mean_sq_231_cast")];
            tensor<int32, [1]> var_7755 = const()[name = tensor<string, []>("op_7755"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7756_cast = reduce_mean(axes = var_7755, keep_dims = var_6860, x = zero_mean_sq_231_cast)[name = tensor<string, []>("op_7756_cast")];
            tensor<fp16, []> var_7757_to_fp16 = const()[name = tensor<string, []>("op_7757_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7758_cast = add(x = var_7756_cast, y = var_7757_to_fp16)[name = tensor<string, []>("op_7758_cast")];
            tensor<fp16, []> denom_231_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_231_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_231_cast = rsqrt(epsilon = denom_231_epsilon_0_to_fp16, x = var_7758_cast)[name = tensor<string, []>("denom_231_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_231_cast = mul(x = zero_mean_231_cast, y = denom_231_cast)[name = tensor<string, []>("out_231_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7762_to_fp16 = const()[name = tensor<string, []>("op_7762_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720861120)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7763_cast = add(x = out_231_cast, y = var_7762_to_fp16)[name = tensor<string, []>("op_7763_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7765_to_fp16 = const()[name = tensor<string, []>("op_7765_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720863744)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_313_cast = mul(x = var_7763_cast, y = var_7765_to_fp16)[name = tensor<string, []>("hidden_states_313_cast")];
            tensor<int32, [2]> var_7772 = const()[name = tensor<string, []>("op_7772"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7774 = const()[name = tensor<string, []>("op_7774"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_155_pad_type_0 = const()[name = tensor<string, []>("q_155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_155_pad_0 = const()[name = tensor<string, []>("q_155_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720866368))), lut = tensor<fp16, [2]>([-0x1.22cp-6, 0x1.234p-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_155_cast = conv(dilations = var_7774, groups = var_6865, pad = q_155_pad_0, pad_type = q_155_pad_type_0, strides = var_7772, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_313_cast)[name = tensor<string, []>("q_155_cast")];
            tensor<int32, [2]> var_7778 = const()[name = tensor<string, []>("op_7778"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7780 = const()[name = tensor<string, []>("op_7780"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_155_pad_type_0 = const()[name = tensor<string, []>("k_155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_155_pad_0 = const()[name = tensor<string, []>("k_155_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721071232))), lut = tensor<fp16, [2]>([-0x1.da8p-7, 0x1.da8p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_155_cast = conv(dilations = var_7780, groups = var_6865, pad = k_155_pad_0, pad_type = k_155_pad_type_0, strides = var_7778, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_155_cast")];
            tensor<int32, [2]> var_7784 = const()[name = tensor<string, []>("op_7784"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7786 = const()[name = tensor<string, []>("op_7786"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_155_pad_type_0 = const()[name = tensor<string, []>("v_155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_155_pad_0 = const()[name = tensor<string, []>("v_155_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721398976))), lut = tensor<fp16, [2]>([-0x1.14cp-6, 0x1.15p-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_155_cast = conv(dilations = var_7786, groups = var_6865, pad = v_155_pad_0, pad_type = v_155_pad_type_0, strides = var_7784, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_155_cast")];
            tensor<int32, [4]> var_7790 = const()[name = tensor<string, []>("op_7790"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7791_cast = reshape(shape = var_7790, x = q_155_cast)[name = tensor<string, []>("op_7791_cast")];
            tensor<int32, [4]> var_7792 = const()[name = tensor<string, []>("op_7792"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7793_cast = reshape(shape = var_7792, x = k_155_cast)[name = tensor<string, []>("op_7793_cast")];
            tensor<int32, [4]> var_7794 = const()[name = tensor<string, []>("op_7794"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7795_cast = reshape(shape = var_7794, x = v_155_cast)[name = tensor<string, []>("op_7795_cast")];
            tensor<bool, []> attn_weights_309_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_309_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_309_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_309_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_309_cast = matmul(transpose_x = attn_weights_309_transpose_x_0, transpose_y = attn_weights_309_transpose_y_0, x = var_7791_cast, y = var_7793_cast)[name = tensor<string, []>("attn_weights_309_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_311_cast = mul(x = attn_weights_309_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_311_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_7799_cast = softmax(axis = var_6849, x = attn_weights_311_cast)[name = tensor<string, []>("op_7799_cast")];
            tensor<bool, []> attn_155_transpose_x_0 = const()[name = tensor<string, []>("attn_155_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_155_transpose_y_0 = const()[name = tensor<string, []>("attn_155_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_155_cast = matmul(transpose_x = attn_155_transpose_x_0, transpose_y = attn_155_transpose_y_0, x = var_7795_cast, y = var_7799_cast)[name = tensor<string, []>("attn_155_cast")];
            tensor<int32, [4]> var_7803 = const()[name = tensor<string, []>("op_7803"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_473_cast = reshape(shape = var_7803, x = attn_155_cast)[name = tensor<string, []>("input_473_cast")];
            tensor<int32, [2]> var_7808 = const()[name = tensor<string, []>("op_7808"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7810 = const()[name = tensor<string, []>("op_7810"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7812_pad_type_0 = const()[name = tensor<string, []>("op_7812_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7812_pad_0 = const()[name = tensor<string, []>("op_7812_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721726720))), lut = tensor<fp16, [2]>([-0x1.4acp-7, 0x1.494p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721931584)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7812_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_7810, groups = var_6865, pad = var_7812_pad_0, pad_type = var_7812_pad_type_0, strides = var_7808, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_473_cast)[name = tensor<string, []>("op_7812_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_233_cast = add(x = var_7812_cast, y = inputs_231_cast)[name = tensor<string, []>("inputs_233_cast")];
            tensor<int32, [1]> var_7816 = const()[name = tensor<string, []>("op_7816"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_233_cast = reduce_mean(axes = var_7816, keep_dims = var_6860, x = inputs_233_cast)[name = tensor<string, []>("channels_mean_233_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_233_cast = sub(x = inputs_233_cast, y = channels_mean_233_cast)[name = tensor<string, []>("zero_mean_233_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_233_cast = mul(x = zero_mean_233_cast, y = zero_mean_233_cast)[name = tensor<string, []>("zero_mean_sq_233_cast")];
            tensor<int32, [1]> var_7820 = const()[name = tensor<string, []>("op_7820"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7821_cast = reduce_mean(axes = var_7820, keep_dims = var_6860, x = zero_mean_sq_233_cast)[name = tensor<string, []>("op_7821_cast")];
            tensor<fp16, []> var_7822_to_fp16 = const()[name = tensor<string, []>("op_7822_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7823_cast = add(x = var_7821_cast, y = var_7822_to_fp16)[name = tensor<string, []>("op_7823_cast")];
            tensor<fp16, []> denom_233_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_233_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_233_cast = rsqrt(epsilon = denom_233_epsilon_0_to_fp16, x = var_7823_cast)[name = tensor<string, []>("denom_233_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_233_cast = mul(x = zero_mean_233_cast, y = denom_233_cast)[name = tensor<string, []>("out_233_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7827_to_fp16 = const()[name = tensor<string, []>("op_7827_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721934208)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7828_cast = add(x = out_233_cast, y = var_7827_to_fp16)[name = tensor<string, []>("op_7828_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7830_to_fp16 = const()[name = tensor<string, []>("op_7830_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721936832)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_475_cast = mul(x = var_7828_cast, y = var_7830_to_fp16)[name = tensor<string, []>("input_475_cast")];
            tensor<int32, [2]> var_7838 = const()[name = tensor<string, []>("op_7838"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7840 = const()[name = tensor<string, []>("op_7840"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7842_pad_type_0 = const()[name = tensor<string, []>("op_7842_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7842_pad_0 = const()[name = tensor<string, []>("op_7842_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721939456))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(728493120))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(728493248)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_7842_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_7840, groups = var_6865, pad = var_7842_pad_0, pad_type = var_7842_pad_type_0, strides = var_7838, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_475_cast)[name = tensor<string, []>("op_7842_cast")];
            tensor<int32, [2]> var_7843_split_sizes_0 = const()[name = tensor<string, []>("op_7843_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_7843_axis_0 = const()[name = tensor<string, []>("op_7843_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_7843_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_7843_cast_1 = split(axis = var_7843_axis_0, split_sizes = var_7843_split_sizes_0, x = var_7842_cast)[name = tensor<string, []>("op_7843_cast")];
            tensor<string, []> var_7845_mode_0 = const()[name = tensor<string, []>("op_7845_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_7845_cast = gelu(mode = var_7845_mode_0, x = var_7843_cast_1)[name = tensor<string, []>("op_7845_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_477_cast = mul(x = var_7843_cast_0, y = var_7845_cast)[name = tensor<string, []>("input_477_cast")];
            tensor<int32, [2]> var_7849 = const()[name = tensor<string, []>("op_7849"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7851 = const()[name = tensor<string, []>("op_7851"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7853_pad_type_0 = const()[name = tensor<string, []>("op_7853_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7853_pad_0 = const()[name = tensor<string, []>("op_7853_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(728513792))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(731790656))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(731790784)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7853_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_7851, groups = var_6865, pad = var_7853_pad_0, pad_type = var_7853_pad_type_0, strides = var_7849, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_477_cast)[name = tensor<string, []>("op_7853_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_235_cast = add(x = var_7853_cast, y = inputs_233_cast)[name = tensor<string, []>("inputs_235_cast")];
            tensor<int32, [1]> var_7863 = const()[name = tensor<string, []>("op_7863"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_235_cast = reduce_mean(axes = var_7863, keep_dims = var_6860, x = inputs_235_cast)[name = tensor<string, []>("channels_mean_235_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_235_cast = sub(x = inputs_235_cast, y = channels_mean_235_cast)[name = tensor<string, []>("zero_mean_235_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_235_cast = mul(x = zero_mean_235_cast, y = zero_mean_235_cast)[name = tensor<string, []>("zero_mean_sq_235_cast")];
            tensor<int32, [1]> var_7867 = const()[name = tensor<string, []>("op_7867"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7868_cast = reduce_mean(axes = var_7867, keep_dims = var_6860, x = zero_mean_sq_235_cast)[name = tensor<string, []>("op_7868_cast")];
            tensor<fp16, []> var_7869_to_fp16 = const()[name = tensor<string, []>("op_7869_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7870_cast = add(x = var_7868_cast, y = var_7869_to_fp16)[name = tensor<string, []>("op_7870_cast")];
            tensor<fp16, []> denom_235_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_235_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_235_cast = rsqrt(epsilon = denom_235_epsilon_0_to_fp16, x = var_7870_cast)[name = tensor<string, []>("denom_235_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_235_cast = mul(x = zero_mean_235_cast, y = denom_235_cast)[name = tensor<string, []>("out_235_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7874_to_fp16 = const()[name = tensor<string, []>("op_7874_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(731793408)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7875_cast = add(x = out_235_cast, y = var_7874_to_fp16)[name = tensor<string, []>("op_7875_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7877_to_fp16 = const()[name = tensor<string, []>("op_7877_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(731796032)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_317_cast = mul(x = var_7875_cast, y = var_7877_to_fp16)[name = tensor<string, []>("hidden_states_317_cast")];
            tensor<int32, [2]> var_7884 = const()[name = tensor<string, []>("op_7884"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7886 = const()[name = tensor<string, []>("op_7886"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_157_pad_type_0 = const()[name = tensor<string, []>("q_157_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_157_pad_0 = const()[name = tensor<string, []>("q_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(731798656))), lut = tensor<fp16, [4]>([-0x1.66cp-5, -0x1.b04p-7, 0x1.adcp-7, 0x1.66cp-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_157_cast = conv(dilations = var_7886, groups = var_6865, pad = q_157_pad_0, pad_type = q_157_pad_type_0, strides = var_7884, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_317_cast)[name = tensor<string, []>("q_157_cast")];
            tensor<int32, [2]> var_7890 = const()[name = tensor<string, []>("op_7890"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7892 = const()[name = tensor<string, []>("op_7892"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_157_pad_type_0 = const()[name = tensor<string, []>("k_157_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_157_pad_0 = const()[name = tensor<string, []>("k_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(732208320))), lut = tensor<fp16, [4]>([-0x1.684p-5, -0x1.b38p-7, 0x1.accp-7, 0x1.66cp-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_157_cast = conv(dilations = var_7892, groups = var_6865, pad = k_157_pad_0, pad_type = k_157_pad_type_0, strides = var_7890, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_317_cast)[name = tensor<string, []>("k_157_cast")];
            tensor<int32, [2]> var_7896 = const()[name = tensor<string, []>("op_7896"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7898 = const()[name = tensor<string, []>("op_7898"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_157_pad_type_0 = const()[name = tensor<string, []>("v_157_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_157_pad_0 = const()[name = tensor<string, []>("v_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(732617984))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(733437248))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_157_cast = conv(dilations = var_7898, groups = var_6865, pad = v_157_pad_0, pad_type = v_157_pad_type_0, strides = var_7896, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_317_cast)[name = tensor<string, []>("v_157_cast")];
            tensor<int32, [4]> var_7902 = const()[name = tensor<string, []>("op_7902"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7903_cast = reshape(shape = var_7902, x = q_157_cast)[name = tensor<string, []>("op_7903_cast")];
            tensor<int32, [4]> var_7904 = const()[name = tensor<string, []>("op_7904"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7905_cast = reshape(shape = var_7904, x = k_157_cast)[name = tensor<string, []>("op_7905_cast")];
            tensor<int32, [4]> var_7906 = const()[name = tensor<string, []>("op_7906"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7907_cast = reshape(shape = var_7906, x = v_157_cast)[name = tensor<string, []>("op_7907_cast")];
            tensor<bool, []> attn_weights_313_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_313_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_313_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_313_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_313_cast = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7903_cast, y = var_7905_cast)[name = tensor<string, []>("attn_weights_313_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_315_cast = mul(x = attn_weights_313_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_315_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_7911_cast = softmax(axis = var_6849, x = attn_weights_315_cast)[name = tensor<string, []>("op_7911_cast")];
            tensor<bool, []> attn_157_transpose_x_0 = const()[name = tensor<string, []>("attn_157_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_157_transpose_y_0 = const()[name = tensor<string, []>("attn_157_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_157_cast = matmul(transpose_x = attn_157_transpose_x_0, transpose_y = attn_157_transpose_y_0, x = var_7907_cast, y = var_7911_cast)[name = tensor<string, []>("attn_157_cast")];
            tensor<int32, [4]> var_7915 = const()[name = tensor<string, []>("op_7915"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_479_cast = reshape(shape = var_7915, x = attn_157_cast)[name = tensor<string, []>("input_479_cast")];
            tensor<int32, [2]> var_7920 = const()[name = tensor<string, []>("op_7920"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7922 = const()[name = tensor<string, []>("op_7922"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7924_pad_type_0 = const()[name = tensor<string, []>("op_7924_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7924_pad_0 = const()[name = tensor<string, []>("op_7924_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(733437376))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734666240))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734666432)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7924_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_7922, groups = var_6865, pad = var_7924_pad_0, pad_type = var_7924_pad_type_0, strides = var_7920, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_479_cast)[name = tensor<string, []>("op_7924_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_237_cast = add(x = var_7924_cast, y = inputs_235_cast)[name = tensor<string, []>("inputs_237_cast")];
            tensor<int32, [1]> var_7928 = const()[name = tensor<string, []>("op_7928"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_237_cast = reduce_mean(axes = var_7928, keep_dims = var_6860, x = inputs_237_cast)[name = tensor<string, []>("channels_mean_237_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_237_cast = sub(x = inputs_237_cast, y = channels_mean_237_cast)[name = tensor<string, []>("zero_mean_237_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_237_cast = mul(x = zero_mean_237_cast, y = zero_mean_237_cast)[name = tensor<string, []>("zero_mean_sq_237_cast")];
            tensor<int32, [1]> var_7932 = const()[name = tensor<string, []>("op_7932"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7933_cast = reduce_mean(axes = var_7932, keep_dims = var_6860, x = zero_mean_sq_237_cast)[name = tensor<string, []>("op_7933_cast")];
            tensor<fp16, []> var_7934_to_fp16 = const()[name = tensor<string, []>("op_7934_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_7935_cast = add(x = var_7933_cast, y = var_7934_to_fp16)[name = tensor<string, []>("op_7935_cast")];
            tensor<fp16, []> denom_237_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_237_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_237_cast = rsqrt(epsilon = denom_237_epsilon_0_to_fp16, x = var_7935_cast)[name = tensor<string, []>("denom_237_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_237_cast = mul(x = zero_mean_237_cast, y = denom_237_cast)[name = tensor<string, []>("out_237_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7939_to_fp16 = const()[name = tensor<string, []>("op_7939_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734669056)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7940_cast = add(x = out_237_cast, y = var_7939_to_fp16)[name = tensor<string, []>("op_7940_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_7942_to_fp16 = const()[name = tensor<string, []>("op_7942_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734671680)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_319_cast = mul(x = var_7940_cast, y = var_7942_to_fp16)[name = tensor<string, []>("hidden_states_319_cast")];
            tensor<int32, [2]> var_7949 = const()[name = tensor<string, []>("op_7949"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7951 = const()[name = tensor<string, []>("op_7951"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_159_pad_type_0 = const()[name = tensor<string, []>("q_159_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_159_pad_0 = const()[name = tensor<string, []>("q_159_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734674304))), lut = tensor<fp16, [2]>([-0x1.144p-6, 0x1.148p-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_159_cast = conv(dilations = var_7951, groups = var_6865, pad = q_159_pad_0, pad_type = q_159_pad_type_0, strides = var_7949, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_319_cast)[name = tensor<string, []>("q_159_cast")];
            tensor<int32, [2]> var_7955 = const()[name = tensor<string, []>("op_7955"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7957 = const()[name = tensor<string, []>("op_7957"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_159_pad_type_0 = const()[name = tensor<string, []>("k_159_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_159_pad_0 = const()[name = tensor<string, []>("k_159_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734879168))), lut = tensor<fp16, [2]>([-0x1.b5cp-7, 0x1.b6p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_159_cast = conv(dilations = var_7957, groups = var_6865, pad = k_159_pad_0, pad_type = k_159_pad_type_0, strides = var_7955, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_159_cast")];
            tensor<int32, [2]> var_7961 = const()[name = tensor<string, []>("op_7961"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7963 = const()[name = tensor<string, []>("op_7963"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_159_pad_type_0 = const()[name = tensor<string, []>("v_159_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_159_pad_0 = const()[name = tensor<string, []>("v_159_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(735206912))), lut = tensor<fp16, [2]>([-0x1.0cp-6, 0x1.0c8p-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_159_cast = conv(dilations = var_7963, groups = var_6865, pad = v_159_pad_0, pad_type = v_159_pad_type_0, strides = var_7961, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_159_cast")];
            tensor<int32, [4]> var_7967 = const()[name = tensor<string, []>("op_7967"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_7968_cast = reshape(shape = var_7967, x = q_159_cast)[name = tensor<string, []>("op_7968_cast")];
            tensor<int32, [4]> var_7969 = const()[name = tensor<string, []>("op_7969"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7970_cast = reshape(shape = var_7969, x = k_159_cast)[name = tensor<string, []>("op_7970_cast")];
            tensor<int32, [4]> var_7971 = const()[name = tensor<string, []>("op_7971"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_7972_cast = reshape(shape = var_7971, x = v_159_cast)[name = tensor<string, []>("op_7972_cast")];
            tensor<bool, []> attn_weights_317_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_317_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_317_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_317_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_317_cast = matmul(transpose_x = attn_weights_317_transpose_x_0, transpose_y = attn_weights_317_transpose_y_0, x = var_7968_cast, y = var_7970_cast)[name = tensor<string, []>("attn_weights_317_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_319_cast = mul(x = attn_weights_317_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_319_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_7976_cast = softmax(axis = var_6849, x = attn_weights_319_cast)[name = tensor<string, []>("op_7976_cast")];
            tensor<bool, []> attn_159_transpose_x_0 = const()[name = tensor<string, []>("attn_159_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_159_transpose_y_0 = const()[name = tensor<string, []>("attn_159_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_159_cast = matmul(transpose_x = attn_159_transpose_x_0, transpose_y = attn_159_transpose_y_0, x = var_7972_cast, y = var_7976_cast)[name = tensor<string, []>("attn_159_cast")];
            tensor<int32, [4]> var_7980 = const()[name = tensor<string, []>("op_7980"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_481_cast = reshape(shape = var_7980, x = attn_159_cast)[name = tensor<string, []>("input_481_cast")];
            tensor<int32, [2]> var_7985 = const()[name = tensor<string, []>("op_7985"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_7987 = const()[name = tensor<string, []>("op_7987"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_7989_pad_type_0 = const()[name = tensor<string, []>("op_7989_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_7989_pad_0 = const()[name = tensor<string, []>("op_7989_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(735534656))), lut = tensor<fp16, [2]>([-0x1.46p-7, 0x1.45cp-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(735739520)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_7989_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_7987, groups = var_6865, pad = var_7989_pad_0, pad_type = var_7989_pad_type_0, strides = var_7985, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_481_cast)[name = tensor<string, []>("op_7989_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_239_cast = add(x = var_7989_cast, y = inputs_237_cast)[name = tensor<string, []>("inputs_239_cast")];
            tensor<int32, [1]> var_7993 = const()[name = tensor<string, []>("op_7993"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_239_cast = reduce_mean(axes = var_7993, keep_dims = var_6860, x = inputs_239_cast)[name = tensor<string, []>("channels_mean_239_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_239_cast = sub(x = inputs_239_cast, y = channels_mean_239_cast)[name = tensor<string, []>("zero_mean_239_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_239_cast = mul(x = zero_mean_239_cast, y = zero_mean_239_cast)[name = tensor<string, []>("zero_mean_sq_239_cast")];
            tensor<int32, [1]> var_7997 = const()[name = tensor<string, []>("op_7997"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_7998_cast = reduce_mean(axes = var_7997, keep_dims = var_6860, x = zero_mean_sq_239_cast)[name = tensor<string, []>("op_7998_cast")];
            tensor<fp16, []> var_7999_to_fp16 = const()[name = tensor<string, []>("op_7999_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8000_cast = add(x = var_7998_cast, y = var_7999_to_fp16)[name = tensor<string, []>("op_8000_cast")];
            tensor<fp16, []> denom_239_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_239_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_239_cast = rsqrt(epsilon = denom_239_epsilon_0_to_fp16, x = var_8000_cast)[name = tensor<string, []>("denom_239_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_239_cast = mul(x = zero_mean_239_cast, y = denom_239_cast)[name = tensor<string, []>("out_239_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8004_to_fp16 = const()[name = tensor<string, []>("op_8004_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(735742144)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8005_cast = add(x = out_239_cast, y = var_8004_to_fp16)[name = tensor<string, []>("op_8005_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8007_to_fp16 = const()[name = tensor<string, []>("op_8007_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(735744768)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_483_cast = mul(x = var_8005_cast, y = var_8007_to_fp16)[name = tensor<string, []>("input_483_cast")];
            tensor<int32, [2]> var_8015 = const()[name = tensor<string, []>("op_8015"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8017 = const()[name = tensor<string, []>("op_8017"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8019_pad_type_0 = const()[name = tensor<string, []>("op_8019_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8019_pad_0 = const()[name = tensor<string, []>("op_8019_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(735747392))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742301056))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742301184)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_8019_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_8017, groups = var_6865, pad = var_8019_pad_0, pad_type = var_8019_pad_type_0, strides = var_8015, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_483_cast)[name = tensor<string, []>("op_8019_cast")];
            tensor<int32, [2]> var_8020_split_sizes_0 = const()[name = tensor<string, []>("op_8020_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_8020_axis_0 = const()[name = tensor<string, []>("op_8020_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_8020_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_8020_cast_1 = split(axis = var_8020_axis_0, split_sizes = var_8020_split_sizes_0, x = var_8019_cast)[name = tensor<string, []>("op_8020_cast")];
            tensor<string, []> var_8022_mode_0 = const()[name = tensor<string, []>("op_8022_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_8022_cast = gelu(mode = var_8022_mode_0, x = var_8020_cast_1)[name = tensor<string, []>("op_8022_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_485_cast = mul(x = var_8020_cast_0, y = var_8022_cast)[name = tensor<string, []>("input_485_cast")];
            tensor<int32, [2]> var_8026 = const()[name = tensor<string, []>("op_8026"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8028 = const()[name = tensor<string, []>("op_8028"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8030_pad_type_0 = const()[name = tensor<string, []>("op_8030_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8030_pad_0 = const()[name = tensor<string, []>("op_8030_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(742321728))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745598592))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745598720)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8030_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_8028, groups = var_6865, pad = var_8030_pad_0, pad_type = var_8030_pad_type_0, strides = var_8026, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_485_cast)[name = tensor<string, []>("op_8030_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_241_cast = add(x = var_8030_cast, y = inputs_239_cast)[name = tensor<string, []>("inputs_241_cast")];
            tensor<int32, [1]> var_8040 = const()[name = tensor<string, []>("op_8040"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_241_cast = reduce_mean(axes = var_8040, keep_dims = var_6860, x = inputs_241_cast)[name = tensor<string, []>("channels_mean_241_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_241_cast = sub(x = inputs_241_cast, y = channels_mean_241_cast)[name = tensor<string, []>("zero_mean_241_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_241_cast = mul(x = zero_mean_241_cast, y = zero_mean_241_cast)[name = tensor<string, []>("zero_mean_sq_241_cast")];
            tensor<int32, [1]> var_8044 = const()[name = tensor<string, []>("op_8044"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8045_cast = reduce_mean(axes = var_8044, keep_dims = var_6860, x = zero_mean_sq_241_cast)[name = tensor<string, []>("op_8045_cast")];
            tensor<fp16, []> var_8046_to_fp16 = const()[name = tensor<string, []>("op_8046_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8047_cast = add(x = var_8045_cast, y = var_8046_to_fp16)[name = tensor<string, []>("op_8047_cast")];
            tensor<fp16, []> denom_241_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_241_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_241_cast = rsqrt(epsilon = denom_241_epsilon_0_to_fp16, x = var_8047_cast)[name = tensor<string, []>("denom_241_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_241_cast = mul(x = zero_mean_241_cast, y = denom_241_cast)[name = tensor<string, []>("out_241_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8051_to_fp16 = const()[name = tensor<string, []>("op_8051_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745601344)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8052_cast = add(x = out_241_cast, y = var_8051_to_fp16)[name = tensor<string, []>("op_8052_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8054_to_fp16 = const()[name = tensor<string, []>("op_8054_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745603968)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_323_cast = mul(x = var_8052_cast, y = var_8054_to_fp16)[name = tensor<string, []>("hidden_states_323_cast")];
            tensor<int32, [2]> var_8061 = const()[name = tensor<string, []>("op_8061"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8063 = const()[name = tensor<string, []>("op_8063"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_161_pad_type_0 = const()[name = tensor<string, []>("q_161_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_161_pad_0 = const()[name = tensor<string, []>("q_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745606592))), lut = tensor<fp16, [2]>([-0x1.7a8p-6, 0x1.7cp-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_161_cast = conv(dilations = var_8063, groups = var_6865, pad = q_161_pad_0, pad_type = q_161_pad_type_0, strides = var_8061, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_323_cast)[name = tensor<string, []>("q_161_cast")];
            tensor<int32, [2]> var_8067 = const()[name = tensor<string, []>("op_8067"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8069 = const()[name = tensor<string, []>("op_8069"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_161_pad_type_0 = const()[name = tensor<string, []>("k_161_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_161_pad_0 = const()[name = tensor<string, []>("k_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(745811456))), lut = tensor<fp16, [2]>([-0x1.7b8p-6, 0x1.7bcp-6]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_161_cast = conv(dilations = var_8069, groups = var_6865, pad = k_161_pad_0, pad_type = k_161_pad_type_0, strides = var_8067, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_323_cast)[name = tensor<string, []>("k_161_cast")];
            tensor<int32, [2]> var_8073 = const()[name = tensor<string, []>("op_8073"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8075 = const()[name = tensor<string, []>("op_8075"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_161_pad_type_0 = const()[name = tensor<string, []>("v_161_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_161_pad_0 = const()[name = tensor<string, []>("v_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(746016320))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(746835584))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_161_cast = conv(dilations = var_8075, groups = var_6865, pad = v_161_pad_0, pad_type = v_161_pad_type_0, strides = var_8073, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_323_cast)[name = tensor<string, []>("v_161_cast")];
            tensor<int32, [4]> var_8079 = const()[name = tensor<string, []>("op_8079"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8080_cast = reshape(shape = var_8079, x = q_161_cast)[name = tensor<string, []>("op_8080_cast")];
            tensor<int32, [4]> var_8081 = const()[name = tensor<string, []>("op_8081"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8082_cast = reshape(shape = var_8081, x = k_161_cast)[name = tensor<string, []>("op_8082_cast")];
            tensor<int32, [4]> var_8083 = const()[name = tensor<string, []>("op_8083"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8084_cast = reshape(shape = var_8083, x = v_161_cast)[name = tensor<string, []>("op_8084_cast")];
            tensor<bool, []> attn_weights_321_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_321_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_321_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_321_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_321_cast = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_8080_cast, y = var_8082_cast)[name = tensor<string, []>("attn_weights_321_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_323_cast = mul(x = attn_weights_321_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_323_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_8088_cast = softmax(axis = var_6849, x = attn_weights_323_cast)[name = tensor<string, []>("op_8088_cast")];
            tensor<bool, []> attn_161_transpose_x_0 = const()[name = tensor<string, []>("attn_161_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_161_transpose_y_0 = const()[name = tensor<string, []>("attn_161_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_161_cast = matmul(transpose_x = attn_161_transpose_x_0, transpose_y = attn_161_transpose_y_0, x = var_8084_cast, y = var_8088_cast)[name = tensor<string, []>("attn_161_cast")];
            tensor<int32, [4]> var_8092 = const()[name = tensor<string, []>("op_8092"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_487_cast = reshape(shape = var_8092, x = attn_161_cast)[name = tensor<string, []>("input_487_cast")];
            tensor<int32, [2]> var_8097 = const()[name = tensor<string, []>("op_8097"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8099 = const()[name = tensor<string, []>("op_8099"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8101_pad_type_0 = const()[name = tensor<string, []>("op_8101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8101_pad_0 = const()[name = tensor<string, []>("op_8101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(746835712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748064576))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748064768)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8101_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_8099, groups = var_6865, pad = var_8101_pad_0, pad_type = var_8101_pad_type_0, strides = var_8097, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_487_cast)[name = tensor<string, []>("op_8101_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_243_cast = add(x = var_8101_cast, y = inputs_241_cast)[name = tensor<string, []>("inputs_243_cast")];
            tensor<int32, [1]> var_8105 = const()[name = tensor<string, []>("op_8105"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_243_cast = reduce_mean(axes = var_8105, keep_dims = var_6860, x = inputs_243_cast)[name = tensor<string, []>("channels_mean_243_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_243_cast = sub(x = inputs_243_cast, y = channels_mean_243_cast)[name = tensor<string, []>("zero_mean_243_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_243_cast = mul(x = zero_mean_243_cast, y = zero_mean_243_cast)[name = tensor<string, []>("zero_mean_sq_243_cast")];
            tensor<int32, [1]> var_8109 = const()[name = tensor<string, []>("op_8109"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8110_cast = reduce_mean(axes = var_8109, keep_dims = var_6860, x = zero_mean_sq_243_cast)[name = tensor<string, []>("op_8110_cast")];
            tensor<fp16, []> var_8111_to_fp16 = const()[name = tensor<string, []>("op_8111_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8112_cast = add(x = var_8110_cast, y = var_8111_to_fp16)[name = tensor<string, []>("op_8112_cast")];
            tensor<fp16, []> denom_243_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_243_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_243_cast = rsqrt(epsilon = denom_243_epsilon_0_to_fp16, x = var_8112_cast)[name = tensor<string, []>("denom_243_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_243_cast = mul(x = zero_mean_243_cast, y = denom_243_cast)[name = tensor<string, []>("out_243_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8116_to_fp16 = const()[name = tensor<string, []>("op_8116_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748067392)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8117_cast = add(x = out_243_cast, y = var_8116_to_fp16)[name = tensor<string, []>("op_8117_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8119_to_fp16 = const()[name = tensor<string, []>("op_8119_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748070016)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_325_cast = mul(x = var_8117_cast, y = var_8119_to_fp16)[name = tensor<string, []>("hidden_states_325_cast")];
            tensor<int32, [2]> var_8126 = const()[name = tensor<string, []>("op_8126"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8128 = const()[name = tensor<string, []>("op_8128"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_163_pad_type_0 = const()[name = tensor<string, []>("q_163_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_163_pad_0 = const()[name = tensor<string, []>("q_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748072640))), lut = tensor<fp16, [2]>([-0x1.f4cp-7, 0x1.f54p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_163_cast = conv(dilations = var_8128, groups = var_6865, pad = q_163_pad_0, pad_type = q_163_pad_type_0, strides = var_8126, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_325_cast)[name = tensor<string, []>("q_163_cast")];
            tensor<int32, [2]> var_8132 = const()[name = tensor<string, []>("op_8132"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8134 = const()[name = tensor<string, []>("op_8134"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_163_pad_type_0 = const()[name = tensor<string, []>("k_163_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_163_pad_0 = const()[name = tensor<string, []>("k_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748277504))), lut = tensor<fp16, [2]>([-0x1.7dcp-7, 0x1.7dp-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_163_cast = conv(dilations = var_8134, groups = var_6865, pad = k_163_pad_0, pad_type = k_163_pad_type_0, strides = var_8132, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_163_cast")];
            tensor<int32, [2]> var_8138 = const()[name = tensor<string, []>("op_8138"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8140 = const()[name = tensor<string, []>("op_8140"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_163_pad_type_0 = const()[name = tensor<string, []>("v_163_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_163_pad_0 = const()[name = tensor<string, []>("v_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748605248))), lut = tensor<fp16, [2]>([-0x1.ed4p-7, 0x1.ea4p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_163_cast = conv(dilations = var_8140, groups = var_6865, pad = v_163_pad_0, pad_type = v_163_pad_type_0, strides = var_8138, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_163_cast")];
            tensor<int32, [4]> var_8144 = const()[name = tensor<string, []>("op_8144"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8145_cast = reshape(shape = var_8144, x = q_163_cast)[name = tensor<string, []>("op_8145_cast")];
            tensor<int32, [4]> var_8146 = const()[name = tensor<string, []>("op_8146"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8147_cast = reshape(shape = var_8146, x = k_163_cast)[name = tensor<string, []>("op_8147_cast")];
            tensor<int32, [4]> var_8148 = const()[name = tensor<string, []>("op_8148"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8149_cast = reshape(shape = var_8148, x = v_163_cast)[name = tensor<string, []>("op_8149_cast")];
            tensor<bool, []> attn_weights_325_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_325_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_325_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_325_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_325_cast = matmul(transpose_x = attn_weights_325_transpose_x_0, transpose_y = attn_weights_325_transpose_y_0, x = var_8145_cast, y = var_8147_cast)[name = tensor<string, []>("attn_weights_325_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_327_cast = mul(x = attn_weights_325_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_327_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_8153_cast = softmax(axis = var_6849, x = attn_weights_327_cast)[name = tensor<string, []>("op_8153_cast")];
            tensor<bool, []> attn_163_transpose_x_0 = const()[name = tensor<string, []>("attn_163_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_163_transpose_y_0 = const()[name = tensor<string, []>("attn_163_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_163_cast = matmul(transpose_x = attn_163_transpose_x_0, transpose_y = attn_163_transpose_y_0, x = var_8149_cast, y = var_8153_cast)[name = tensor<string, []>("attn_163_cast")];
            tensor<int32, [4]> var_8157 = const()[name = tensor<string, []>("op_8157"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_489_cast = reshape(shape = var_8157, x = attn_163_cast)[name = tensor<string, []>("input_489_cast")];
            tensor<int32, [2]> var_8162 = const()[name = tensor<string, []>("op_8162"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8164 = const()[name = tensor<string, []>("op_8164"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8166_pad_type_0 = const()[name = tensor<string, []>("op_8166_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8166_pad_0 = const()[name = tensor<string, []>("op_8166_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748932992))), lut = tensor<fp16, [2]>([-0x1.2bp-7, 0x1.2b4p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(749137856)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8166_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_8164, groups = var_6865, pad = var_8166_pad_0, pad_type = var_8166_pad_type_0, strides = var_8162, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_489_cast)[name = tensor<string, []>("op_8166_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_245_cast = add(x = var_8166_cast, y = inputs_243_cast)[name = tensor<string, []>("inputs_245_cast")];
            tensor<int32, [1]> var_8170 = const()[name = tensor<string, []>("op_8170"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_245_cast = reduce_mean(axes = var_8170, keep_dims = var_6860, x = inputs_245_cast)[name = tensor<string, []>("channels_mean_245_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_245_cast = sub(x = inputs_245_cast, y = channels_mean_245_cast)[name = tensor<string, []>("zero_mean_245_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_245_cast = mul(x = zero_mean_245_cast, y = zero_mean_245_cast)[name = tensor<string, []>("zero_mean_sq_245_cast")];
            tensor<int32, [1]> var_8174 = const()[name = tensor<string, []>("op_8174"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8175_cast = reduce_mean(axes = var_8174, keep_dims = var_6860, x = zero_mean_sq_245_cast)[name = tensor<string, []>("op_8175_cast")];
            tensor<fp16, []> var_8176_to_fp16 = const()[name = tensor<string, []>("op_8176_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8177_cast = add(x = var_8175_cast, y = var_8176_to_fp16)[name = tensor<string, []>("op_8177_cast")];
            tensor<fp16, []> denom_245_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_245_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_245_cast = rsqrt(epsilon = denom_245_epsilon_0_to_fp16, x = var_8177_cast)[name = tensor<string, []>("denom_245_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_245_cast = mul(x = zero_mean_245_cast, y = denom_245_cast)[name = tensor<string, []>("out_245_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8181_to_fp16 = const()[name = tensor<string, []>("op_8181_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(749140480)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8182_cast = add(x = out_245_cast, y = var_8181_to_fp16)[name = tensor<string, []>("op_8182_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8184_to_fp16 = const()[name = tensor<string, []>("op_8184_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(749143104)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_491_cast = mul(x = var_8182_cast, y = var_8184_to_fp16)[name = tensor<string, []>("input_491_cast")];
            tensor<int32, [2]> var_8192 = const()[name = tensor<string, []>("op_8192"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8194 = const()[name = tensor<string, []>("op_8194"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8196_pad_type_0 = const()[name = tensor<string, []>("op_8196_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8196_pad_0 = const()[name = tensor<string, []>("op_8196_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(749145728))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(755699392))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(755699520)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_8196_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_8194, groups = var_6865, pad = var_8196_pad_0, pad_type = var_8196_pad_type_0, strides = var_8192, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_491_cast)[name = tensor<string, []>("op_8196_cast")];
            tensor<int32, [2]> var_8197_split_sizes_0 = const()[name = tensor<string, []>("op_8197_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_8197_axis_0 = const()[name = tensor<string, []>("op_8197_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_8197_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_8197_cast_1 = split(axis = var_8197_axis_0, split_sizes = var_8197_split_sizes_0, x = var_8196_cast)[name = tensor<string, []>("op_8197_cast")];
            tensor<string, []> var_8199_mode_0 = const()[name = tensor<string, []>("op_8199_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_8199_cast = gelu(mode = var_8199_mode_0, x = var_8197_cast_1)[name = tensor<string, []>("op_8199_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_493_cast = mul(x = var_8197_cast_0, y = var_8199_cast)[name = tensor<string, []>("input_493_cast")];
            tensor<int32, [2]> var_8203 = const()[name = tensor<string, []>("op_8203"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8205 = const()[name = tensor<string, []>("op_8205"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8207_pad_type_0 = const()[name = tensor<string, []>("op_8207_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8207_pad_0 = const()[name = tensor<string, []>("op_8207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(755720064))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758996928))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758997056)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8207_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_8205, groups = var_6865, pad = var_8207_pad_0, pad_type = var_8207_pad_type_0, strides = var_8203, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_493_cast)[name = tensor<string, []>("op_8207_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_247_cast = add(x = var_8207_cast, y = inputs_245_cast)[name = tensor<string, []>("inputs_247_cast")];
            tensor<int32, [1]> var_8217 = const()[name = tensor<string, []>("op_8217"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_247_cast = reduce_mean(axes = var_8217, keep_dims = var_6860, x = inputs_247_cast)[name = tensor<string, []>("channels_mean_247_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_247_cast = sub(x = inputs_247_cast, y = channels_mean_247_cast)[name = tensor<string, []>("zero_mean_247_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_247_cast = mul(x = zero_mean_247_cast, y = zero_mean_247_cast)[name = tensor<string, []>("zero_mean_sq_247_cast")];
            tensor<int32, [1]> var_8221 = const()[name = tensor<string, []>("op_8221"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8222_cast = reduce_mean(axes = var_8221, keep_dims = var_6860, x = zero_mean_sq_247_cast)[name = tensor<string, []>("op_8222_cast")];
            tensor<fp16, []> var_8223_to_fp16 = const()[name = tensor<string, []>("op_8223_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8224_cast = add(x = var_8222_cast, y = var_8223_to_fp16)[name = tensor<string, []>("op_8224_cast")];
            tensor<fp16, []> denom_247_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_247_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_247_cast = rsqrt(epsilon = denom_247_epsilon_0_to_fp16, x = var_8224_cast)[name = tensor<string, []>("denom_247_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_247_cast = mul(x = zero_mean_247_cast, y = denom_247_cast)[name = tensor<string, []>("out_247_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8228_to_fp16 = const()[name = tensor<string, []>("op_8228_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758999680)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8229_cast = add(x = out_247_cast, y = var_8228_to_fp16)[name = tensor<string, []>("op_8229_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8231_to_fp16 = const()[name = tensor<string, []>("op_8231_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(759002304)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_329_cast = mul(x = var_8229_cast, y = var_8231_to_fp16)[name = tensor<string, []>("hidden_states_329_cast")];
            tensor<int32, [2]> var_8238 = const()[name = tensor<string, []>("op_8238"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8240 = const()[name = tensor<string, []>("op_8240"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_165_pad_type_0 = const()[name = tensor<string, []>("q_165_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_165_pad_0 = const()[name = tensor<string, []>("q_165_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(759004928))), lut = tensor<fp16, [4]>([-0x1.67p-5, -0x1.afp-7, 0x1.afcp-7, 0x1.668p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_165_cast = conv(dilations = var_8240, groups = var_6865, pad = q_165_pad_0, pad_type = q_165_pad_type_0, strides = var_8238, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_329_cast)[name = tensor<string, []>("q_165_cast")];
            tensor<int32, [2]> var_8244 = const()[name = tensor<string, []>("op_8244"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8246 = const()[name = tensor<string, []>("op_8246"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_165_pad_type_0 = const()[name = tensor<string, []>("k_165_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_165_pad_0 = const()[name = tensor<string, []>("k_165_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(759414592))), lut = tensor<fp16, [4]>([-0x1.678p-5, -0x1.b0cp-7, 0x1.b04p-7, 0x1.678p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_165_cast = conv(dilations = var_8246, groups = var_6865, pad = k_165_pad_0, pad_type = k_165_pad_type_0, strides = var_8244, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_329_cast)[name = tensor<string, []>("k_165_cast")];
            tensor<int32, [2]> var_8250 = const()[name = tensor<string, []>("op_8250"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8252 = const()[name = tensor<string, []>("op_8252"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_165_pad_type_0 = const()[name = tensor<string, []>("v_165_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_165_pad_0 = const()[name = tensor<string, []>("v_165_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(759824256))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(760643520))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_165_cast = conv(dilations = var_8252, groups = var_6865, pad = v_165_pad_0, pad_type = v_165_pad_type_0, strides = var_8250, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_329_cast)[name = tensor<string, []>("v_165_cast")];
            tensor<int32, [4]> var_8256 = const()[name = tensor<string, []>("op_8256"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8257_cast = reshape(shape = var_8256, x = q_165_cast)[name = tensor<string, []>("op_8257_cast")];
            tensor<int32, [4]> var_8258 = const()[name = tensor<string, []>("op_8258"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8259_cast = reshape(shape = var_8258, x = k_165_cast)[name = tensor<string, []>("op_8259_cast")];
            tensor<int32, [4]> var_8260 = const()[name = tensor<string, []>("op_8260"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8261_cast = reshape(shape = var_8260, x = v_165_cast)[name = tensor<string, []>("op_8261_cast")];
            tensor<bool, []> attn_weights_329_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_329_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_329_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_329_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_329_cast = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_8257_cast, y = var_8259_cast)[name = tensor<string, []>("attn_weights_329_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_331_cast = mul(x = attn_weights_329_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_331_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_8265_cast = softmax(axis = var_6849, x = attn_weights_331_cast)[name = tensor<string, []>("op_8265_cast")];
            tensor<bool, []> attn_165_transpose_x_0 = const()[name = tensor<string, []>("attn_165_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_165_transpose_y_0 = const()[name = tensor<string, []>("attn_165_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_165_cast = matmul(transpose_x = attn_165_transpose_x_0, transpose_y = attn_165_transpose_y_0, x = var_8261_cast, y = var_8265_cast)[name = tensor<string, []>("attn_165_cast")];
            tensor<int32, [4]> var_8269 = const()[name = tensor<string, []>("op_8269"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_495_cast = reshape(shape = var_8269, x = attn_165_cast)[name = tensor<string, []>("input_495_cast")];
            tensor<int32, [2]> var_8274 = const()[name = tensor<string, []>("op_8274"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8276 = const()[name = tensor<string, []>("op_8276"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8278_pad_type_0 = const()[name = tensor<string, []>("op_8278_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8278_pad_0 = const()[name = tensor<string, []>("op_8278_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(760643648))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(761462912))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(761463040)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8278_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_8276, groups = var_6865, pad = var_8278_pad_0, pad_type = var_8278_pad_type_0, strides = var_8274, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_495_cast)[name = tensor<string, []>("op_8278_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_249_cast = add(x = var_8278_cast, y = inputs_247_cast)[name = tensor<string, []>("inputs_249_cast")];
            tensor<int32, [1]> var_8282 = const()[name = tensor<string, []>("op_8282"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_249_cast = reduce_mean(axes = var_8282, keep_dims = var_6860, x = inputs_249_cast)[name = tensor<string, []>("channels_mean_249_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_249_cast = sub(x = inputs_249_cast, y = channels_mean_249_cast)[name = tensor<string, []>("zero_mean_249_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_249_cast = mul(x = zero_mean_249_cast, y = zero_mean_249_cast)[name = tensor<string, []>("zero_mean_sq_249_cast")];
            tensor<int32, [1]> var_8286 = const()[name = tensor<string, []>("op_8286"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8287_cast = reduce_mean(axes = var_8286, keep_dims = var_6860, x = zero_mean_sq_249_cast)[name = tensor<string, []>("op_8287_cast")];
            tensor<fp16, []> var_8288_to_fp16 = const()[name = tensor<string, []>("op_8288_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8289_cast = add(x = var_8287_cast, y = var_8288_to_fp16)[name = tensor<string, []>("op_8289_cast")];
            tensor<fp16, []> denom_249_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_249_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_249_cast = rsqrt(epsilon = denom_249_epsilon_0_to_fp16, x = var_8289_cast)[name = tensor<string, []>("denom_249_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_249_cast = mul(x = zero_mean_249_cast, y = denom_249_cast)[name = tensor<string, []>("out_249_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8293_to_fp16 = const()[name = tensor<string, []>("op_8293_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(761465664)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8294_cast = add(x = out_249_cast, y = var_8293_to_fp16)[name = tensor<string, []>("op_8294_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8296_to_fp16 = const()[name = tensor<string, []>("op_8296_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(761468288)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_331_cast = mul(x = var_8294_cast, y = var_8296_to_fp16)[name = tensor<string, []>("hidden_states_331_cast")];
            tensor<int32, [2]> var_8303 = const()[name = tensor<string, []>("op_8303"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8305 = const()[name = tensor<string, []>("op_8305"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_167_pad_type_0 = const()[name = tensor<string, []>("q_167_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_167_pad_0 = const()[name = tensor<string, []>("q_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(761470912))), lut = tensor<fp16, [2]>([-0x1.d44p-7, 0x1.d3cp-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_167_cast = conv(dilations = var_8305, groups = var_6865, pad = q_167_pad_0, pad_type = q_167_pad_type_0, strides = var_8303, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_331_cast)[name = tensor<string, []>("q_167_cast")];
            tensor<int32, [2]> var_8309 = const()[name = tensor<string, []>("op_8309"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8311 = const()[name = tensor<string, []>("op_8311"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_167_pad_type_0 = const()[name = tensor<string, []>("k_167_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_167_pad_0 = const()[name = tensor<string, []>("k_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(761675776))), lut = tensor<fp16, [2]>([-0x1.59cp-7, 0x1.59p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_167_cast = conv(dilations = var_8311, groups = var_6865, pad = k_167_pad_0, pad_type = k_167_pad_type_0, strides = var_8309, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_167_cast")];
            tensor<int32, [2]> var_8315 = const()[name = tensor<string, []>("op_8315"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8317 = const()[name = tensor<string, []>("op_8317"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_167_pad_type_0 = const()[name = tensor<string, []>("v_167_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_167_pad_0 = const()[name = tensor<string, []>("v_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762003520))), lut = tensor<fp16, [2]>([-0x1.c78p-7, 0x1.c88p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_167_cast = conv(dilations = var_8317, groups = var_6865, pad = v_167_pad_0, pad_type = v_167_pad_type_0, strides = var_8315, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_167_cast")];
            tensor<int32, [4]> var_8321 = const()[name = tensor<string, []>("op_8321"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8322_cast = reshape(shape = var_8321, x = q_167_cast)[name = tensor<string, []>("op_8322_cast")];
            tensor<int32, [4]> var_8323 = const()[name = tensor<string, []>("op_8323"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8324_cast = reshape(shape = var_8323, x = k_167_cast)[name = tensor<string, []>("op_8324_cast")];
            tensor<int32, [4]> var_8325 = const()[name = tensor<string, []>("op_8325"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8326_cast = reshape(shape = var_8325, x = v_167_cast)[name = tensor<string, []>("op_8326_cast")];
            tensor<bool, []> attn_weights_333_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_333_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_333_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_333_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_333_cast = matmul(transpose_x = attn_weights_333_transpose_x_0, transpose_y = attn_weights_333_transpose_y_0, x = var_8322_cast, y = var_8324_cast)[name = tensor<string, []>("attn_weights_333_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_335_cast = mul(x = attn_weights_333_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_335_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_8330_cast = softmax(axis = var_6849, x = attn_weights_335_cast)[name = tensor<string, []>("op_8330_cast")];
            tensor<bool, []> attn_167_transpose_x_0 = const()[name = tensor<string, []>("attn_167_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_167_transpose_y_0 = const()[name = tensor<string, []>("attn_167_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_167_cast = matmul(transpose_x = attn_167_transpose_x_0, transpose_y = attn_167_transpose_y_0, x = var_8326_cast, y = var_8330_cast)[name = tensor<string, []>("attn_167_cast")];
            tensor<int32, [4]> var_8334 = const()[name = tensor<string, []>("op_8334"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_497_cast = reshape(shape = var_8334, x = attn_167_cast)[name = tensor<string, []>("input_497_cast")];
            tensor<int32, [2]> var_8339 = const()[name = tensor<string, []>("op_8339"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8341 = const()[name = tensor<string, []>("op_8341"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8343_pad_type_0 = const()[name = tensor<string, []>("op_8343_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8343_pad_0 = const()[name = tensor<string, []>("op_8343_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762331264))), lut = tensor<fp16, [2]>([-0x1.1a4p-7, 0x1.194p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762536128)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8343_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_8341, groups = var_6865, pad = var_8343_pad_0, pad_type = var_8343_pad_type_0, strides = var_8339, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_497_cast)[name = tensor<string, []>("op_8343_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_251_cast = add(x = var_8343_cast, y = inputs_249_cast)[name = tensor<string, []>("inputs_251_cast")];
            tensor<int32, [1]> var_8347 = const()[name = tensor<string, []>("op_8347"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_251_cast = reduce_mean(axes = var_8347, keep_dims = var_6860, x = inputs_251_cast)[name = tensor<string, []>("channels_mean_251_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_251_cast = sub(x = inputs_251_cast, y = channels_mean_251_cast)[name = tensor<string, []>("zero_mean_251_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_251_cast = mul(x = zero_mean_251_cast, y = zero_mean_251_cast)[name = tensor<string, []>("zero_mean_sq_251_cast")];
            tensor<int32, [1]> var_8351 = const()[name = tensor<string, []>("op_8351"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8352_cast = reduce_mean(axes = var_8351, keep_dims = var_6860, x = zero_mean_sq_251_cast)[name = tensor<string, []>("op_8352_cast")];
            tensor<fp16, []> var_8353_to_fp16 = const()[name = tensor<string, []>("op_8353_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8354_cast = add(x = var_8352_cast, y = var_8353_to_fp16)[name = tensor<string, []>("op_8354_cast")];
            tensor<fp16, []> denom_251_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_251_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_251_cast = rsqrt(epsilon = denom_251_epsilon_0_to_fp16, x = var_8354_cast)[name = tensor<string, []>("denom_251_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_251_cast = mul(x = zero_mean_251_cast, y = denom_251_cast)[name = tensor<string, []>("out_251_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8358_to_fp16 = const()[name = tensor<string, []>("op_8358_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762538752)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8359_cast = add(x = out_251_cast, y = var_8358_to_fp16)[name = tensor<string, []>("op_8359_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8361_to_fp16 = const()[name = tensor<string, []>("op_8361_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762541376)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_499_cast = mul(x = var_8359_cast, y = var_8361_to_fp16)[name = tensor<string, []>("input_499_cast")];
            tensor<int32, [2]> var_8369 = const()[name = tensor<string, []>("op_8369"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8371 = const()[name = tensor<string, []>("op_8371"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8373_pad_type_0 = const()[name = tensor<string, []>("op_8373_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8373_pad_0 = const()[name = tensor<string, []>("op_8373_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762544000))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(769097664))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(769097792)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_8373_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_8371, groups = var_6865, pad = var_8373_pad_0, pad_type = var_8373_pad_type_0, strides = var_8369, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_499_cast)[name = tensor<string, []>("op_8373_cast")];
            tensor<int32, [2]> var_8374_split_sizes_0 = const()[name = tensor<string, []>("op_8374_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_8374_axis_0 = const()[name = tensor<string, []>("op_8374_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_8374_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_8374_cast_1 = split(axis = var_8374_axis_0, split_sizes = var_8374_split_sizes_0, x = var_8373_cast)[name = tensor<string, []>("op_8374_cast")];
            tensor<string, []> var_8376_mode_0 = const()[name = tensor<string, []>("op_8376_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_8376_cast = gelu(mode = var_8376_mode_0, x = var_8374_cast_1)[name = tensor<string, []>("op_8376_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_501_cast = mul(x = var_8374_cast_0, y = var_8376_cast)[name = tensor<string, []>("input_501_cast")];
            tensor<int32, [2]> var_8380 = const()[name = tensor<string, []>("op_8380"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8382 = const()[name = tensor<string, []>("op_8382"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8384_pad_type_0 = const()[name = tensor<string, []>("op_8384_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8384_pad_0 = const()[name = tensor<string, []>("op_8384_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(769118336))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(772395200))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(772395328)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8384_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_8382, groups = var_6865, pad = var_8384_pad_0, pad_type = var_8384_pad_type_0, strides = var_8380, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_501_cast)[name = tensor<string, []>("op_8384_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_253_cast = add(x = var_8384_cast, y = inputs_251_cast)[name = tensor<string, []>("inputs_253_cast")];
            tensor<int32, [1]> var_8394 = const()[name = tensor<string, []>("op_8394"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_253_cast = reduce_mean(axes = var_8394, keep_dims = var_6860, x = inputs_253_cast)[name = tensor<string, []>("channels_mean_253_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_253_cast = sub(x = inputs_253_cast, y = channels_mean_253_cast)[name = tensor<string, []>("zero_mean_253_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_253_cast = mul(x = zero_mean_253_cast, y = zero_mean_253_cast)[name = tensor<string, []>("zero_mean_sq_253_cast")];
            tensor<int32, [1]> var_8398 = const()[name = tensor<string, []>("op_8398"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8399_cast = reduce_mean(axes = var_8398, keep_dims = var_6860, x = zero_mean_sq_253_cast)[name = tensor<string, []>("op_8399_cast")];
            tensor<fp16, []> var_8400_to_fp16 = const()[name = tensor<string, []>("op_8400_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8401_cast = add(x = var_8399_cast, y = var_8400_to_fp16)[name = tensor<string, []>("op_8401_cast")];
            tensor<fp16, []> denom_253_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_253_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_253_cast = rsqrt(epsilon = denom_253_epsilon_0_to_fp16, x = var_8401_cast)[name = tensor<string, []>("denom_253_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_253_cast = mul(x = zero_mean_253_cast, y = denom_253_cast)[name = tensor<string, []>("out_253_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8405_to_fp16 = const()[name = tensor<string, []>("op_8405_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(772397952)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8406_cast = add(x = out_253_cast, y = var_8405_to_fp16)[name = tensor<string, []>("op_8406_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8408_to_fp16 = const()[name = tensor<string, []>("op_8408_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(772400576)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_335_cast = mul(x = var_8406_cast, y = var_8408_to_fp16)[name = tensor<string, []>("hidden_states_335_cast")];
            tensor<int32, [2]> var_8415 = const()[name = tensor<string, []>("op_8415"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8417 = const()[name = tensor<string, []>("op_8417"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_169_pad_type_0 = const()[name = tensor<string, []>("q_169_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_169_pad_0 = const()[name = tensor<string, []>("q_169_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(772403200))), lut = tensor<fp16, [4]>([-0x1.6ap-5, -0x1.b7p-7, 0x1.afp-7, 0x1.684p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_169_cast = conv(dilations = var_8417, groups = var_6865, pad = q_169_pad_0, pad_type = q_169_pad_type_0, strides = var_8415, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_335_cast)[name = tensor<string, []>("q_169_cast")];
            tensor<int32, [2]> var_8421 = const()[name = tensor<string, []>("op_8421"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8423 = const()[name = tensor<string, []>("op_8423"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_169_pad_type_0 = const()[name = tensor<string, []>("k_169_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_169_pad_0 = const()[name = tensor<string, []>("k_169_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(772812864))), lut = tensor<fp16, [4]>([-0x1.69p-5, -0x1.b34p-7, 0x1.b2cp-7, 0x1.688p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_169_cast = conv(dilations = var_8423, groups = var_6865, pad = k_169_pad_0, pad_type = k_169_pad_type_0, strides = var_8421, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_335_cast)[name = tensor<string, []>("k_169_cast")];
            tensor<int32, [2]> var_8427 = const()[name = tensor<string, []>("op_8427"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8429 = const()[name = tensor<string, []>("op_8429"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_169_pad_type_0 = const()[name = tensor<string, []>("v_169_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_169_pad_0 = const()[name = tensor<string, []>("v_169_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(773222528))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774041792))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_169_cast = conv(dilations = var_8429, groups = var_6865, pad = v_169_pad_0, pad_type = v_169_pad_type_0, strides = var_8427, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_335_cast)[name = tensor<string, []>("v_169_cast")];
            tensor<int32, [4]> var_8433 = const()[name = tensor<string, []>("op_8433"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8434_cast = reshape(shape = var_8433, x = q_169_cast)[name = tensor<string, []>("op_8434_cast")];
            tensor<int32, [4]> var_8435 = const()[name = tensor<string, []>("op_8435"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8436_cast = reshape(shape = var_8435, x = k_169_cast)[name = tensor<string, []>("op_8436_cast")];
            tensor<int32, [4]> var_8437 = const()[name = tensor<string, []>("op_8437"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8438_cast = reshape(shape = var_8437, x = v_169_cast)[name = tensor<string, []>("op_8438_cast")];
            tensor<bool, []> attn_weights_337_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_337_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_337_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_337_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_337_cast = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_8434_cast, y = var_8436_cast)[name = tensor<string, []>("attn_weights_337_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_339_cast = mul(x = attn_weights_337_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_339_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_8442_cast = softmax(axis = var_6849, x = attn_weights_339_cast)[name = tensor<string, []>("op_8442_cast")];
            tensor<bool, []> attn_169_transpose_x_0 = const()[name = tensor<string, []>("attn_169_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_169_transpose_y_0 = const()[name = tensor<string, []>("attn_169_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_169_cast = matmul(transpose_x = attn_169_transpose_x_0, transpose_y = attn_169_transpose_y_0, x = var_8438_cast, y = var_8442_cast)[name = tensor<string, []>("attn_169_cast")];
            tensor<int32, [4]> var_8446 = const()[name = tensor<string, []>("op_8446"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_503_cast = reshape(shape = var_8446, x = attn_169_cast)[name = tensor<string, []>("input_503_cast")];
            tensor<int32, [2]> var_8451 = const()[name = tensor<string, []>("op_8451"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8453 = const()[name = tensor<string, []>("op_8453"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8455_pad_type_0 = const()[name = tensor<string, []>("op_8455_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8455_pad_0 = const()[name = tensor<string, []>("op_8455_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774041920))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774861184))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774861312)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8455_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_8453, groups = var_6865, pad = var_8455_pad_0, pad_type = var_8455_pad_type_0, strides = var_8451, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_503_cast)[name = tensor<string, []>("op_8455_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_255_cast = add(x = var_8455_cast, y = inputs_253_cast)[name = tensor<string, []>("inputs_255_cast")];
            tensor<int32, [1]> var_8459 = const()[name = tensor<string, []>("op_8459"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_255_cast = reduce_mean(axes = var_8459, keep_dims = var_6860, x = inputs_255_cast)[name = tensor<string, []>("channels_mean_255_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_255_cast = sub(x = inputs_255_cast, y = channels_mean_255_cast)[name = tensor<string, []>("zero_mean_255_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_255_cast = mul(x = zero_mean_255_cast, y = zero_mean_255_cast)[name = tensor<string, []>("zero_mean_sq_255_cast")];
            tensor<int32, [1]> var_8463 = const()[name = tensor<string, []>("op_8463"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8464_cast = reduce_mean(axes = var_8463, keep_dims = var_6860, x = zero_mean_sq_255_cast)[name = tensor<string, []>("op_8464_cast")];
            tensor<fp16, []> var_8465_to_fp16 = const()[name = tensor<string, []>("op_8465_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8466_cast = add(x = var_8464_cast, y = var_8465_to_fp16)[name = tensor<string, []>("op_8466_cast")];
            tensor<fp16, []> denom_255_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_255_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_255_cast = rsqrt(epsilon = denom_255_epsilon_0_to_fp16, x = var_8466_cast)[name = tensor<string, []>("denom_255_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_255_cast = mul(x = zero_mean_255_cast, y = denom_255_cast)[name = tensor<string, []>("out_255_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8470_to_fp16 = const()[name = tensor<string, []>("op_8470_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774863936)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8471_cast = add(x = out_255_cast, y = var_8470_to_fp16)[name = tensor<string, []>("op_8471_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8473_to_fp16 = const()[name = tensor<string, []>("op_8473_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774866560)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_337_cast = mul(x = var_8471_cast, y = var_8473_to_fp16)[name = tensor<string, []>("hidden_states_337_cast")];
            tensor<int32, [2]> var_8480 = const()[name = tensor<string, []>("op_8480"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8482 = const()[name = tensor<string, []>("op_8482"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_171_pad_type_0 = const()[name = tensor<string, []>("q_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_171_pad_0 = const()[name = tensor<string, []>("q_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774869184))), lut = tensor<fp16, [2]>([-0x1.b48p-7, 0x1.b54p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_171_cast = conv(dilations = var_8482, groups = var_6865, pad = q_171_pad_0, pad_type = q_171_pad_type_0, strides = var_8480, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_337_cast)[name = tensor<string, []>("q_171_cast")];
            tensor<int32, [2]> var_8486 = const()[name = tensor<string, []>("op_8486"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8488 = const()[name = tensor<string, []>("op_8488"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_171_pad_type_0 = const()[name = tensor<string, []>("k_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_171_pad_0 = const()[name = tensor<string, []>("k_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775074048))), lut = tensor<fp16, [2]>([-0x1.38p-7, 0x1.37p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_171_cast = conv(dilations = var_8488, groups = var_6865, pad = k_171_pad_0, pad_type = k_171_pad_type_0, strides = var_8486, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_171_cast")];
            tensor<int32, [2]> var_8492 = const()[name = tensor<string, []>("op_8492"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8494 = const()[name = tensor<string, []>("op_8494"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_171_pad_type_0 = const()[name = tensor<string, []>("v_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_171_pad_0 = const()[name = tensor<string, []>("v_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775401792))), lut = tensor<fp16, [2]>([-0x1.a1p-7, 0x1.a28p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_171_cast = conv(dilations = var_8494, groups = var_6865, pad = v_171_pad_0, pad_type = v_171_pad_type_0, strides = var_8492, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_171_cast")];
            tensor<int32, [4]> var_8498 = const()[name = tensor<string, []>("op_8498"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8499_cast = reshape(shape = var_8498, x = q_171_cast)[name = tensor<string, []>("op_8499_cast")];
            tensor<int32, [4]> var_8500 = const()[name = tensor<string, []>("op_8500"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8501_cast = reshape(shape = var_8500, x = k_171_cast)[name = tensor<string, []>("op_8501_cast")];
            tensor<int32, [4]> var_8502 = const()[name = tensor<string, []>("op_8502"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8503_cast = reshape(shape = var_8502, x = v_171_cast)[name = tensor<string, []>("op_8503_cast")];
            tensor<bool, []> attn_weights_341_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_341_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_341_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_341_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_341_cast = matmul(transpose_x = attn_weights_341_transpose_x_0, transpose_y = attn_weights_341_transpose_y_0, x = var_8499_cast, y = var_8501_cast)[name = tensor<string, []>("attn_weights_341_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_343_cast = mul(x = attn_weights_341_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_343_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_8507_cast = softmax(axis = var_6849, x = attn_weights_343_cast)[name = tensor<string, []>("op_8507_cast")];
            tensor<bool, []> attn_171_transpose_x_0 = const()[name = tensor<string, []>("attn_171_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_171_transpose_y_0 = const()[name = tensor<string, []>("attn_171_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_171_cast = matmul(transpose_x = attn_171_transpose_x_0, transpose_y = attn_171_transpose_y_0, x = var_8503_cast, y = var_8507_cast)[name = tensor<string, []>("attn_171_cast")];
            tensor<int32, [4]> var_8511 = const()[name = tensor<string, []>("op_8511"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_505_cast = reshape(shape = var_8511, x = attn_171_cast)[name = tensor<string, []>("input_505_cast")];
            tensor<int32, [2]> var_8516 = const()[name = tensor<string, []>("op_8516"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8518 = const()[name = tensor<string, []>("op_8518"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8520_pad_type_0 = const()[name = tensor<string, []>("op_8520_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8520_pad_0 = const()[name = tensor<string, []>("op_8520_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775729536))), lut = tensor<fp16, [2]>([-0x1.048p-7, 0x1.04cp-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775934400)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8520_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_8518, groups = var_6865, pad = var_8520_pad_0, pad_type = var_8520_pad_type_0, strides = var_8516, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_505_cast)[name = tensor<string, []>("op_8520_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_257_cast = add(x = var_8520_cast, y = inputs_255_cast)[name = tensor<string, []>("inputs_257_cast")];
            tensor<int32, [1]> var_8524 = const()[name = tensor<string, []>("op_8524"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_257_cast = reduce_mean(axes = var_8524, keep_dims = var_6860, x = inputs_257_cast)[name = tensor<string, []>("channels_mean_257_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_257_cast = sub(x = inputs_257_cast, y = channels_mean_257_cast)[name = tensor<string, []>("zero_mean_257_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_257_cast = mul(x = zero_mean_257_cast, y = zero_mean_257_cast)[name = tensor<string, []>("zero_mean_sq_257_cast")];
            tensor<int32, [1]> var_8528 = const()[name = tensor<string, []>("op_8528"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8529_cast = reduce_mean(axes = var_8528, keep_dims = var_6860, x = zero_mean_sq_257_cast)[name = tensor<string, []>("op_8529_cast")];
            tensor<fp16, []> var_8530_to_fp16 = const()[name = tensor<string, []>("op_8530_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8531_cast = add(x = var_8529_cast, y = var_8530_to_fp16)[name = tensor<string, []>("op_8531_cast")];
            tensor<fp16, []> denom_257_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_257_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_257_cast = rsqrt(epsilon = denom_257_epsilon_0_to_fp16, x = var_8531_cast)[name = tensor<string, []>("denom_257_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_257_cast = mul(x = zero_mean_257_cast, y = denom_257_cast)[name = tensor<string, []>("out_257_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8535_to_fp16 = const()[name = tensor<string, []>("op_8535_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775937024)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8536_cast = add(x = out_257_cast, y = var_8535_to_fp16)[name = tensor<string, []>("op_8536_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8538_to_fp16 = const()[name = tensor<string, []>("op_8538_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775939648)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_507_cast = mul(x = var_8536_cast, y = var_8538_to_fp16)[name = tensor<string, []>("input_507_cast")];
            tensor<int32, [2]> var_8546 = const()[name = tensor<string, []>("op_8546"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8548 = const()[name = tensor<string, []>("op_8548"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8550_pad_type_0 = const()[name = tensor<string, []>("op_8550_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8550_pad_0 = const()[name = tensor<string, []>("op_8550_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775942272))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(782495936))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(782496064)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_8550_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_8548, groups = var_6865, pad = var_8550_pad_0, pad_type = var_8550_pad_type_0, strides = var_8546, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_507_cast)[name = tensor<string, []>("op_8550_cast")];
            tensor<int32, [2]> var_8551_split_sizes_0 = const()[name = tensor<string, []>("op_8551_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_8551_axis_0 = const()[name = tensor<string, []>("op_8551_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_8551_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_8551_cast_1 = split(axis = var_8551_axis_0, split_sizes = var_8551_split_sizes_0, x = var_8550_cast)[name = tensor<string, []>("op_8551_cast")];
            tensor<string, []> var_8553_mode_0 = const()[name = tensor<string, []>("op_8553_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_8553_cast = gelu(mode = var_8553_mode_0, x = var_8551_cast_1)[name = tensor<string, []>("op_8553_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_509_cast = mul(x = var_8551_cast_0, y = var_8553_cast)[name = tensor<string, []>("input_509_cast")];
            tensor<int32, [2]> var_8557 = const()[name = tensor<string, []>("op_8557"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8559 = const()[name = tensor<string, []>("op_8559"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8561_pad_type_0 = const()[name = tensor<string, []>("op_8561_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8561_pad_0 = const()[name = tensor<string, []>("op_8561_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(782516608))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(785793472))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(785793600)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8561_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_8559, groups = var_6865, pad = var_8561_pad_0, pad_type = var_8561_pad_type_0, strides = var_8557, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_509_cast)[name = tensor<string, []>("op_8561_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_259_cast = add(x = var_8561_cast, y = inputs_257_cast)[name = tensor<string, []>("inputs_259_cast")];
            tensor<int32, [1]> var_8571 = const()[name = tensor<string, []>("op_8571"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_259_cast = reduce_mean(axes = var_8571, keep_dims = var_6860, x = inputs_259_cast)[name = tensor<string, []>("channels_mean_259_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_259_cast = sub(x = inputs_259_cast, y = channels_mean_259_cast)[name = tensor<string, []>("zero_mean_259_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_259_cast = mul(x = zero_mean_259_cast, y = zero_mean_259_cast)[name = tensor<string, []>("zero_mean_sq_259_cast")];
            tensor<int32, [1]> var_8575 = const()[name = tensor<string, []>("op_8575"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8576_cast = reduce_mean(axes = var_8575, keep_dims = var_6860, x = zero_mean_sq_259_cast)[name = tensor<string, []>("op_8576_cast")];
            tensor<fp16, []> var_8577_to_fp16 = const()[name = tensor<string, []>("op_8577_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8578_cast = add(x = var_8576_cast, y = var_8577_to_fp16)[name = tensor<string, []>("op_8578_cast")];
            tensor<fp16, []> denom_259_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_259_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_259_cast = rsqrt(epsilon = denom_259_epsilon_0_to_fp16, x = var_8578_cast)[name = tensor<string, []>("denom_259_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_259_cast = mul(x = zero_mean_259_cast, y = denom_259_cast)[name = tensor<string, []>("out_259_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8582_to_fp16 = const()[name = tensor<string, []>("op_8582_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(785796224)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8583_cast = add(x = out_259_cast, y = var_8582_to_fp16)[name = tensor<string, []>("op_8583_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8585_to_fp16 = const()[name = tensor<string, []>("op_8585_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(785798848)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_341_cast = mul(x = var_8583_cast, y = var_8585_to_fp16)[name = tensor<string, []>("hidden_states_341_cast")];
            tensor<int32, [2]> var_8592 = const()[name = tensor<string, []>("op_8592"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8594 = const()[name = tensor<string, []>("op_8594"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_173_pad_type_0 = const()[name = tensor<string, []>("q_173_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_173_pad_0 = const()[name = tensor<string, []>("q_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(785801472))), lut = tensor<fp16, [4]>([-0x1.6b4p-5, -0x1.b6cp-7, 0x1.b3cp-7, 0x1.6a4p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_173_cast = conv(dilations = var_8594, groups = var_6865, pad = q_173_pad_0, pad_type = q_173_pad_type_0, strides = var_8592, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_341_cast)[name = tensor<string, []>("q_173_cast")];
            tensor<int32, [2]> var_8598 = const()[name = tensor<string, []>("op_8598"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8600 = const()[name = tensor<string, []>("op_8600"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_173_pad_type_0 = const()[name = tensor<string, []>("k_173_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_173_pad_0 = const()[name = tensor<string, []>("k_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(786211136))), lut = tensor<fp16, [4]>([-0x1.69cp-5, -0x1.b24p-7, 0x1.b88p-7, 0x1.6b8p-5]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_173_cast = conv(dilations = var_8600, groups = var_6865, pad = k_173_pad_0, pad_type = k_173_pad_type_0, strides = var_8598, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_341_cast)[name = tensor<string, []>("k_173_cast")];
            tensor<int32, [2]> var_8604 = const()[name = tensor<string, []>("op_8604"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8606 = const()[name = tensor<string, []>("op_8606"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_173_pad_type_0 = const()[name = tensor<string, []>("v_173_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_173_pad_0 = const()[name = tensor<string, []>("v_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(786620800))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(787440064))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_173_cast = conv(dilations = var_8606, groups = var_6865, pad = v_173_pad_0, pad_type = v_173_pad_type_0, strides = var_8604, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_341_cast)[name = tensor<string, []>("v_173_cast")];
            tensor<int32, [4]> var_8610 = const()[name = tensor<string, []>("op_8610"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8611_cast = reshape(shape = var_8610, x = q_173_cast)[name = tensor<string, []>("op_8611_cast")];
            tensor<int32, [4]> var_8612 = const()[name = tensor<string, []>("op_8612"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8613_cast = reshape(shape = var_8612, x = k_173_cast)[name = tensor<string, []>("op_8613_cast")];
            tensor<int32, [4]> var_8614 = const()[name = tensor<string, []>("op_8614"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8615_cast = reshape(shape = var_8614, x = v_173_cast)[name = tensor<string, []>("op_8615_cast")];
            tensor<bool, []> attn_weights_345_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_345_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_345_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_345_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_345_cast = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_8611_cast, y = var_8613_cast)[name = tensor<string, []>("attn_weights_345_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_347_cast = mul(x = attn_weights_345_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_347_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_8619_cast = softmax(axis = var_6849, x = attn_weights_347_cast)[name = tensor<string, []>("op_8619_cast")];
            tensor<bool, []> attn_173_transpose_x_0 = const()[name = tensor<string, []>("attn_173_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_173_transpose_y_0 = const()[name = tensor<string, []>("attn_173_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_173_cast = matmul(transpose_x = attn_173_transpose_x_0, transpose_y = attn_173_transpose_y_0, x = var_8615_cast, y = var_8619_cast)[name = tensor<string, []>("attn_173_cast")];
            tensor<int32, [4]> var_8623 = const()[name = tensor<string, []>("op_8623"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_511_cast = reshape(shape = var_8623, x = attn_173_cast)[name = tensor<string, []>("input_511_cast")];
            tensor<int32, [2]> var_8628 = const()[name = tensor<string, []>("op_8628"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8630 = const()[name = tensor<string, []>("op_8630"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8632_pad_type_0 = const()[name = tensor<string, []>("op_8632_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8632_pad_0 = const()[name = tensor<string, []>("op_8632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(787440192))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(788669056))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(788669248)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8632_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_8630, groups = var_6865, pad = var_8632_pad_0, pad_type = var_8632_pad_type_0, strides = var_8628, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_511_cast)[name = tensor<string, []>("op_8632_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_261_cast = add(x = var_8632_cast, y = inputs_259_cast)[name = tensor<string, []>("inputs_261_cast")];
            tensor<int32, [1]> var_8636 = const()[name = tensor<string, []>("op_8636"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_261_cast = reduce_mean(axes = var_8636, keep_dims = var_6860, x = inputs_261_cast)[name = tensor<string, []>("channels_mean_261_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_261_cast = sub(x = inputs_261_cast, y = channels_mean_261_cast)[name = tensor<string, []>("zero_mean_261_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_261_cast = mul(x = zero_mean_261_cast, y = zero_mean_261_cast)[name = tensor<string, []>("zero_mean_sq_261_cast")];
            tensor<int32, [1]> var_8640 = const()[name = tensor<string, []>("op_8640"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8641_cast = reduce_mean(axes = var_8640, keep_dims = var_6860, x = zero_mean_sq_261_cast)[name = tensor<string, []>("op_8641_cast")];
            tensor<fp16, []> var_8642_to_fp16 = const()[name = tensor<string, []>("op_8642_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8643_cast = add(x = var_8641_cast, y = var_8642_to_fp16)[name = tensor<string, []>("op_8643_cast")];
            tensor<fp16, []> denom_261_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_261_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_261_cast = rsqrt(epsilon = denom_261_epsilon_0_to_fp16, x = var_8643_cast)[name = tensor<string, []>("denom_261_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_261_cast = mul(x = zero_mean_261_cast, y = denom_261_cast)[name = tensor<string, []>("out_261_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8647_to_fp16 = const()[name = tensor<string, []>("op_8647_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(788671872)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8648_cast = add(x = out_261_cast, y = var_8647_to_fp16)[name = tensor<string, []>("op_8648_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8650_to_fp16 = const()[name = tensor<string, []>("op_8650_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(788674496)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_343_cast = mul(x = var_8648_cast, y = var_8650_to_fp16)[name = tensor<string, []>("hidden_states_343_cast")];
            tensor<int32, [2]> var_8657 = const()[name = tensor<string, []>("op_8657"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8659 = const()[name = tensor<string, []>("op_8659"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_175_pad_type_0 = const()[name = tensor<string, []>("q_175_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_175_pad_0 = const()[name = tensor<string, []>("q_175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(788677120))), lut = tensor<fp16, [2]>([-0x1.944p-7, 0x1.958p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_175_cast = conv(dilations = var_8659, groups = var_6865, pad = q_175_pad_0, pad_type = q_175_pad_type_0, strides = var_8657, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_343_cast)[name = tensor<string, []>("q_175_cast")];
            tensor<int32, [2]> var_8663 = const()[name = tensor<string, []>("op_8663"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8665 = const()[name = tensor<string, []>("op_8665"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_175_pad_type_0 = const()[name = tensor<string, []>("k_175_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_175_pad_0 = const()[name = tensor<string, []>("k_175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(788881984))), lut = tensor<fp16, [2]>([-0x1.128p-7, 0x1.124p-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_175_cast = conv(dilations = var_8665, groups = var_6865, pad = k_175_pad_0, pad_type = k_175_pad_type_0, strides = var_8663, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_175_cast")];
            tensor<int32, [2]> var_8669 = const()[name = tensor<string, []>("op_8669"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8671 = const()[name = tensor<string, []>("op_8671"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_175_pad_type_0 = const()[name = tensor<string, []>("v_175_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_175_pad_0 = const()[name = tensor<string, []>("v_175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789209728))), lut = tensor<fp16, [2]>([-0x1.57p-7, 0x1.57cp-7]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_175_cast = conv(dilations = var_8671, groups = var_6865, pad = v_175_pad_0, pad_type = v_175_pad_type_0, strides = var_8669, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_175_cast")];
            tensor<int32, [4]> var_8675 = const()[name = tensor<string, []>("op_8675"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8676_cast = reshape(shape = var_8675, x = q_175_cast)[name = tensor<string, []>("op_8676_cast")];
            tensor<int32, [4]> var_8677 = const()[name = tensor<string, []>("op_8677"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8678_cast = reshape(shape = var_8677, x = k_175_cast)[name = tensor<string, []>("op_8678_cast")];
            tensor<int32, [4]> var_8679 = const()[name = tensor<string, []>("op_8679"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8680_cast = reshape(shape = var_8679, x = v_175_cast)[name = tensor<string, []>("op_8680_cast")];
            tensor<bool, []> attn_weights_349_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_349_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_349_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_349_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_349_cast = matmul(transpose_x = attn_weights_349_transpose_x_0, transpose_y = attn_weights_349_transpose_y_0, x = var_8676_cast, y = var_8678_cast)[name = tensor<string, []>("attn_weights_349_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_351_cast = mul(x = attn_weights_349_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_351_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_8684_cast = softmax(axis = var_6849, x = attn_weights_351_cast)[name = tensor<string, []>("op_8684_cast")];
            tensor<bool, []> attn_175_transpose_x_0 = const()[name = tensor<string, []>("attn_175_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_175_transpose_y_0 = const()[name = tensor<string, []>("attn_175_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_175_cast = matmul(transpose_x = attn_175_transpose_x_0, transpose_y = attn_175_transpose_y_0, x = var_8680_cast, y = var_8684_cast)[name = tensor<string, []>("attn_175_cast")];
            tensor<int32, [4]> var_8688 = const()[name = tensor<string, []>("op_8688"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_513_cast = reshape(shape = var_8688, x = attn_175_cast)[name = tensor<string, []>("input_513_cast")];
            tensor<int32, [2]> var_8693 = const()[name = tensor<string, []>("op_8693"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8695 = const()[name = tensor<string, []>("op_8695"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8697_pad_type_0 = const()[name = tensor<string, []>("op_8697_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8697_pad_0 = const()[name = tensor<string, []>("op_8697_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789537472))), lut = tensor<fp16, [2]>([-0x1.bep-8, 0x1.bdcp-8]), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789742336)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8697_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_8695, groups = var_6865, pad = var_8697_pad_0, pad_type = var_8697_pad_type_0, strides = var_8693, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_513_cast)[name = tensor<string, []>("op_8697_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_263_cast = add(x = var_8697_cast, y = inputs_261_cast)[name = tensor<string, []>("inputs_263_cast")];
            tensor<int32, [1]> var_8701 = const()[name = tensor<string, []>("op_8701"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_263_cast = reduce_mean(axes = var_8701, keep_dims = var_6860, x = inputs_263_cast)[name = tensor<string, []>("channels_mean_263_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_263_cast = sub(x = inputs_263_cast, y = channels_mean_263_cast)[name = tensor<string, []>("zero_mean_263_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_263_cast = mul(x = zero_mean_263_cast, y = zero_mean_263_cast)[name = tensor<string, []>("zero_mean_sq_263_cast")];
            tensor<int32, [1]> var_8705 = const()[name = tensor<string, []>("op_8705"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8706_cast = reduce_mean(axes = var_8705, keep_dims = var_6860, x = zero_mean_sq_263_cast)[name = tensor<string, []>("op_8706_cast")];
            tensor<fp16, []> var_8707_to_fp16 = const()[name = tensor<string, []>("op_8707_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8708_cast = add(x = var_8706_cast, y = var_8707_to_fp16)[name = tensor<string, []>("op_8708_cast")];
            tensor<fp16, []> denom_263_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_263_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_263_cast = rsqrt(epsilon = denom_263_epsilon_0_to_fp16, x = var_8708_cast)[name = tensor<string, []>("denom_263_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_263_cast = mul(x = zero_mean_263_cast, y = denom_263_cast)[name = tensor<string, []>("out_263_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8712_to_fp16 = const()[name = tensor<string, []>("op_8712_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789744960)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8713_cast = add(x = out_263_cast, y = var_8712_to_fp16)[name = tensor<string, []>("op_8713_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8715_to_fp16 = const()[name = tensor<string, []>("op_8715_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789747584)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_515_cast = mul(x = var_8713_cast, y = var_8715_to_fp16)[name = tensor<string, []>("input_515_cast")];
            tensor<int32, [2]> var_8723 = const()[name = tensor<string, []>("op_8723"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8725 = const()[name = tensor<string, []>("op_8725"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8727_pad_type_0 = const()[name = tensor<string, []>("op_8727_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8727_pad_0 = const()[name = tensor<string, []>("op_8727_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6553600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789750208))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(796303872))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(796304000)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_8727_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_8725, groups = var_6865, pad = var_8727_pad_0, pad_type = var_8727_pad_type_0, strides = var_8723, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_515_cast)[name = tensor<string, []>("op_8727_cast")];
            tensor<int32, [2]> var_8728_split_sizes_0 = const()[name = tensor<string, []>("op_8728_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_8728_axis_0 = const()[name = tensor<string, []>("op_8728_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_8728_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_8728_cast_1 = split(axis = var_8728_axis_0, split_sizes = var_8728_split_sizes_0, x = var_8727_cast)[name = tensor<string, []>("op_8728_cast")];
            tensor<string, []> var_8730_mode_0 = const()[name = tensor<string, []>("op_8730_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_8730_cast = gelu(mode = var_8730_mode_0, x = var_8728_cast_1)[name = tensor<string, []>("op_8730_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_517_cast = mul(x = var_8728_cast_0, y = var_8730_cast)[name = tensor<string, []>("input_517_cast")];
            tensor<int32, [2]> var_8734 = const()[name = tensor<string, []>("op_8734"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8736 = const()[name = tensor<string, []>("op_8736"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8738_pad_type_0 = const()[name = tensor<string, []>("op_8738_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8738_pad_0 = const()[name = tensor<string, []>("op_8738_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(796324544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801239808))), name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801240000)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8738_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_8736, groups = var_6865, pad = var_8738_pad_0, pad_type = var_8738_pad_type_0, strides = var_8734, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_517_cast)[name = tensor<string, []>("op_8738_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_347_cast = add(x = var_8738_cast, y = inputs_263_cast)[name = tensor<string, []>("hidden_states_347_cast")];
            tensor<int32, [4]> var_8740 = const()[name = tensor<string, []>("op_8740"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> input_519_cast = reshape(shape = var_8740, x = hidden_states_347_cast)[name = tensor<string, []>("input_519_cast")];
            tensor<int32, [2]> var_8744 = const()[name = tensor<string, []>("op_8744"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8746 = const()[name = tensor<string, []>("op_8746"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_349_pad_type_0 = const()[name = tensor<string, []>("hidden_states_349_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_349_pad_0 = const()[name = tensor<string, []>("hidden_states_349_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801242624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(802471488))), name = tensor<string, []>("up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(802471680)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_349_cast = conv(bias = up_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_8746, groups = var_6865, pad = hidden_states_349_pad_0, pad_type = hidden_states_349_pad_type_0, strides = var_8744, weight = up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized, x = input_519_cast)[name = tensor<string, []>("hidden_states_349_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_351_cast = add(x = hidden_states_349_cast, y = hidden_states_283_cast)[name = tensor<string, []>("hidden_states_351_cast")];
            tensor<bool, []> input_521_interleave_0 = const()[name = tensor<string, []>("input_521_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 32, 32]> input_521_cast = concat(axis = var_6865, interleave = input_521_interleave_0, values = (hidden_states_351_cast, input_213_cast))[name = tensor<string, []>("input_521_cast")];
            tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 32, 32])];
            tensor<fp16, [2, 32, 80, 32, 32]> reshape_96_cast = reshape(shape = reshape_96_shape_0, x = input_521_cast)[name = tensor<string, []>("reshape_96_cast")];
            tensor<int32, [3]> reduce_mean_72_axes_0 = const()[name = tensor<string, []>("reduce_mean_72_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_72_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_72_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_72_cast = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast)[name = tensor<string, []>("reduce_mean_72_cast")];
            tensor<fp16, [2, 32, 80, 32, 32]> sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor<string, []>("sub_48_cast")];
            tensor<fp16, [2, 32, 80, 32, 32]> square_24_cast = square(x = sub_48_cast)[name = tensor<string, []>("square_24_cast")];
            tensor<int32, [3]> reduce_mean_74_axes_0 = const()[name = tensor<string, []>("reduce_mean_74_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_74_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_74_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_74_cast = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast)[name = tensor<string, []>("reduce_mean_74_cast")];
            tensor<fp16, []> add_48_y_0_to_fp16 = const()[name = tensor<string, []>("add_48_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_48_cast = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16)[name = tensor<string, []>("add_48_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_24_cast = sqrt(x = add_48_cast)[name = tensor<string, []>("sqrt_24_cast")];
            tensor<fp16, [2, 32, 80, 32, 32]> real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast)[name = tensor<string, []>("real_div_24_cast")];
            tensor<int32, [4]> reshape_97_shape_0 = const()[name = tensor<string, []>("reshape_97_shape_0"), val = tensor<int32, [4]>([2, 2560, 32, 32])];
            tensor<fp16, [2, 2560, 32, 32]> reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor<string, []>("reshape_97_cast")];
            tensor<fp16, [2560]> add_49_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_49_gamma_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(802474304)))];
            tensor<fp16, [2560]> add_49_beta_0_to_fp16 = const()[name = tensor<string, []>("add_49_beta_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(802479488)))];
            tensor<fp16, []> add_49_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_49_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 2560, 32, 32]> add_49_cast = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_97_cast)[name = tensor<string, []>("add_49_cast")];
            tensor<fp16, [2, 2560, 32, 32]> input_525_cast = silu(x = add_49_cast)[name = tensor<string, []>("input_525_cast")];
            tensor<int32, [2]> var_8764 = const()[name = tensor<string, []>("op_8764"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8766 = const()[name = tensor<string, []>("op_8766"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_353_pad_type_0 = const()[name = tensor<string, []>("hidden_states_353_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_353_pad_0 = const()[name = tensor<string, []>("hidden_states_353_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 2560, 3, 3]> up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [22118400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(802484672))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(824603136))), name = tensor<string, []>("up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 3, 3])];
            tensor<fp16, [1280]> up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(824603328)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_353_cast = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_8766, groups = var_6865, pad = hidden_states_353_pad_0, pad_type = hidden_states_353_pad_type_0, strides = var_8764, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_525_cast)[name = tensor<string, []>("hidden_states_353_cast")];
            tensor<int32, [2]> var_8772 = const()[name = tensor<string, []>("op_8772"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8774 = const()[name = tensor<string, []>("op_8774"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_19_pad_type_0 = const()[name = tensor<string, []>("temb_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_19_pad_0 = const()[name = tensor<string, []>("temb_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(824605952))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(825834816))), name = tensor<string, []>("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(825835008)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_19_cast = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_8774, groups = var_6865, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_8772, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_19_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_529_cast = add(x = hidden_states_353_cast, y = temb_19_cast)[name = tensor<string, []>("input_529_cast")];
            tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_529_cast)[name = tensor<string, []>("reshape_100_cast")];
            tensor<int32, [3]> reduce_mean_75_axes_0 = const()[name = tensor<string, []>("reduce_mean_75_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_75_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_75_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_75_cast = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast)[name = tensor<string, []>("reduce_mean_75_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor<string, []>("sub_50_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_25_cast = square(x = sub_50_cast)[name = tensor<string, []>("square_25_cast")];
            tensor<int32, [3]> reduce_mean_77_axes_0 = const()[name = tensor<string, []>("reduce_mean_77_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_77_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_77_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_77_cast = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast)[name = tensor<string, []>("reduce_mean_77_cast")];
            tensor<fp16, []> add_50_y_0_to_fp16 = const()[name = tensor<string, []>("add_50_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_50_cast = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16)[name = tensor<string, []>("add_50_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_25_cast = sqrt(x = add_50_cast)[name = tensor<string, []>("sqrt_25_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast)[name = tensor<string, []>("real_div_25_cast")];
            tensor<int32, [4]> reshape_101_shape_0 = const()[name = tensor<string, []>("reshape_101_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast)[name = tensor<string, []>("reshape_101_cast")];
            tensor<fp16, [1280]> add_51_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_51_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(825837632)))];
            tensor<fp16, [1280]> add_51_beta_0_to_fp16 = const()[name = tensor<string, []>("add_51_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(825840256)))];
            tensor<fp16, []> add_51_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_51_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_51_cast = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_101_cast)[name = tensor<string, []>("add_51_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_533_cast = silu(x = add_51_cast)[name = tensor<string, []>("input_533_cast")];
            tensor<int32, [2]> var_8784 = const()[name = tensor<string, []>("op_8784"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8786 = const()[name = tensor<string, []>("op_8786"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_355_pad_type_0 = const()[name = tensor<string, []>("hidden_states_355_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_355_pad_0 = const()[name = tensor<string, []>("hidden_states_355_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(825842880))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(836902144))), name = tensor<string, []>("up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(836902336)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_355_cast = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_8786, groups = var_6865, pad = hidden_states_355_pad_0, pad_type = hidden_states_355_pad_type_0, strides = var_8784, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_533_cast)[name = tensor<string, []>("hidden_states_355_cast")];
            tensor<int32, [2]> var_8791 = const()[name = tensor<string, []>("op_8791"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8793 = const()[name = tensor<string, []>("op_8793"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_7_pad_type_0 = const()[name = tensor<string, []>("x_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_7_pad_0 = const()[name = tensor<string, []>("x_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2560, 1, 1]> up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(836904960))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839362624))), name = tensor<string, []>("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839362816)))];
            tensor<fp16, [2, 1280, 32, 32]> x_7_cast = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_8793, groups = var_6865, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_8791, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_521_cast)[name = tensor<string, []>("x_7_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_357_cast = add(x = x_7_cast, y = hidden_states_355_cast)[name = tensor<string, []>("hidden_states_357_cast")];
            tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_104_cast = reshape(shape = reshape_104_shape_0, x = hidden_states_357_cast)[name = tensor<string, []>("reshape_104_cast")];
            tensor<int32, [3]> reduce_mean_78_axes_0 = const()[name = tensor<string, []>("reduce_mean_78_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_78_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_78_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_78_cast = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast)[name = tensor<string, []>("reduce_mean_78_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor<string, []>("sub_52_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_26_cast = square(x = sub_52_cast)[name = tensor<string, []>("square_26_cast")];
            tensor<int32, [3]> reduce_mean_80_axes_0 = const()[name = tensor<string, []>("reduce_mean_80_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_80_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_80_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_80_cast = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast)[name = tensor<string, []>("reduce_mean_80_cast")];
            tensor<fp16, []> add_52_y_0_to_fp16 = const()[name = tensor<string, []>("add_52_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_52_cast = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16)[name = tensor<string, []>("add_52_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_26_cast = sqrt(x = add_52_cast)[name = tensor<string, []>("sqrt_26_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast)[name = tensor<string, []>("real_div_26_cast")];
            tensor<int32, [4]> reshape_105_shape_0 = const()[name = tensor<string, []>("reshape_105_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast)[name = tensor<string, []>("reshape_105_cast")];
            tensor<fp16, [1280]> add_53_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_53_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839365440)))];
            tensor<fp16, [1280]> add_53_beta_0_to_fp16 = const()[name = tensor<string, []>("add_53_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839368064)))];
            tensor<fp16, []> add_53_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_53_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_53_cast = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_105_cast)[name = tensor<string, []>("add_53_cast")];
            tensor<int32, [2]> var_8831 = const()[name = tensor<string, []>("op_8831"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8833 = const()[name = tensor<string, []>("op_8833"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_359_pad_type_0 = const()[name = tensor<string, []>("hidden_states_359_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_359_pad_0 = const()[name = tensor<string, []>("hidden_states_359_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839370688))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(840599552))), name = tensor<string, []>("up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(840599744)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_359_cast = conv(bias = up_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_8833, groups = var_6865, pad = hidden_states_359_pad_0, pad_type = hidden_states_359_pad_type_0, strides = var_8831, weight = up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized, x = add_53_cast)[name = tensor<string, []>("hidden_states_359_cast")];
            tensor<int32, [4]> var_8838 = const()[name = tensor<string, []>("op_8838"), val = tensor<int32, [4]>([2, 1280, 1, 1024])];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_265_cast = reshape(shape = var_8838, x = hidden_states_359_cast)[name = tensor<string, []>("inputs_265_cast")];
            tensor<int32, [1]> var_8848 = const()[name = tensor<string, []>("op_8848"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_265_cast = reduce_mean(axes = var_8848, keep_dims = var_6860, x = inputs_265_cast)[name = tensor<string, []>("channels_mean_265_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_265_cast = sub(x = inputs_265_cast, y = channels_mean_265_cast)[name = tensor<string, []>("zero_mean_265_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_265_cast = mul(x = zero_mean_265_cast, y = zero_mean_265_cast)[name = tensor<string, []>("zero_mean_sq_265_cast")];
            tensor<int32, [1]> var_8852 = const()[name = tensor<string, []>("op_8852"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8853_cast = reduce_mean(axes = var_8852, keep_dims = var_6860, x = zero_mean_sq_265_cast)[name = tensor<string, []>("op_8853_cast")];
            tensor<fp16, []> var_8854_to_fp16 = const()[name = tensor<string, []>("op_8854_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8855_cast = add(x = var_8853_cast, y = var_8854_to_fp16)[name = tensor<string, []>("op_8855_cast")];
            tensor<fp16, []> denom_265_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_265_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_265_cast = rsqrt(epsilon = denom_265_epsilon_0_to_fp16, x = var_8855_cast)[name = tensor<string, []>("denom_265_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_265_cast = mul(x = zero_mean_265_cast, y = denom_265_cast)[name = tensor<string, []>("out_265_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8859_to_fp16 = const()[name = tensor<string, []>("op_8859_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(840602368)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8860_cast = add(x = out_265_cast, y = var_8859_to_fp16)[name = tensor<string, []>("op_8860_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8862_to_fp16 = const()[name = tensor<string, []>("op_8862_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(840604992)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_361_cast = mul(x = var_8860_cast, y = var_8862_to_fp16)[name = tensor<string, []>("hidden_states_361_cast")];
            tensor<int32, [2]> var_8869 = const()[name = tensor<string, []>("op_8869"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8871 = const()[name = tensor<string, []>("op_8871"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_177_pad_type_0 = const()[name = tensor<string, []>("q_177_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_177_pad_0 = const()[name = tensor<string, []>("q_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(840607616))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(841426880))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_177_cast = conv(dilations = var_8871, groups = var_6865, pad = q_177_pad_0, pad_type = q_177_pad_type_0, strides = var_8869, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_361_cast)[name = tensor<string, []>("q_177_cast")];
            tensor<int32, [2]> var_8875 = const()[name = tensor<string, []>("op_8875"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8877 = const()[name = tensor<string, []>("op_8877"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_177_pad_type_0 = const()[name = tensor<string, []>("k_177_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_177_pad_0 = const()[name = tensor<string, []>("k_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(841427008))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842246272))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_177_cast = conv(dilations = var_8877, groups = var_6865, pad = k_177_pad_0, pad_type = k_177_pad_type_0, strides = var_8875, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_361_cast)[name = tensor<string, []>("k_177_cast")];
            tensor<int32, [2]> var_8881 = const()[name = tensor<string, []>("op_8881"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8883 = const()[name = tensor<string, []>("op_8883"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_177_pad_type_0 = const()[name = tensor<string, []>("v_177_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_177_pad_0 = const()[name = tensor<string, []>("v_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842246400))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(843475264))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_177_cast = conv(dilations = var_8883, groups = var_6865, pad = v_177_pad_0, pad_type = v_177_pad_type_0, strides = var_8881, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_361_cast)[name = tensor<string, []>("v_177_cast")];
            tensor<int32, [4]> var_8887 = const()[name = tensor<string, []>("op_8887"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8888_cast = reshape(shape = var_8887, x = q_177_cast)[name = tensor<string, []>("op_8888_cast")];
            tensor<int32, [4]> var_8889 = const()[name = tensor<string, []>("op_8889"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8890_cast = reshape(shape = var_8889, x = k_177_cast)[name = tensor<string, []>("op_8890_cast")];
            tensor<int32, [4]> var_8891 = const()[name = tensor<string, []>("op_8891"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8892_cast = reshape(shape = var_8891, x = v_177_cast)[name = tensor<string, []>("op_8892_cast")];
            tensor<bool, []> attn_weights_353_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_353_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_353_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_353_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_353_cast = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8888_cast, y = var_8890_cast)[name = tensor<string, []>("attn_weights_353_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_355_cast = mul(x = attn_weights_353_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_355_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_8896_cast = softmax(axis = var_6849, x = attn_weights_355_cast)[name = tensor<string, []>("op_8896_cast")];
            tensor<bool, []> attn_177_transpose_x_0 = const()[name = tensor<string, []>("attn_177_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_177_transpose_y_0 = const()[name = tensor<string, []>("attn_177_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_177_cast = matmul(transpose_x = attn_177_transpose_x_0, transpose_y = attn_177_transpose_y_0, x = var_8892_cast, y = var_8896_cast)[name = tensor<string, []>("attn_177_cast")];
            tensor<int32, [4]> var_8900 = const()[name = tensor<string, []>("op_8900"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_537_cast = reshape(shape = var_8900, x = attn_177_cast)[name = tensor<string, []>("input_537_cast")];
            tensor<int32, [2]> var_8905 = const()[name = tensor<string, []>("op_8905"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8907 = const()[name = tensor<string, []>("op_8907"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8909_pad_type_0 = const()[name = tensor<string, []>("op_8909_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8909_pad_0 = const()[name = tensor<string, []>("op_8909_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(843475456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(844704320))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(844704512)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8909_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8907, groups = var_6865, pad = var_8909_pad_0, pad_type = var_8909_pad_type_0, strides = var_8905, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_537_cast)[name = tensor<string, []>("op_8909_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_267_cast = add(x = var_8909_cast, y = inputs_265_cast)[name = tensor<string, []>("inputs_267_cast")];
            tensor<int32, [1]> var_8913 = const()[name = tensor<string, []>("op_8913"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_267_cast = reduce_mean(axes = var_8913, keep_dims = var_6860, x = inputs_267_cast)[name = tensor<string, []>("channels_mean_267_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_267_cast = sub(x = inputs_267_cast, y = channels_mean_267_cast)[name = tensor<string, []>("zero_mean_267_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_267_cast = mul(x = zero_mean_267_cast, y = zero_mean_267_cast)[name = tensor<string, []>("zero_mean_sq_267_cast")];
            tensor<int32, [1]> var_8917 = const()[name = tensor<string, []>("op_8917"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8918_cast = reduce_mean(axes = var_8917, keep_dims = var_6860, x = zero_mean_sq_267_cast)[name = tensor<string, []>("op_8918_cast")];
            tensor<fp16, []> var_8919_to_fp16 = const()[name = tensor<string, []>("op_8919_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8920_cast = add(x = var_8918_cast, y = var_8919_to_fp16)[name = tensor<string, []>("op_8920_cast")];
            tensor<fp16, []> denom_267_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_267_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_267_cast = rsqrt(epsilon = denom_267_epsilon_0_to_fp16, x = var_8920_cast)[name = tensor<string, []>("denom_267_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_267_cast = mul(x = zero_mean_267_cast, y = denom_267_cast)[name = tensor<string, []>("out_267_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8924_to_fp16 = const()[name = tensor<string, []>("op_8924_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(844707136)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8925_cast = add(x = out_267_cast, y = var_8924_to_fp16)[name = tensor<string, []>("op_8925_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8927_to_fp16 = const()[name = tensor<string, []>("op_8927_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(844709760)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_363_cast = mul(x = var_8925_cast, y = var_8927_to_fp16)[name = tensor<string, []>("hidden_states_363_cast")];
            tensor<int32, [2]> var_8934 = const()[name = tensor<string, []>("op_8934"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8936 = const()[name = tensor<string, []>("op_8936"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_179_pad_type_0 = const()[name = tensor<string, []>("q_179_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_179_pad_0 = const()[name = tensor<string, []>("q_179_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(844712384))), lut = tensor<fp16, [4]>([-0x1.d34p-6, -0x1.1dp-7, 0x1.1c8p-7, 0x1.d38p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_179_cast = conv(dilations = var_8936, groups = var_6865, pad = q_179_pad_0, pad_type = q_179_pad_type_0, strides = var_8934, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_363_cast)[name = tensor<string, []>("q_179_cast")];
            tensor<int32, [2]> var_8940 = const()[name = tensor<string, []>("op_8940"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8942 = const()[name = tensor<string, []>("op_8942"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_179_pad_type_0 = const()[name = tensor<string, []>("k_179_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_179_pad_0 = const()[name = tensor<string, []>("k_179_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(845122048))), lut = tensor<fp16, [4]>([-0x1.b84p-6, -0x1.05p-7, 0x1.07p-7, 0x1.b9cp-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_179_cast = conv(dilations = var_8942, groups = var_6865, pad = k_179_pad_0, pad_type = k_179_pad_type_0, strides = var_8940, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_179_cast")];
            tensor<int32, [2]> var_8946 = const()[name = tensor<string, []>("op_8946"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8948 = const()[name = tensor<string, []>("op_8948"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_179_pad_type_0 = const()[name = tensor<string, []>("v_179_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_179_pad_0 = const()[name = tensor<string, []>("v_179_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(845777472))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(847088256))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_179_cast = conv(dilations = var_8948, groups = var_6865, pad = v_179_pad_0, pad_type = v_179_pad_type_0, strides = var_8946, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_179_cast")];
            tensor<int32, [4]> var_8952 = const()[name = tensor<string, []>("op_8952"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_8953_cast = reshape(shape = var_8952, x = q_179_cast)[name = tensor<string, []>("op_8953_cast")];
            tensor<int32, [4]> var_8954 = const()[name = tensor<string, []>("op_8954"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8955_cast = reshape(shape = var_8954, x = k_179_cast)[name = tensor<string, []>("op_8955_cast")];
            tensor<int32, [4]> var_8956 = const()[name = tensor<string, []>("op_8956"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_8957_cast = reshape(shape = var_8956, x = v_179_cast)[name = tensor<string, []>("op_8957_cast")];
            tensor<bool, []> attn_weights_357_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_357_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_357_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_357_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_357_cast = matmul(transpose_x = attn_weights_357_transpose_x_0, transpose_y = attn_weights_357_transpose_y_0, x = var_8953_cast, y = var_8955_cast)[name = tensor<string, []>("attn_weights_357_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_359_cast = mul(x = attn_weights_357_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_359_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_8961_cast = softmax(axis = var_6849, x = attn_weights_359_cast)[name = tensor<string, []>("op_8961_cast")];
            tensor<bool, []> attn_179_transpose_x_0 = const()[name = tensor<string, []>("attn_179_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_179_transpose_y_0 = const()[name = tensor<string, []>("attn_179_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_179_cast = matmul(transpose_x = attn_179_transpose_x_0, transpose_y = attn_179_transpose_y_0, x = var_8957_cast, y = var_8961_cast)[name = tensor<string, []>("attn_179_cast")];
            tensor<int32, [4]> var_8965 = const()[name = tensor<string, []>("op_8965"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_539_cast = reshape(shape = var_8965, x = attn_179_cast)[name = tensor<string, []>("input_539_cast")];
            tensor<int32, [2]> var_8970 = const()[name = tensor<string, []>("op_8970"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_8972 = const()[name = tensor<string, []>("op_8972"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_8974_pad_type_0 = const()[name = tensor<string, []>("op_8974_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_8974_pad_0 = const()[name = tensor<string, []>("op_8974_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(847088384))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(847907648))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(847907776)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8974_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8972, groups = var_6865, pad = var_8974_pad_0, pad_type = var_8974_pad_type_0, strides = var_8970, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_539_cast)[name = tensor<string, []>("op_8974_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_269_cast = add(x = var_8974_cast, y = inputs_267_cast)[name = tensor<string, []>("inputs_269_cast")];
            tensor<int32, [1]> var_8978 = const()[name = tensor<string, []>("op_8978"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_269_cast = reduce_mean(axes = var_8978, keep_dims = var_6860, x = inputs_269_cast)[name = tensor<string, []>("channels_mean_269_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_269_cast = sub(x = inputs_269_cast, y = channels_mean_269_cast)[name = tensor<string, []>("zero_mean_269_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_269_cast = mul(x = zero_mean_269_cast, y = zero_mean_269_cast)[name = tensor<string, []>("zero_mean_sq_269_cast")];
            tensor<int32, [1]> var_8982 = const()[name = tensor<string, []>("op_8982"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_8983_cast = reduce_mean(axes = var_8982, keep_dims = var_6860, x = zero_mean_sq_269_cast)[name = tensor<string, []>("op_8983_cast")];
            tensor<fp16, []> var_8984_to_fp16 = const()[name = tensor<string, []>("op_8984_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_8985_cast = add(x = var_8983_cast, y = var_8984_to_fp16)[name = tensor<string, []>("op_8985_cast")];
            tensor<fp16, []> denom_269_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_269_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_269_cast = rsqrt(epsilon = denom_269_epsilon_0_to_fp16, x = var_8985_cast)[name = tensor<string, []>("denom_269_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_269_cast = mul(x = zero_mean_269_cast, y = denom_269_cast)[name = tensor<string, []>("out_269_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8989_to_fp16 = const()[name = tensor<string, []>("op_8989_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(847910400)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_8990_cast = add(x = out_269_cast, y = var_8989_to_fp16)[name = tensor<string, []>("op_8990_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_8992_to_fp16 = const()[name = tensor<string, []>("op_8992_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(847913024)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_541_cast = mul(x = var_8990_cast, y = var_8992_to_fp16)[name = tensor<string, []>("input_541_cast")];
            tensor<int32, [2]> var_9000 = const()[name = tensor<string, []>("op_9000"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9002 = const()[name = tensor<string, []>("op_9002"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9004_pad_type_0 = const()[name = tensor<string, []>("op_9004_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9004_pad_0 = const()[name = tensor<string, []>("op_9004_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(847915648))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(857746112))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(857746304)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_9004_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_9002, groups = var_6865, pad = var_9004_pad_0, pad_type = var_9004_pad_type_0, strides = var_9000, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_541_cast)[name = tensor<string, []>("op_9004_cast")];
            tensor<int32, [2]> var_9005_split_sizes_0 = const()[name = tensor<string, []>("op_9005_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_9005_axis_0 = const()[name = tensor<string, []>("op_9005_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_9005_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_9005_cast_1 = split(axis = var_9005_axis_0, split_sizes = var_9005_split_sizes_0, x = var_9004_cast)[name = tensor<string, []>("op_9005_cast")];
            tensor<string, []> var_9007_mode_0 = const()[name = tensor<string, []>("op_9007_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_9007_cast = gelu(mode = var_9007_mode_0, x = var_9005_cast_1)[name = tensor<string, []>("op_9007_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_543_cast = mul(x = var_9005_cast_0, y = var_9007_cast)[name = tensor<string, []>("input_543_cast")];
            tensor<int32, [2]> var_9011 = const()[name = tensor<string, []>("op_9011"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9013 = const()[name = tensor<string, []>("op_9013"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9015_pad_type_0 = const()[name = tensor<string, []>("op_9015_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9015_pad_0 = const()[name = tensor<string, []>("op_9015_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(857766848))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(862682112))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(862682304)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9015_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_9013, groups = var_6865, pad = var_9015_pad_0, pad_type = var_9015_pad_type_0, strides = var_9011, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_543_cast)[name = tensor<string, []>("op_9015_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_271_cast = add(x = var_9015_cast, y = inputs_269_cast)[name = tensor<string, []>("inputs_271_cast")];
            tensor<int32, [1]> var_9025 = const()[name = tensor<string, []>("op_9025"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_271_cast = reduce_mean(axes = var_9025, keep_dims = var_6860, x = inputs_271_cast)[name = tensor<string, []>("channels_mean_271_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_271_cast = sub(x = inputs_271_cast, y = channels_mean_271_cast)[name = tensor<string, []>("zero_mean_271_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_271_cast = mul(x = zero_mean_271_cast, y = zero_mean_271_cast)[name = tensor<string, []>("zero_mean_sq_271_cast")];
            tensor<int32, [1]> var_9029 = const()[name = tensor<string, []>("op_9029"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9030_cast = reduce_mean(axes = var_9029, keep_dims = var_6860, x = zero_mean_sq_271_cast)[name = tensor<string, []>("op_9030_cast")];
            tensor<fp16, []> var_9031_to_fp16 = const()[name = tensor<string, []>("op_9031_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9032_cast = add(x = var_9030_cast, y = var_9031_to_fp16)[name = tensor<string, []>("op_9032_cast")];
            tensor<fp16, []> denom_271_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_271_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_271_cast = rsqrt(epsilon = denom_271_epsilon_0_to_fp16, x = var_9032_cast)[name = tensor<string, []>("denom_271_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_271_cast = mul(x = zero_mean_271_cast, y = denom_271_cast)[name = tensor<string, []>("out_271_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9036_to_fp16 = const()[name = tensor<string, []>("op_9036_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(862684928)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9037_cast = add(x = out_271_cast, y = var_9036_to_fp16)[name = tensor<string, []>("op_9037_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9039_to_fp16 = const()[name = tensor<string, []>("op_9039_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(862687552)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_367_cast = mul(x = var_9037_cast, y = var_9039_to_fp16)[name = tensor<string, []>("hidden_states_367_cast")];
            tensor<int32, [2]> var_9046 = const()[name = tensor<string, []>("op_9046"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9048 = const()[name = tensor<string, []>("op_9048"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_181_pad_type_0 = const()[name = tensor<string, []>("q_181_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_181_pad_0 = const()[name = tensor<string, []>("q_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(862690176))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863509440))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_181_cast = conv(dilations = var_9048, groups = var_6865, pad = q_181_pad_0, pad_type = q_181_pad_type_0, strides = var_9046, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_367_cast)[name = tensor<string, []>("q_181_cast")];
            tensor<int32, [2]> var_9052 = const()[name = tensor<string, []>("op_9052"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9054 = const()[name = tensor<string, []>("op_9054"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_181_pad_type_0 = const()[name = tensor<string, []>("k_181_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_181_pad_0 = const()[name = tensor<string, []>("k_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863509568))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(864328832))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_181_cast = conv(dilations = var_9054, groups = var_6865, pad = k_181_pad_0, pad_type = k_181_pad_type_0, strides = var_9052, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_367_cast)[name = tensor<string, []>("k_181_cast")];
            tensor<int32, [2]> var_9058 = const()[name = tensor<string, []>("op_9058"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9060 = const()[name = tensor<string, []>("op_9060"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_181_pad_type_0 = const()[name = tensor<string, []>("v_181_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_181_pad_0 = const()[name = tensor<string, []>("v_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(864328960))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(865557824))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_181_cast = conv(dilations = var_9060, groups = var_6865, pad = v_181_pad_0, pad_type = v_181_pad_type_0, strides = var_9058, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_367_cast)[name = tensor<string, []>("v_181_cast")];
            tensor<int32, [4]> var_9064 = const()[name = tensor<string, []>("op_9064"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9065_cast = reshape(shape = var_9064, x = q_181_cast)[name = tensor<string, []>("op_9065_cast")];
            tensor<int32, [4]> var_9066 = const()[name = tensor<string, []>("op_9066"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9067_cast = reshape(shape = var_9066, x = k_181_cast)[name = tensor<string, []>("op_9067_cast")];
            tensor<int32, [4]> var_9068 = const()[name = tensor<string, []>("op_9068"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9069_cast = reshape(shape = var_9068, x = v_181_cast)[name = tensor<string, []>("op_9069_cast")];
            tensor<bool, []> attn_weights_361_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_361_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_361_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_361_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_361_cast = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_9065_cast, y = var_9067_cast)[name = tensor<string, []>("attn_weights_361_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_363_cast = mul(x = attn_weights_361_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_363_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_9073_cast = softmax(axis = var_6849, x = attn_weights_363_cast)[name = tensor<string, []>("op_9073_cast")];
            tensor<bool, []> attn_181_transpose_x_0 = const()[name = tensor<string, []>("attn_181_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_181_transpose_y_0 = const()[name = tensor<string, []>("attn_181_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_181_cast = matmul(transpose_x = attn_181_transpose_x_0, transpose_y = attn_181_transpose_y_0, x = var_9069_cast, y = var_9073_cast)[name = tensor<string, []>("attn_181_cast")];
            tensor<int32, [4]> var_9077 = const()[name = tensor<string, []>("op_9077"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_545_cast = reshape(shape = var_9077, x = attn_181_cast)[name = tensor<string, []>("input_545_cast")];
            tensor<int32, [2]> var_9082 = const()[name = tensor<string, []>("op_9082"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9084 = const()[name = tensor<string, []>("op_9084"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9086_pad_type_0 = const()[name = tensor<string, []>("op_9086_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9086_pad_0 = const()[name = tensor<string, []>("op_9086_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(865558016))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(866786880))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(866787072)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9086_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_9084, groups = var_6865, pad = var_9086_pad_0, pad_type = var_9086_pad_type_0, strides = var_9082, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_545_cast)[name = tensor<string, []>("op_9086_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_273_cast = add(x = var_9086_cast, y = inputs_271_cast)[name = tensor<string, []>("inputs_273_cast")];
            tensor<int32, [1]> var_9090 = const()[name = tensor<string, []>("op_9090"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_273_cast = reduce_mean(axes = var_9090, keep_dims = var_6860, x = inputs_273_cast)[name = tensor<string, []>("channels_mean_273_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_273_cast = sub(x = inputs_273_cast, y = channels_mean_273_cast)[name = tensor<string, []>("zero_mean_273_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_273_cast = mul(x = zero_mean_273_cast, y = zero_mean_273_cast)[name = tensor<string, []>("zero_mean_sq_273_cast")];
            tensor<int32, [1]> var_9094 = const()[name = tensor<string, []>("op_9094"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9095_cast = reduce_mean(axes = var_9094, keep_dims = var_6860, x = zero_mean_sq_273_cast)[name = tensor<string, []>("op_9095_cast")];
            tensor<fp16, []> var_9096_to_fp16 = const()[name = tensor<string, []>("op_9096_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9097_cast = add(x = var_9095_cast, y = var_9096_to_fp16)[name = tensor<string, []>("op_9097_cast")];
            tensor<fp16, []> denom_273_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_273_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_273_cast = rsqrt(epsilon = denom_273_epsilon_0_to_fp16, x = var_9097_cast)[name = tensor<string, []>("denom_273_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_273_cast = mul(x = zero_mean_273_cast, y = denom_273_cast)[name = tensor<string, []>("out_273_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9101_to_fp16 = const()[name = tensor<string, []>("op_9101_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(866789696)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9102_cast = add(x = out_273_cast, y = var_9101_to_fp16)[name = tensor<string, []>("op_9102_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9104_to_fp16 = const()[name = tensor<string, []>("op_9104_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(866792320)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_369_cast = mul(x = var_9102_cast, y = var_9104_to_fp16)[name = tensor<string, []>("hidden_states_369_cast")];
            tensor<int32, [2]> var_9111 = const()[name = tensor<string, []>("op_9111"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9113 = const()[name = tensor<string, []>("op_9113"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_183_pad_type_0 = const()[name = tensor<string, []>("q_183_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_183_pad_0 = const()[name = tensor<string, []>("q_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(866794944))), lut = tensor<fp16, [4]>([-0x1.2bp-5, -0x1.64cp-7, 0x1.60cp-7, 0x1.2ap-5]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_183_cast = conv(dilations = var_9113, groups = var_6865, pad = q_183_pad_0, pad_type = q_183_pad_type_0, strides = var_9111, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_369_cast)[name = tensor<string, []>("q_183_cast")];
            tensor<int32, [2]> var_9117 = const()[name = tensor<string, []>("op_9117"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9119 = const()[name = tensor<string, []>("op_9119"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_183_pad_type_0 = const()[name = tensor<string, []>("k_183_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_183_pad_0 = const()[name = tensor<string, []>("k_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(867204608))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(868515392))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_183_cast = conv(dilations = var_9119, groups = var_6865, pad = k_183_pad_0, pad_type = k_183_pad_type_0, strides = var_9117, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_183_cast")];
            tensor<int32, [2]> var_9123 = const()[name = tensor<string, []>("op_9123"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9125 = const()[name = tensor<string, []>("op_9125"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_183_pad_type_0 = const()[name = tensor<string, []>("v_183_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_183_pad_0 = const()[name = tensor<string, []>("v_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(868515520))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(869826304))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_183_cast = conv(dilations = var_9125, groups = var_6865, pad = v_183_pad_0, pad_type = v_183_pad_type_0, strides = var_9123, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_183_cast")];
            tensor<int32, [4]> var_9129 = const()[name = tensor<string, []>("op_9129"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9130_cast = reshape(shape = var_9129, x = q_183_cast)[name = tensor<string, []>("op_9130_cast")];
            tensor<int32, [4]> var_9131 = const()[name = tensor<string, []>("op_9131"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9132_cast = reshape(shape = var_9131, x = k_183_cast)[name = tensor<string, []>("op_9132_cast")];
            tensor<int32, [4]> var_9133 = const()[name = tensor<string, []>("op_9133"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9134_cast = reshape(shape = var_9133, x = v_183_cast)[name = tensor<string, []>("op_9134_cast")];
            tensor<bool, []> attn_weights_365_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_365_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_365_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_365_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_365_cast = matmul(transpose_x = attn_weights_365_transpose_x_0, transpose_y = attn_weights_365_transpose_y_0, x = var_9130_cast, y = var_9132_cast)[name = tensor<string, []>("attn_weights_365_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_367_cast = mul(x = attn_weights_365_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_367_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_9138_cast = softmax(axis = var_6849, x = attn_weights_367_cast)[name = tensor<string, []>("op_9138_cast")];
            tensor<bool, []> attn_183_transpose_x_0 = const()[name = tensor<string, []>("attn_183_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_183_transpose_y_0 = const()[name = tensor<string, []>("attn_183_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_183_cast = matmul(transpose_x = attn_183_transpose_x_0, transpose_y = attn_183_transpose_y_0, x = var_9134_cast, y = var_9138_cast)[name = tensor<string, []>("attn_183_cast")];
            tensor<int32, [4]> var_9142 = const()[name = tensor<string, []>("op_9142"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_547_cast = reshape(shape = var_9142, x = attn_183_cast)[name = tensor<string, []>("input_547_cast")];
            tensor<int32, [2]> var_9147 = const()[name = tensor<string, []>("op_9147"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9149 = const()[name = tensor<string, []>("op_9149"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9151_pad_type_0 = const()[name = tensor<string, []>("op_9151_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9151_pad_0 = const()[name = tensor<string, []>("op_9151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(869826432))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870645696))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870645824)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9151_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_9149, groups = var_6865, pad = var_9151_pad_0, pad_type = var_9151_pad_type_0, strides = var_9147, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_547_cast)[name = tensor<string, []>("op_9151_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_275_cast = add(x = var_9151_cast, y = inputs_273_cast)[name = tensor<string, []>("inputs_275_cast")];
            tensor<int32, [1]> var_9155 = const()[name = tensor<string, []>("op_9155"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_275_cast = reduce_mean(axes = var_9155, keep_dims = var_6860, x = inputs_275_cast)[name = tensor<string, []>("channels_mean_275_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_275_cast = sub(x = inputs_275_cast, y = channels_mean_275_cast)[name = tensor<string, []>("zero_mean_275_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_275_cast = mul(x = zero_mean_275_cast, y = zero_mean_275_cast)[name = tensor<string, []>("zero_mean_sq_275_cast")];
            tensor<int32, [1]> var_9159 = const()[name = tensor<string, []>("op_9159"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9160_cast = reduce_mean(axes = var_9159, keep_dims = var_6860, x = zero_mean_sq_275_cast)[name = tensor<string, []>("op_9160_cast")];
            tensor<fp16, []> var_9161_to_fp16 = const()[name = tensor<string, []>("op_9161_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9162_cast = add(x = var_9160_cast, y = var_9161_to_fp16)[name = tensor<string, []>("op_9162_cast")];
            tensor<fp16, []> denom_275_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_275_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_275_cast = rsqrt(epsilon = denom_275_epsilon_0_to_fp16, x = var_9162_cast)[name = tensor<string, []>("denom_275_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_275_cast = mul(x = zero_mean_275_cast, y = denom_275_cast)[name = tensor<string, []>("out_275_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9166_to_fp16 = const()[name = tensor<string, []>("op_9166_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870648448)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9167_cast = add(x = out_275_cast, y = var_9166_to_fp16)[name = tensor<string, []>("op_9167_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9169_to_fp16 = const()[name = tensor<string, []>("op_9169_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870651072)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_549_cast = mul(x = var_9167_cast, y = var_9169_to_fp16)[name = tensor<string, []>("input_549_cast")];
            tensor<int32, [2]> var_9177 = const()[name = tensor<string, []>("op_9177"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9179 = const()[name = tensor<string, []>("op_9179"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9181_pad_type_0 = const()[name = tensor<string, []>("op_9181_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9181_pad_0 = const()[name = tensor<string, []>("op_9181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870653696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(880484160))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(880484352)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_9181_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_9179, groups = var_6865, pad = var_9181_pad_0, pad_type = var_9181_pad_type_0, strides = var_9177, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_549_cast)[name = tensor<string, []>("op_9181_cast")];
            tensor<int32, [2]> var_9182_split_sizes_0 = const()[name = tensor<string, []>("op_9182_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_9182_axis_0 = const()[name = tensor<string, []>("op_9182_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_9182_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_9182_cast_1 = split(axis = var_9182_axis_0, split_sizes = var_9182_split_sizes_0, x = var_9181_cast)[name = tensor<string, []>("op_9182_cast")];
            tensor<string, []> var_9184_mode_0 = const()[name = tensor<string, []>("op_9184_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_9184_cast = gelu(mode = var_9184_mode_0, x = var_9182_cast_1)[name = tensor<string, []>("op_9184_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_551_cast = mul(x = var_9182_cast_0, y = var_9184_cast)[name = tensor<string, []>("input_551_cast")];
            tensor<int32, [2]> var_9188 = const()[name = tensor<string, []>("op_9188"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9190 = const()[name = tensor<string, []>("op_9190"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9192_pad_type_0 = const()[name = tensor<string, []>("op_9192_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9192_pad_0 = const()[name = tensor<string, []>("op_9192_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(880504896))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(885420160))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(885420352)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9192_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_9190, groups = var_6865, pad = var_9192_pad_0, pad_type = var_9192_pad_type_0, strides = var_9188, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_551_cast)[name = tensor<string, []>("op_9192_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_277_cast = add(x = var_9192_cast, y = inputs_275_cast)[name = tensor<string, []>("inputs_277_cast")];
            tensor<int32, [1]> var_9202 = const()[name = tensor<string, []>("op_9202"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_277_cast = reduce_mean(axes = var_9202, keep_dims = var_6860, x = inputs_277_cast)[name = tensor<string, []>("channels_mean_277_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_277_cast = sub(x = inputs_277_cast, y = channels_mean_277_cast)[name = tensor<string, []>("zero_mean_277_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_277_cast = mul(x = zero_mean_277_cast, y = zero_mean_277_cast)[name = tensor<string, []>("zero_mean_sq_277_cast")];
            tensor<int32, [1]> var_9206 = const()[name = tensor<string, []>("op_9206"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9207_cast = reduce_mean(axes = var_9206, keep_dims = var_6860, x = zero_mean_sq_277_cast)[name = tensor<string, []>("op_9207_cast")];
            tensor<fp16, []> var_9208_to_fp16 = const()[name = tensor<string, []>("op_9208_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9209_cast = add(x = var_9207_cast, y = var_9208_to_fp16)[name = tensor<string, []>("op_9209_cast")];
            tensor<fp16, []> denom_277_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_277_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_277_cast = rsqrt(epsilon = denom_277_epsilon_0_to_fp16, x = var_9209_cast)[name = tensor<string, []>("denom_277_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_277_cast = mul(x = zero_mean_277_cast, y = denom_277_cast)[name = tensor<string, []>("out_277_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9213_to_fp16 = const()[name = tensor<string, []>("op_9213_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(885422976)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9214_cast = add(x = out_277_cast, y = var_9213_to_fp16)[name = tensor<string, []>("op_9214_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9216_to_fp16 = const()[name = tensor<string, []>("op_9216_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(885425600)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_373_cast = mul(x = var_9214_cast, y = var_9216_to_fp16)[name = tensor<string, []>("hidden_states_373_cast")];
            tensor<int32, [2]> var_9223 = const()[name = tensor<string, []>("op_9223"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9225 = const()[name = tensor<string, []>("op_9225"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_185_pad_type_0 = const()[name = tensor<string, []>("q_185_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_185_pad_0 = const()[name = tensor<string, []>("q_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(885428224))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(886247488))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_185_cast = conv(dilations = var_9225, groups = var_6865, pad = q_185_pad_0, pad_type = q_185_pad_type_0, strides = var_9223, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_373_cast)[name = tensor<string, []>("q_185_cast")];
            tensor<int32, [2]> var_9229 = const()[name = tensor<string, []>("op_9229"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9231 = const()[name = tensor<string, []>("op_9231"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_185_pad_type_0 = const()[name = tensor<string, []>("k_185_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_185_pad_0 = const()[name = tensor<string, []>("k_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(886247616))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(887066880))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_185_cast = conv(dilations = var_9231, groups = var_6865, pad = k_185_pad_0, pad_type = k_185_pad_type_0, strides = var_9229, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_373_cast)[name = tensor<string, []>("k_185_cast")];
            tensor<int32, [2]> var_9235 = const()[name = tensor<string, []>("op_9235"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9237 = const()[name = tensor<string, []>("op_9237"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_185_pad_type_0 = const()[name = tensor<string, []>("v_185_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_185_pad_0 = const()[name = tensor<string, []>("v_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(887067008))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(888295872))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_185_cast = conv(dilations = var_9237, groups = var_6865, pad = v_185_pad_0, pad_type = v_185_pad_type_0, strides = var_9235, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_373_cast)[name = tensor<string, []>("v_185_cast")];
            tensor<int32, [4]> var_9241 = const()[name = tensor<string, []>("op_9241"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9242_cast = reshape(shape = var_9241, x = q_185_cast)[name = tensor<string, []>("op_9242_cast")];
            tensor<int32, [4]> var_9243 = const()[name = tensor<string, []>("op_9243"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9244_cast = reshape(shape = var_9243, x = k_185_cast)[name = tensor<string, []>("op_9244_cast")];
            tensor<int32, [4]> var_9245 = const()[name = tensor<string, []>("op_9245"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9246_cast = reshape(shape = var_9245, x = v_185_cast)[name = tensor<string, []>("op_9246_cast")];
            tensor<bool, []> attn_weights_369_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_369_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_369_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_369_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_369_cast = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_9242_cast, y = var_9244_cast)[name = tensor<string, []>("attn_weights_369_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_371_cast = mul(x = attn_weights_369_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_371_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_9250_cast = softmax(axis = var_6849, x = attn_weights_371_cast)[name = tensor<string, []>("op_9250_cast")];
            tensor<bool, []> attn_185_transpose_x_0 = const()[name = tensor<string, []>("attn_185_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_185_transpose_y_0 = const()[name = tensor<string, []>("attn_185_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_185_cast = matmul(transpose_x = attn_185_transpose_x_0, transpose_y = attn_185_transpose_y_0, x = var_9246_cast, y = var_9250_cast)[name = tensor<string, []>("attn_185_cast")];
            tensor<int32, [4]> var_9254 = const()[name = tensor<string, []>("op_9254"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_553_cast = reshape(shape = var_9254, x = attn_185_cast)[name = tensor<string, []>("input_553_cast")];
            tensor<int32, [2]> var_9259 = const()[name = tensor<string, []>("op_9259"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9261 = const()[name = tensor<string, []>("op_9261"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9263_pad_type_0 = const()[name = tensor<string, []>("op_9263_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9263_pad_0 = const()[name = tensor<string, []>("op_9263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(888296064))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889524928))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889525120)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9263_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_9261, groups = var_6865, pad = var_9263_pad_0, pad_type = var_9263_pad_type_0, strides = var_9259, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_553_cast)[name = tensor<string, []>("op_9263_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_279_cast = add(x = var_9263_cast, y = inputs_277_cast)[name = tensor<string, []>("inputs_279_cast")];
            tensor<int32, [1]> var_9267 = const()[name = tensor<string, []>("op_9267"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_279_cast = reduce_mean(axes = var_9267, keep_dims = var_6860, x = inputs_279_cast)[name = tensor<string, []>("channels_mean_279_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_279_cast = sub(x = inputs_279_cast, y = channels_mean_279_cast)[name = tensor<string, []>("zero_mean_279_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_279_cast = mul(x = zero_mean_279_cast, y = zero_mean_279_cast)[name = tensor<string, []>("zero_mean_sq_279_cast")];
            tensor<int32, [1]> var_9271 = const()[name = tensor<string, []>("op_9271"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9272_cast = reduce_mean(axes = var_9271, keep_dims = var_6860, x = zero_mean_sq_279_cast)[name = tensor<string, []>("op_9272_cast")];
            tensor<fp16, []> var_9273_to_fp16 = const()[name = tensor<string, []>("op_9273_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9274_cast = add(x = var_9272_cast, y = var_9273_to_fp16)[name = tensor<string, []>("op_9274_cast")];
            tensor<fp16, []> denom_279_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_279_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_279_cast = rsqrt(epsilon = denom_279_epsilon_0_to_fp16, x = var_9274_cast)[name = tensor<string, []>("denom_279_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_279_cast = mul(x = zero_mean_279_cast, y = denom_279_cast)[name = tensor<string, []>("out_279_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9278_to_fp16 = const()[name = tensor<string, []>("op_9278_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889527744)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9279_cast = add(x = out_279_cast, y = var_9278_to_fp16)[name = tensor<string, []>("op_9279_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9281_to_fp16 = const()[name = tensor<string, []>("op_9281_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889530368)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_375_cast = mul(x = var_9279_cast, y = var_9281_to_fp16)[name = tensor<string, []>("hidden_states_375_cast")];
            tensor<int32, [2]> var_9288 = const()[name = tensor<string, []>("op_9288"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9290 = const()[name = tensor<string, []>("op_9290"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_187_pad_type_0 = const()[name = tensor<string, []>("q_187_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_187_pad_0 = const()[name = tensor<string, []>("q_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889532992))), lut = tensor<fp16, [4]>([-0x1.348p-5, -0x1.6fcp-7, 0x1.71cp-7, 0x1.354p-5]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_187_cast = conv(dilations = var_9290, groups = var_6865, pad = q_187_pad_0, pad_type = q_187_pad_type_0, strides = var_9288, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_375_cast)[name = tensor<string, []>("q_187_cast")];
            tensor<int32, [2]> var_9294 = const()[name = tensor<string, []>("op_9294"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9296 = const()[name = tensor<string, []>("op_9296"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_187_pad_type_0 = const()[name = tensor<string, []>("k_187_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_187_pad_0 = const()[name = tensor<string, []>("k_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889942656))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(891253440))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_187_cast = conv(dilations = var_9296, groups = var_6865, pad = k_187_pad_0, pad_type = k_187_pad_type_0, strides = var_9294, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_187_cast")];
            tensor<int32, [2]> var_9300 = const()[name = tensor<string, []>("op_9300"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9302 = const()[name = tensor<string, []>("op_9302"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_187_pad_type_0 = const()[name = tensor<string, []>("v_187_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_187_pad_0 = const()[name = tensor<string, []>("v_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(891253568))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(892564352))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_187_cast = conv(dilations = var_9302, groups = var_6865, pad = v_187_pad_0, pad_type = v_187_pad_type_0, strides = var_9300, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_187_cast")];
            tensor<int32, [4]> var_9306 = const()[name = tensor<string, []>("op_9306"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9307_cast = reshape(shape = var_9306, x = q_187_cast)[name = tensor<string, []>("op_9307_cast")];
            tensor<int32, [4]> var_9308 = const()[name = tensor<string, []>("op_9308"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9309_cast = reshape(shape = var_9308, x = k_187_cast)[name = tensor<string, []>("op_9309_cast")];
            tensor<int32, [4]> var_9310 = const()[name = tensor<string, []>("op_9310"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9311_cast = reshape(shape = var_9310, x = v_187_cast)[name = tensor<string, []>("op_9311_cast")];
            tensor<bool, []> attn_weights_373_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_373_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_373_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_373_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_373_cast = matmul(transpose_x = attn_weights_373_transpose_x_0, transpose_y = attn_weights_373_transpose_y_0, x = var_9307_cast, y = var_9309_cast)[name = tensor<string, []>("attn_weights_373_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_375_cast = mul(x = attn_weights_373_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_375_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_9315_cast = softmax(axis = var_6849, x = attn_weights_375_cast)[name = tensor<string, []>("op_9315_cast")];
            tensor<bool, []> attn_187_transpose_x_0 = const()[name = tensor<string, []>("attn_187_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_187_transpose_y_0 = const()[name = tensor<string, []>("attn_187_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_187_cast = matmul(transpose_x = attn_187_transpose_x_0, transpose_y = attn_187_transpose_y_0, x = var_9311_cast, y = var_9315_cast)[name = tensor<string, []>("attn_187_cast")];
            tensor<int32, [4]> var_9319 = const()[name = tensor<string, []>("op_9319"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_555_cast = reshape(shape = var_9319, x = attn_187_cast)[name = tensor<string, []>("input_555_cast")];
            tensor<int32, [2]> var_9324 = const()[name = tensor<string, []>("op_9324"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9326 = const()[name = tensor<string, []>("op_9326"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9328_pad_type_0 = const()[name = tensor<string, []>("op_9328_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9328_pad_0 = const()[name = tensor<string, []>("op_9328_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(892564480))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(893383744))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(893383872)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9328_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_9326, groups = var_6865, pad = var_9328_pad_0, pad_type = var_9328_pad_type_0, strides = var_9324, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_555_cast)[name = tensor<string, []>("op_9328_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_281_cast = add(x = var_9328_cast, y = inputs_279_cast)[name = tensor<string, []>("inputs_281_cast")];
            tensor<int32, [1]> var_9332 = const()[name = tensor<string, []>("op_9332"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_281_cast = reduce_mean(axes = var_9332, keep_dims = var_6860, x = inputs_281_cast)[name = tensor<string, []>("channels_mean_281_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_281_cast = sub(x = inputs_281_cast, y = channels_mean_281_cast)[name = tensor<string, []>("zero_mean_281_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_281_cast = mul(x = zero_mean_281_cast, y = zero_mean_281_cast)[name = tensor<string, []>("zero_mean_sq_281_cast")];
            tensor<int32, [1]> var_9336 = const()[name = tensor<string, []>("op_9336"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9337_cast = reduce_mean(axes = var_9336, keep_dims = var_6860, x = zero_mean_sq_281_cast)[name = tensor<string, []>("op_9337_cast")];
            tensor<fp16, []> var_9338_to_fp16 = const()[name = tensor<string, []>("op_9338_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9339_cast = add(x = var_9337_cast, y = var_9338_to_fp16)[name = tensor<string, []>("op_9339_cast")];
            tensor<fp16, []> denom_281_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_281_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_281_cast = rsqrt(epsilon = denom_281_epsilon_0_to_fp16, x = var_9339_cast)[name = tensor<string, []>("denom_281_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_281_cast = mul(x = zero_mean_281_cast, y = denom_281_cast)[name = tensor<string, []>("out_281_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9343_to_fp16 = const()[name = tensor<string, []>("op_9343_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(893386496)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9344_cast = add(x = out_281_cast, y = var_9343_to_fp16)[name = tensor<string, []>("op_9344_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9346_to_fp16 = const()[name = tensor<string, []>("op_9346_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(893389120)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_557_cast = mul(x = var_9344_cast, y = var_9346_to_fp16)[name = tensor<string, []>("input_557_cast")];
            tensor<int32, [2]> var_9354 = const()[name = tensor<string, []>("op_9354"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9356 = const()[name = tensor<string, []>("op_9356"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9358_pad_type_0 = const()[name = tensor<string, []>("op_9358_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9358_pad_0 = const()[name = tensor<string, []>("op_9358_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(893391744))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(903222208))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(903222400)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_9358_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_9356, groups = var_6865, pad = var_9358_pad_0, pad_type = var_9358_pad_type_0, strides = var_9354, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_557_cast)[name = tensor<string, []>("op_9358_cast")];
            tensor<int32, [2]> var_9359_split_sizes_0 = const()[name = tensor<string, []>("op_9359_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_9359_axis_0 = const()[name = tensor<string, []>("op_9359_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_9359_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_9359_cast_1 = split(axis = var_9359_axis_0, split_sizes = var_9359_split_sizes_0, x = var_9358_cast)[name = tensor<string, []>("op_9359_cast")];
            tensor<string, []> var_9361_mode_0 = const()[name = tensor<string, []>("op_9361_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_9361_cast = gelu(mode = var_9361_mode_0, x = var_9359_cast_1)[name = tensor<string, []>("op_9361_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_559_cast = mul(x = var_9359_cast_0, y = var_9361_cast)[name = tensor<string, []>("input_559_cast")];
            tensor<int32, [2]> var_9365 = const()[name = tensor<string, []>("op_9365"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9367 = const()[name = tensor<string, []>("op_9367"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9369_pad_type_0 = const()[name = tensor<string, []>("op_9369_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9369_pad_0 = const()[name = tensor<string, []>("op_9369_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(903242944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908158208))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908158400)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9369_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_9367, groups = var_6865, pad = var_9369_pad_0, pad_type = var_9369_pad_type_0, strides = var_9365, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_559_cast)[name = tensor<string, []>("op_9369_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_283_cast = add(x = var_9369_cast, y = inputs_281_cast)[name = tensor<string, []>("inputs_283_cast")];
            tensor<int32, [1]> var_9379 = const()[name = tensor<string, []>("op_9379"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_283_cast = reduce_mean(axes = var_9379, keep_dims = var_6860, x = inputs_283_cast)[name = tensor<string, []>("channels_mean_283_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_283_cast = sub(x = inputs_283_cast, y = channels_mean_283_cast)[name = tensor<string, []>("zero_mean_283_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_283_cast = mul(x = zero_mean_283_cast, y = zero_mean_283_cast)[name = tensor<string, []>("zero_mean_sq_283_cast")];
            tensor<int32, [1]> var_9383 = const()[name = tensor<string, []>("op_9383"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9384_cast = reduce_mean(axes = var_9383, keep_dims = var_6860, x = zero_mean_sq_283_cast)[name = tensor<string, []>("op_9384_cast")];
            tensor<fp16, []> var_9385_to_fp16 = const()[name = tensor<string, []>("op_9385_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9386_cast = add(x = var_9384_cast, y = var_9385_to_fp16)[name = tensor<string, []>("op_9386_cast")];
            tensor<fp16, []> denom_283_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_283_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_283_cast = rsqrt(epsilon = denom_283_epsilon_0_to_fp16, x = var_9386_cast)[name = tensor<string, []>("denom_283_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_283_cast = mul(x = zero_mean_283_cast, y = denom_283_cast)[name = tensor<string, []>("out_283_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9390_to_fp16 = const()[name = tensor<string, []>("op_9390_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908161024)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9391_cast = add(x = out_283_cast, y = var_9390_to_fp16)[name = tensor<string, []>("op_9391_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9393_to_fp16 = const()[name = tensor<string, []>("op_9393_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908163648)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_379_cast = mul(x = var_9391_cast, y = var_9393_to_fp16)[name = tensor<string, []>("hidden_states_379_cast")];
            tensor<int32, [2]> var_9400 = const()[name = tensor<string, []>("op_9400"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9402 = const()[name = tensor<string, []>("op_9402"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_189_pad_type_0 = const()[name = tensor<string, []>("q_189_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_189_pad_0 = const()[name = tensor<string, []>("q_189_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908166272))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908985536))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_189_cast = conv(dilations = var_9402, groups = var_6865, pad = q_189_pad_0, pad_type = q_189_pad_type_0, strides = var_9400, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_379_cast)[name = tensor<string, []>("q_189_cast")];
            tensor<int32, [2]> var_9406 = const()[name = tensor<string, []>("op_9406"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9408 = const()[name = tensor<string, []>("op_9408"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_189_pad_type_0 = const()[name = tensor<string, []>("k_189_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_189_pad_0 = const()[name = tensor<string, []>("k_189_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908985664))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(909804928))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_189_cast = conv(dilations = var_9408, groups = var_6865, pad = k_189_pad_0, pad_type = k_189_pad_type_0, strides = var_9406, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_379_cast)[name = tensor<string, []>("k_189_cast")];
            tensor<int32, [2]> var_9412 = const()[name = tensor<string, []>("op_9412"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9414 = const()[name = tensor<string, []>("op_9414"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_189_pad_type_0 = const()[name = tensor<string, []>("v_189_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_189_pad_0 = const()[name = tensor<string, []>("v_189_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(909805056))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(911033920))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_189_cast = conv(dilations = var_9414, groups = var_6865, pad = v_189_pad_0, pad_type = v_189_pad_type_0, strides = var_9412, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_379_cast)[name = tensor<string, []>("v_189_cast")];
            tensor<int32, [4]> var_9418 = const()[name = tensor<string, []>("op_9418"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9419_cast = reshape(shape = var_9418, x = q_189_cast)[name = tensor<string, []>("op_9419_cast")];
            tensor<int32, [4]> var_9420 = const()[name = tensor<string, []>("op_9420"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9421_cast = reshape(shape = var_9420, x = k_189_cast)[name = tensor<string, []>("op_9421_cast")];
            tensor<int32, [4]> var_9422 = const()[name = tensor<string, []>("op_9422"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9423_cast = reshape(shape = var_9422, x = v_189_cast)[name = tensor<string, []>("op_9423_cast")];
            tensor<bool, []> attn_weights_377_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_377_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_377_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_377_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_377_cast = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_9419_cast, y = var_9421_cast)[name = tensor<string, []>("attn_weights_377_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_379_cast = mul(x = attn_weights_377_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_379_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_9427_cast = softmax(axis = var_6849, x = attn_weights_379_cast)[name = tensor<string, []>("op_9427_cast")];
            tensor<bool, []> attn_189_transpose_x_0 = const()[name = tensor<string, []>("attn_189_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_189_transpose_y_0 = const()[name = tensor<string, []>("attn_189_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_189_cast = matmul(transpose_x = attn_189_transpose_x_0, transpose_y = attn_189_transpose_y_0, x = var_9423_cast, y = var_9427_cast)[name = tensor<string, []>("attn_189_cast")];
            tensor<int32, [4]> var_9431 = const()[name = tensor<string, []>("op_9431"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_561_cast = reshape(shape = var_9431, x = attn_189_cast)[name = tensor<string, []>("input_561_cast")];
            tensor<int32, [2]> var_9436 = const()[name = tensor<string, []>("op_9436"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9438 = const()[name = tensor<string, []>("op_9438"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9440_pad_type_0 = const()[name = tensor<string, []>("op_9440_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9440_pad_0 = const()[name = tensor<string, []>("op_9440_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(911034112))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912262976))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912263168)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9440_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_9438, groups = var_6865, pad = var_9440_pad_0, pad_type = var_9440_pad_type_0, strides = var_9436, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_561_cast)[name = tensor<string, []>("op_9440_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_285_cast = add(x = var_9440_cast, y = inputs_283_cast)[name = tensor<string, []>("inputs_285_cast")];
            tensor<int32, [1]> var_9444 = const()[name = tensor<string, []>("op_9444"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_285_cast = reduce_mean(axes = var_9444, keep_dims = var_6860, x = inputs_285_cast)[name = tensor<string, []>("channels_mean_285_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_285_cast = sub(x = inputs_285_cast, y = channels_mean_285_cast)[name = tensor<string, []>("zero_mean_285_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_285_cast = mul(x = zero_mean_285_cast, y = zero_mean_285_cast)[name = tensor<string, []>("zero_mean_sq_285_cast")];
            tensor<int32, [1]> var_9448 = const()[name = tensor<string, []>("op_9448"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9449_cast = reduce_mean(axes = var_9448, keep_dims = var_6860, x = zero_mean_sq_285_cast)[name = tensor<string, []>("op_9449_cast")];
            tensor<fp16, []> var_9450_to_fp16 = const()[name = tensor<string, []>("op_9450_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9451_cast = add(x = var_9449_cast, y = var_9450_to_fp16)[name = tensor<string, []>("op_9451_cast")];
            tensor<fp16, []> denom_285_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_285_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_285_cast = rsqrt(epsilon = denom_285_epsilon_0_to_fp16, x = var_9451_cast)[name = tensor<string, []>("denom_285_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_285_cast = mul(x = zero_mean_285_cast, y = denom_285_cast)[name = tensor<string, []>("out_285_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9455_to_fp16 = const()[name = tensor<string, []>("op_9455_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912265792)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9456_cast = add(x = out_285_cast, y = var_9455_to_fp16)[name = tensor<string, []>("op_9456_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9458_to_fp16 = const()[name = tensor<string, []>("op_9458_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912268416)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_381_cast = mul(x = var_9456_cast, y = var_9458_to_fp16)[name = tensor<string, []>("hidden_states_381_cast")];
            tensor<int32, [2]> var_9465 = const()[name = tensor<string, []>("op_9465"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9467 = const()[name = tensor<string, []>("op_9467"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_191_pad_type_0 = const()[name = tensor<string, []>("q_191_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_191_pad_0 = const()[name = tensor<string, []>("q_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912271040))), lut = tensor<fp16, [4]>([-0x1.42cp-5, -0x1.84p-7, 0x1.774p-7, 0x1.3fp-5]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_191_cast = conv(dilations = var_9467, groups = var_6865, pad = q_191_pad_0, pad_type = q_191_pad_type_0, strides = var_9465, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_381_cast)[name = tensor<string, []>("q_191_cast")];
            tensor<int32, [2]> var_9471 = const()[name = tensor<string, []>("op_9471"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9473 = const()[name = tensor<string, []>("op_9473"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_191_pad_type_0 = const()[name = tensor<string, []>("k_191_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_191_pad_0 = const()[name = tensor<string, []>("k_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912680704))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(913991488))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_191_cast = conv(dilations = var_9473, groups = var_6865, pad = k_191_pad_0, pad_type = k_191_pad_type_0, strides = var_9471, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_191_cast")];
            tensor<int32, [2]> var_9477 = const()[name = tensor<string, []>("op_9477"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9479 = const()[name = tensor<string, []>("op_9479"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_191_pad_type_0 = const()[name = tensor<string, []>("v_191_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_191_pad_0 = const()[name = tensor<string, []>("v_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(913991616))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(915302400))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_191_cast = conv(dilations = var_9479, groups = var_6865, pad = v_191_pad_0, pad_type = v_191_pad_type_0, strides = var_9477, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_191_cast")];
            tensor<int32, [4]> var_9483 = const()[name = tensor<string, []>("op_9483"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9484_cast = reshape(shape = var_9483, x = q_191_cast)[name = tensor<string, []>("op_9484_cast")];
            tensor<int32, [4]> var_9485 = const()[name = tensor<string, []>("op_9485"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9486_cast = reshape(shape = var_9485, x = k_191_cast)[name = tensor<string, []>("op_9486_cast")];
            tensor<int32, [4]> var_9487 = const()[name = tensor<string, []>("op_9487"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9488_cast = reshape(shape = var_9487, x = v_191_cast)[name = tensor<string, []>("op_9488_cast")];
            tensor<bool, []> attn_weights_381_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_381_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_381_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_381_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_381_cast = matmul(transpose_x = attn_weights_381_transpose_x_0, transpose_y = attn_weights_381_transpose_y_0, x = var_9484_cast, y = var_9486_cast)[name = tensor<string, []>("attn_weights_381_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_383_cast = mul(x = attn_weights_381_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_383_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_9492_cast = softmax(axis = var_6849, x = attn_weights_383_cast)[name = tensor<string, []>("op_9492_cast")];
            tensor<bool, []> attn_191_transpose_x_0 = const()[name = tensor<string, []>("attn_191_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_191_transpose_y_0 = const()[name = tensor<string, []>("attn_191_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_191_cast = matmul(transpose_x = attn_191_transpose_x_0, transpose_y = attn_191_transpose_y_0, x = var_9488_cast, y = var_9492_cast)[name = tensor<string, []>("attn_191_cast")];
            tensor<int32, [4]> var_9496 = const()[name = tensor<string, []>("op_9496"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_563_cast = reshape(shape = var_9496, x = attn_191_cast)[name = tensor<string, []>("input_563_cast")];
            tensor<int32, [2]> var_9501 = const()[name = tensor<string, []>("op_9501"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9503 = const()[name = tensor<string, []>("op_9503"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9505_pad_type_0 = const()[name = tensor<string, []>("op_9505_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9505_pad_0 = const()[name = tensor<string, []>("op_9505_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(915302528))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(916121792))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(916121920)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9505_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_9503, groups = var_6865, pad = var_9505_pad_0, pad_type = var_9505_pad_type_0, strides = var_9501, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_563_cast)[name = tensor<string, []>("op_9505_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_287_cast = add(x = var_9505_cast, y = inputs_285_cast)[name = tensor<string, []>("inputs_287_cast")];
            tensor<int32, [1]> var_9509 = const()[name = tensor<string, []>("op_9509"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_287_cast = reduce_mean(axes = var_9509, keep_dims = var_6860, x = inputs_287_cast)[name = tensor<string, []>("channels_mean_287_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_287_cast = sub(x = inputs_287_cast, y = channels_mean_287_cast)[name = tensor<string, []>("zero_mean_287_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_287_cast = mul(x = zero_mean_287_cast, y = zero_mean_287_cast)[name = tensor<string, []>("zero_mean_sq_287_cast")];
            tensor<int32, [1]> var_9513 = const()[name = tensor<string, []>("op_9513"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9514_cast = reduce_mean(axes = var_9513, keep_dims = var_6860, x = zero_mean_sq_287_cast)[name = tensor<string, []>("op_9514_cast")];
            tensor<fp16, []> var_9515_to_fp16 = const()[name = tensor<string, []>("op_9515_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9516_cast = add(x = var_9514_cast, y = var_9515_to_fp16)[name = tensor<string, []>("op_9516_cast")];
            tensor<fp16, []> denom_287_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_287_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_287_cast = rsqrt(epsilon = denom_287_epsilon_0_to_fp16, x = var_9516_cast)[name = tensor<string, []>("denom_287_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_287_cast = mul(x = zero_mean_287_cast, y = denom_287_cast)[name = tensor<string, []>("out_287_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9520_to_fp16 = const()[name = tensor<string, []>("op_9520_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(916124544)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9521_cast = add(x = out_287_cast, y = var_9520_to_fp16)[name = tensor<string, []>("op_9521_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9523_to_fp16 = const()[name = tensor<string, []>("op_9523_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(916127168)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_565_cast = mul(x = var_9521_cast, y = var_9523_to_fp16)[name = tensor<string, []>("input_565_cast")];
            tensor<int32, [2]> var_9531 = const()[name = tensor<string, []>("op_9531"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9533 = const()[name = tensor<string, []>("op_9533"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9535_pad_type_0 = const()[name = tensor<string, []>("op_9535_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9535_pad_0 = const()[name = tensor<string, []>("op_9535_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(916129792))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(925960256))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(925960448)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_9535_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_9533, groups = var_6865, pad = var_9535_pad_0, pad_type = var_9535_pad_type_0, strides = var_9531, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_565_cast)[name = tensor<string, []>("op_9535_cast")];
            tensor<int32, [2]> var_9536_split_sizes_0 = const()[name = tensor<string, []>("op_9536_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_9536_axis_0 = const()[name = tensor<string, []>("op_9536_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_9536_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_9536_cast_1 = split(axis = var_9536_axis_0, split_sizes = var_9536_split_sizes_0, x = var_9535_cast)[name = tensor<string, []>("op_9536_cast")];
            tensor<string, []> var_9538_mode_0 = const()[name = tensor<string, []>("op_9538_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_9538_cast = gelu(mode = var_9538_mode_0, x = var_9536_cast_1)[name = tensor<string, []>("op_9538_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_567_cast = mul(x = var_9536_cast_0, y = var_9538_cast)[name = tensor<string, []>("input_567_cast")];
            tensor<int32, [2]> var_9542 = const()[name = tensor<string, []>("op_9542"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9544 = const()[name = tensor<string, []>("op_9544"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9546_pad_type_0 = const()[name = tensor<string, []>("op_9546_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9546_pad_0 = const()[name = tensor<string, []>("op_9546_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(925980992))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(930896256))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(930896448)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9546_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_9544, groups = var_6865, pad = var_9546_pad_0, pad_type = var_9546_pad_type_0, strides = var_9542, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_567_cast)[name = tensor<string, []>("op_9546_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_289_cast = add(x = var_9546_cast, y = inputs_287_cast)[name = tensor<string, []>("inputs_289_cast")];
            tensor<int32, [1]> var_9556 = const()[name = tensor<string, []>("op_9556"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_289_cast = reduce_mean(axes = var_9556, keep_dims = var_6860, x = inputs_289_cast)[name = tensor<string, []>("channels_mean_289_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_289_cast = sub(x = inputs_289_cast, y = channels_mean_289_cast)[name = tensor<string, []>("zero_mean_289_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_289_cast = mul(x = zero_mean_289_cast, y = zero_mean_289_cast)[name = tensor<string, []>("zero_mean_sq_289_cast")];
            tensor<int32, [1]> var_9560 = const()[name = tensor<string, []>("op_9560"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9561_cast = reduce_mean(axes = var_9560, keep_dims = var_6860, x = zero_mean_sq_289_cast)[name = tensor<string, []>("op_9561_cast")];
            tensor<fp16, []> var_9562_to_fp16 = const()[name = tensor<string, []>("op_9562_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9563_cast = add(x = var_9561_cast, y = var_9562_to_fp16)[name = tensor<string, []>("op_9563_cast")];
            tensor<fp16, []> denom_289_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_289_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_289_cast = rsqrt(epsilon = denom_289_epsilon_0_to_fp16, x = var_9563_cast)[name = tensor<string, []>("denom_289_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_289_cast = mul(x = zero_mean_289_cast, y = denom_289_cast)[name = tensor<string, []>("out_289_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9567_to_fp16 = const()[name = tensor<string, []>("op_9567_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(930899072)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9568_cast = add(x = out_289_cast, y = var_9567_to_fp16)[name = tensor<string, []>("op_9568_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9570_to_fp16 = const()[name = tensor<string, []>("op_9570_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(930901696)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_385_cast = mul(x = var_9568_cast, y = var_9570_to_fp16)[name = tensor<string, []>("hidden_states_385_cast")];
            tensor<int32, [2]> var_9577 = const()[name = tensor<string, []>("op_9577"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9579 = const()[name = tensor<string, []>("op_9579"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_193_pad_type_0 = const()[name = tensor<string, []>("q_193_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_193_pad_0 = const()[name = tensor<string, []>("q_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(930904320))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(931723584))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_193_cast = conv(dilations = var_9579, groups = var_6865, pad = q_193_pad_0, pad_type = q_193_pad_type_0, strides = var_9577, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_385_cast)[name = tensor<string, []>("q_193_cast")];
            tensor<int32, [2]> var_9583 = const()[name = tensor<string, []>("op_9583"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9585 = const()[name = tensor<string, []>("op_9585"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_193_pad_type_0 = const()[name = tensor<string, []>("k_193_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_193_pad_0 = const()[name = tensor<string, []>("k_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(931723712))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(932542976))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_193_cast = conv(dilations = var_9585, groups = var_6865, pad = k_193_pad_0, pad_type = k_193_pad_type_0, strides = var_9583, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_385_cast)[name = tensor<string, []>("k_193_cast")];
            tensor<int32, [2]> var_9589 = const()[name = tensor<string, []>("op_9589"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9591 = const()[name = tensor<string, []>("op_9591"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_193_pad_type_0 = const()[name = tensor<string, []>("v_193_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_193_pad_0 = const()[name = tensor<string, []>("v_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(932543104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(933771968))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_193_cast = conv(dilations = var_9591, groups = var_6865, pad = v_193_pad_0, pad_type = v_193_pad_type_0, strides = var_9589, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_385_cast)[name = tensor<string, []>("v_193_cast")];
            tensor<int32, [4]> var_9595 = const()[name = tensor<string, []>("op_9595"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9596_cast = reshape(shape = var_9595, x = q_193_cast)[name = tensor<string, []>("op_9596_cast")];
            tensor<int32, [4]> var_9597 = const()[name = tensor<string, []>("op_9597"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9598_cast = reshape(shape = var_9597, x = k_193_cast)[name = tensor<string, []>("op_9598_cast")];
            tensor<int32, [4]> var_9599 = const()[name = tensor<string, []>("op_9599"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9600_cast = reshape(shape = var_9599, x = v_193_cast)[name = tensor<string, []>("op_9600_cast")];
            tensor<bool, []> attn_weights_385_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_385_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_385_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_385_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_385_cast = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_9596_cast, y = var_9598_cast)[name = tensor<string, []>("attn_weights_385_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_387_cast = mul(x = attn_weights_385_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_387_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_9604_cast = softmax(axis = var_6849, x = attn_weights_387_cast)[name = tensor<string, []>("op_9604_cast")];
            tensor<bool, []> attn_193_transpose_x_0 = const()[name = tensor<string, []>("attn_193_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_193_transpose_y_0 = const()[name = tensor<string, []>("attn_193_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_193_cast = matmul(transpose_x = attn_193_transpose_x_0, transpose_y = attn_193_transpose_y_0, x = var_9600_cast, y = var_9604_cast)[name = tensor<string, []>("attn_193_cast")];
            tensor<int32, [4]> var_9608 = const()[name = tensor<string, []>("op_9608"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_569_cast = reshape(shape = var_9608, x = attn_193_cast)[name = tensor<string, []>("input_569_cast")];
            tensor<int32, [2]> var_9613 = const()[name = tensor<string, []>("op_9613"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9615 = const()[name = tensor<string, []>("op_9615"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9617_pad_type_0 = const()[name = tensor<string, []>("op_9617_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9617_pad_0 = const()[name = tensor<string, []>("op_9617_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(933772160))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(935001024))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(935001216)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9617_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_9615, groups = var_6865, pad = var_9617_pad_0, pad_type = var_9617_pad_type_0, strides = var_9613, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_569_cast)[name = tensor<string, []>("op_9617_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_291_cast = add(x = var_9617_cast, y = inputs_289_cast)[name = tensor<string, []>("inputs_291_cast")];
            tensor<int32, [1]> var_9621 = const()[name = tensor<string, []>("op_9621"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_291_cast = reduce_mean(axes = var_9621, keep_dims = var_6860, x = inputs_291_cast)[name = tensor<string, []>("channels_mean_291_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_291_cast = sub(x = inputs_291_cast, y = channels_mean_291_cast)[name = tensor<string, []>("zero_mean_291_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_291_cast = mul(x = zero_mean_291_cast, y = zero_mean_291_cast)[name = tensor<string, []>("zero_mean_sq_291_cast")];
            tensor<int32, [1]> var_9625 = const()[name = tensor<string, []>("op_9625"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9626_cast = reduce_mean(axes = var_9625, keep_dims = var_6860, x = zero_mean_sq_291_cast)[name = tensor<string, []>("op_9626_cast")];
            tensor<fp16, []> var_9627_to_fp16 = const()[name = tensor<string, []>("op_9627_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9628_cast = add(x = var_9626_cast, y = var_9627_to_fp16)[name = tensor<string, []>("op_9628_cast")];
            tensor<fp16, []> denom_291_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_291_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_291_cast = rsqrt(epsilon = denom_291_epsilon_0_to_fp16, x = var_9628_cast)[name = tensor<string, []>("denom_291_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_291_cast = mul(x = zero_mean_291_cast, y = denom_291_cast)[name = tensor<string, []>("out_291_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9632_to_fp16 = const()[name = tensor<string, []>("op_9632_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(935003840)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9633_cast = add(x = out_291_cast, y = var_9632_to_fp16)[name = tensor<string, []>("op_9633_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9635_to_fp16 = const()[name = tensor<string, []>("op_9635_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(935006464)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_387_cast = mul(x = var_9633_cast, y = var_9635_to_fp16)[name = tensor<string, []>("hidden_states_387_cast")];
            tensor<int32, [2]> var_9642 = const()[name = tensor<string, []>("op_9642"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9644 = const()[name = tensor<string, []>("op_9644"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_195_pad_type_0 = const()[name = tensor<string, []>("q_195_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_195_pad_0 = const()[name = tensor<string, []>("q_195_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(935009088))), lut = tensor<fp16, [4]>([-0x1.264p-5, -0x1.604p-7, 0x1.658p-7, 0x1.278p-5]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_195_cast = conv(dilations = var_9644, groups = var_6865, pad = q_195_pad_0, pad_type = q_195_pad_type_0, strides = var_9642, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_387_cast)[name = tensor<string, []>("q_195_cast")];
            tensor<int32, [2]> var_9648 = const()[name = tensor<string, []>("op_9648"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9650 = const()[name = tensor<string, []>("op_9650"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_195_pad_type_0 = const()[name = tensor<string, []>("k_195_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_195_pad_0 = const()[name = tensor<string, []>("k_195_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(935418752))), lut = tensor<fp16, [4]>([-0x1.fa4p-6, -0x1.2dcp-7, 0x1.274p-7, 0x1.f7p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_195_cast = conv(dilations = var_9650, groups = var_6865, pad = k_195_pad_0, pad_type = k_195_pad_type_0, strides = var_9648, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_195_cast")];
            tensor<int32, [2]> var_9654 = const()[name = tensor<string, []>("op_9654"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9656 = const()[name = tensor<string, []>("op_9656"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_195_pad_type_0 = const()[name = tensor<string, []>("v_195_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_195_pad_0 = const()[name = tensor<string, []>("v_195_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(936074176))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937384960))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_195_cast = conv(dilations = var_9656, groups = var_6865, pad = v_195_pad_0, pad_type = v_195_pad_type_0, strides = var_9654, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_195_cast")];
            tensor<int32, [4]> var_9660 = const()[name = tensor<string, []>("op_9660"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9661_cast = reshape(shape = var_9660, x = q_195_cast)[name = tensor<string, []>("op_9661_cast")];
            tensor<int32, [4]> var_9662 = const()[name = tensor<string, []>("op_9662"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9663_cast = reshape(shape = var_9662, x = k_195_cast)[name = tensor<string, []>("op_9663_cast")];
            tensor<int32, [4]> var_9664 = const()[name = tensor<string, []>("op_9664"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9665_cast = reshape(shape = var_9664, x = v_195_cast)[name = tensor<string, []>("op_9665_cast")];
            tensor<bool, []> attn_weights_389_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_389_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_389_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_389_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_389_cast = matmul(transpose_x = attn_weights_389_transpose_x_0, transpose_y = attn_weights_389_transpose_y_0, x = var_9661_cast, y = var_9663_cast)[name = tensor<string, []>("attn_weights_389_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_391_cast = mul(x = attn_weights_389_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_391_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_9669_cast = softmax(axis = var_6849, x = attn_weights_391_cast)[name = tensor<string, []>("op_9669_cast")];
            tensor<bool, []> attn_195_transpose_x_0 = const()[name = tensor<string, []>("attn_195_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_195_transpose_y_0 = const()[name = tensor<string, []>("attn_195_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_195_cast = matmul(transpose_x = attn_195_transpose_x_0, transpose_y = attn_195_transpose_y_0, x = var_9665_cast, y = var_9669_cast)[name = tensor<string, []>("attn_195_cast")];
            tensor<int32, [4]> var_9673 = const()[name = tensor<string, []>("op_9673"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_571_cast = reshape(shape = var_9673, x = attn_195_cast)[name = tensor<string, []>("input_571_cast")];
            tensor<int32, [2]> var_9678 = const()[name = tensor<string, []>("op_9678"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9680 = const()[name = tensor<string, []>("op_9680"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9682_pad_type_0 = const()[name = tensor<string, []>("op_9682_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9682_pad_0 = const()[name = tensor<string, []>("op_9682_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937385088))), lut = tensor<fp16, [4]>([-0x1.3a8p-6, -0x1.79p-8, 0x1.798p-8, 0x1.3bp-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937794752)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9682_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_9680, groups = var_6865, pad = var_9682_pad_0, pad_type = var_9682_pad_type_0, strides = var_9678, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_571_cast)[name = tensor<string, []>("op_9682_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_293_cast = add(x = var_9682_cast, y = inputs_291_cast)[name = tensor<string, []>("inputs_293_cast")];
            tensor<int32, [1]> var_9686 = const()[name = tensor<string, []>("op_9686"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_293_cast = reduce_mean(axes = var_9686, keep_dims = var_6860, x = inputs_293_cast)[name = tensor<string, []>("channels_mean_293_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_293_cast = sub(x = inputs_293_cast, y = channels_mean_293_cast)[name = tensor<string, []>("zero_mean_293_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_293_cast = mul(x = zero_mean_293_cast, y = zero_mean_293_cast)[name = tensor<string, []>("zero_mean_sq_293_cast")];
            tensor<int32, [1]> var_9690 = const()[name = tensor<string, []>("op_9690"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9691_cast = reduce_mean(axes = var_9690, keep_dims = var_6860, x = zero_mean_sq_293_cast)[name = tensor<string, []>("op_9691_cast")];
            tensor<fp16, []> var_9692_to_fp16 = const()[name = tensor<string, []>("op_9692_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9693_cast = add(x = var_9691_cast, y = var_9692_to_fp16)[name = tensor<string, []>("op_9693_cast")];
            tensor<fp16, []> denom_293_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_293_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_293_cast = rsqrt(epsilon = denom_293_epsilon_0_to_fp16, x = var_9693_cast)[name = tensor<string, []>("denom_293_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_293_cast = mul(x = zero_mean_293_cast, y = denom_293_cast)[name = tensor<string, []>("out_293_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9697_to_fp16 = const()[name = tensor<string, []>("op_9697_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937797376)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9698_cast = add(x = out_293_cast, y = var_9697_to_fp16)[name = tensor<string, []>("op_9698_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9700_to_fp16 = const()[name = tensor<string, []>("op_9700_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937800000)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_573_cast = mul(x = var_9698_cast, y = var_9700_to_fp16)[name = tensor<string, []>("input_573_cast")];
            tensor<int32, [2]> var_9708 = const()[name = tensor<string, []>("op_9708"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9710 = const()[name = tensor<string, []>("op_9710"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9712_pad_type_0 = const()[name = tensor<string, []>("op_9712_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9712_pad_0 = const()[name = tensor<string, []>("op_9712_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937802624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(947633088))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(947633280)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_9712_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_9710, groups = var_6865, pad = var_9712_pad_0, pad_type = var_9712_pad_type_0, strides = var_9708, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_573_cast)[name = tensor<string, []>("op_9712_cast")];
            tensor<int32, [2]> var_9713_split_sizes_0 = const()[name = tensor<string, []>("op_9713_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_9713_axis_0 = const()[name = tensor<string, []>("op_9713_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_9713_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_9713_cast_1 = split(axis = var_9713_axis_0, split_sizes = var_9713_split_sizes_0, x = var_9712_cast)[name = tensor<string, []>("op_9713_cast")];
            tensor<string, []> var_9715_mode_0 = const()[name = tensor<string, []>("op_9715_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_9715_cast = gelu(mode = var_9715_mode_0, x = var_9713_cast_1)[name = tensor<string, []>("op_9715_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_575_cast = mul(x = var_9713_cast_0, y = var_9715_cast)[name = tensor<string, []>("input_575_cast")];
            tensor<int32, [2]> var_9719 = const()[name = tensor<string, []>("op_9719"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9721 = const()[name = tensor<string, []>("op_9721"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9723_pad_type_0 = const()[name = tensor<string, []>("op_9723_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9723_pad_0 = const()[name = tensor<string, []>("op_9723_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(947653824))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(952569088))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(952569280)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9723_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_9721, groups = var_6865, pad = var_9723_pad_0, pad_type = var_9723_pad_type_0, strides = var_9719, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_575_cast)[name = tensor<string, []>("op_9723_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_295_cast = add(x = var_9723_cast, y = inputs_293_cast)[name = tensor<string, []>("inputs_295_cast")];
            tensor<int32, [1]> var_9733 = const()[name = tensor<string, []>("op_9733"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_295_cast = reduce_mean(axes = var_9733, keep_dims = var_6860, x = inputs_295_cast)[name = tensor<string, []>("channels_mean_295_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_295_cast = sub(x = inputs_295_cast, y = channels_mean_295_cast)[name = tensor<string, []>("zero_mean_295_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_295_cast = mul(x = zero_mean_295_cast, y = zero_mean_295_cast)[name = tensor<string, []>("zero_mean_sq_295_cast")];
            tensor<int32, [1]> var_9737 = const()[name = tensor<string, []>("op_9737"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9738_cast = reduce_mean(axes = var_9737, keep_dims = var_6860, x = zero_mean_sq_295_cast)[name = tensor<string, []>("op_9738_cast")];
            tensor<fp16, []> var_9739_to_fp16 = const()[name = tensor<string, []>("op_9739_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9740_cast = add(x = var_9738_cast, y = var_9739_to_fp16)[name = tensor<string, []>("op_9740_cast")];
            tensor<fp16, []> denom_295_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_295_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_295_cast = rsqrt(epsilon = denom_295_epsilon_0_to_fp16, x = var_9740_cast)[name = tensor<string, []>("denom_295_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_295_cast = mul(x = zero_mean_295_cast, y = denom_295_cast)[name = tensor<string, []>("out_295_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9744_to_fp16 = const()[name = tensor<string, []>("op_9744_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(952571904)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9745_cast = add(x = out_295_cast, y = var_9744_to_fp16)[name = tensor<string, []>("op_9745_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9747_to_fp16 = const()[name = tensor<string, []>("op_9747_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(952574528)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_391_cast = mul(x = var_9745_cast, y = var_9747_to_fp16)[name = tensor<string, []>("hidden_states_391_cast")];
            tensor<int32, [2]> var_9754 = const()[name = tensor<string, []>("op_9754"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9756 = const()[name = tensor<string, []>("op_9756"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_197_pad_type_0 = const()[name = tensor<string, []>("q_197_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_197_pad_0 = const()[name = tensor<string, []>("q_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(952577152))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(953396416))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_197_cast = conv(dilations = var_9756, groups = var_6865, pad = q_197_pad_0, pad_type = q_197_pad_type_0, strides = var_9754, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_391_cast)[name = tensor<string, []>("q_197_cast")];
            tensor<int32, [2]> var_9760 = const()[name = tensor<string, []>("op_9760"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9762 = const()[name = tensor<string, []>("op_9762"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_197_pad_type_0 = const()[name = tensor<string, []>("k_197_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_197_pad_0 = const()[name = tensor<string, []>("k_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(953396544))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(954215808))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_197_cast = conv(dilations = var_9762, groups = var_6865, pad = k_197_pad_0, pad_type = k_197_pad_type_0, strides = var_9760, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_391_cast)[name = tensor<string, []>("k_197_cast")];
            tensor<int32, [2]> var_9766 = const()[name = tensor<string, []>("op_9766"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9768 = const()[name = tensor<string, []>("op_9768"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_197_pad_type_0 = const()[name = tensor<string, []>("v_197_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_197_pad_0 = const()[name = tensor<string, []>("v_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(954215936))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(955444800))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_197_cast = conv(dilations = var_9768, groups = var_6865, pad = v_197_pad_0, pad_type = v_197_pad_type_0, strides = var_9766, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_391_cast)[name = tensor<string, []>("v_197_cast")];
            tensor<int32, [4]> var_9772 = const()[name = tensor<string, []>("op_9772"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9773_cast = reshape(shape = var_9772, x = q_197_cast)[name = tensor<string, []>("op_9773_cast")];
            tensor<int32, [4]> var_9774 = const()[name = tensor<string, []>("op_9774"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9775_cast = reshape(shape = var_9774, x = k_197_cast)[name = tensor<string, []>("op_9775_cast")];
            tensor<int32, [4]> var_9776 = const()[name = tensor<string, []>("op_9776"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9777_cast = reshape(shape = var_9776, x = v_197_cast)[name = tensor<string, []>("op_9777_cast")];
            tensor<bool, []> attn_weights_393_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_393_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_393_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_393_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_393_cast = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_9773_cast, y = var_9775_cast)[name = tensor<string, []>("attn_weights_393_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_395_cast = mul(x = attn_weights_393_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_395_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_9781_cast = softmax(axis = var_6849, x = attn_weights_395_cast)[name = tensor<string, []>("op_9781_cast")];
            tensor<bool, []> attn_197_transpose_x_0 = const()[name = tensor<string, []>("attn_197_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_197_transpose_y_0 = const()[name = tensor<string, []>("attn_197_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_197_cast = matmul(transpose_x = attn_197_transpose_x_0, transpose_y = attn_197_transpose_y_0, x = var_9777_cast, y = var_9781_cast)[name = tensor<string, []>("attn_197_cast")];
            tensor<int32, [4]> var_9785 = const()[name = tensor<string, []>("op_9785"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_577_cast = reshape(shape = var_9785, x = attn_197_cast)[name = tensor<string, []>("input_577_cast")];
            tensor<int32, [2]> var_9790 = const()[name = tensor<string, []>("op_9790"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9792 = const()[name = tensor<string, []>("op_9792"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9794_pad_type_0 = const()[name = tensor<string, []>("op_9794_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9794_pad_0 = const()[name = tensor<string, []>("op_9794_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(955444992))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(956673856))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(956674048)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9794_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_9792, groups = var_6865, pad = var_9794_pad_0, pad_type = var_9794_pad_type_0, strides = var_9790, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_577_cast)[name = tensor<string, []>("op_9794_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_297_cast = add(x = var_9794_cast, y = inputs_295_cast)[name = tensor<string, []>("inputs_297_cast")];
            tensor<int32, [1]> var_9798 = const()[name = tensor<string, []>("op_9798"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_297_cast = reduce_mean(axes = var_9798, keep_dims = var_6860, x = inputs_297_cast)[name = tensor<string, []>("channels_mean_297_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_297_cast = sub(x = inputs_297_cast, y = channels_mean_297_cast)[name = tensor<string, []>("zero_mean_297_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_297_cast = mul(x = zero_mean_297_cast, y = zero_mean_297_cast)[name = tensor<string, []>("zero_mean_sq_297_cast")];
            tensor<int32, [1]> var_9802 = const()[name = tensor<string, []>("op_9802"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9803_cast = reduce_mean(axes = var_9802, keep_dims = var_6860, x = zero_mean_sq_297_cast)[name = tensor<string, []>("op_9803_cast")];
            tensor<fp16, []> var_9804_to_fp16 = const()[name = tensor<string, []>("op_9804_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9805_cast = add(x = var_9803_cast, y = var_9804_to_fp16)[name = tensor<string, []>("op_9805_cast")];
            tensor<fp16, []> denom_297_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_297_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_297_cast = rsqrt(epsilon = denom_297_epsilon_0_to_fp16, x = var_9805_cast)[name = tensor<string, []>("denom_297_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_297_cast = mul(x = zero_mean_297_cast, y = denom_297_cast)[name = tensor<string, []>("out_297_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9809_to_fp16 = const()[name = tensor<string, []>("op_9809_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(956676672)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9810_cast = add(x = out_297_cast, y = var_9809_to_fp16)[name = tensor<string, []>("op_9810_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9812_to_fp16 = const()[name = tensor<string, []>("op_9812_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(956679296)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_393_cast = mul(x = var_9810_cast, y = var_9812_to_fp16)[name = tensor<string, []>("hidden_states_393_cast")];
            tensor<int32, [2]> var_9819 = const()[name = tensor<string, []>("op_9819"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9821 = const()[name = tensor<string, []>("op_9821"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_199_pad_type_0 = const()[name = tensor<string, []>("q_199_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_199_pad_0 = const()[name = tensor<string, []>("q_199_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(956681920))), lut = tensor<fp16, [4]>([-0x1.164p-5, -0x1.4ecp-7, 0x1.5p-7, 0x1.16p-5]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_199_cast = conv(dilations = var_9821, groups = var_6865, pad = q_199_pad_0, pad_type = q_199_pad_type_0, strides = var_9819, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_393_cast)[name = tensor<string, []>("q_199_cast")];
            tensor<int32, [2]> var_9825 = const()[name = tensor<string, []>("op_9825"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9827 = const()[name = tensor<string, []>("op_9827"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_199_pad_type_0 = const()[name = tensor<string, []>("k_199_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_199_pad_0 = const()[name = tensor<string, []>("k_199_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(957091584))), lut = tensor<fp16, [4]>([-0x1.cfcp-6, -0x1.12p-7, 0x1.0f8p-7, 0x1.cep-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_199_cast = conv(dilations = var_9827, groups = var_6865, pad = k_199_pad_0, pad_type = k_199_pad_type_0, strides = var_9825, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_199_cast")];
            tensor<int32, [2]> var_9831 = const()[name = tensor<string, []>("op_9831"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9833 = const()[name = tensor<string, []>("op_9833"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_199_pad_type_0 = const()[name = tensor<string, []>("v_199_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_199_pad_0 = const()[name = tensor<string, []>("v_199_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(957747008))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959057792))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_199_cast = conv(dilations = var_9833, groups = var_6865, pad = v_199_pad_0, pad_type = v_199_pad_type_0, strides = var_9831, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_199_cast")];
            tensor<int32, [4]> var_9837 = const()[name = tensor<string, []>("op_9837"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9838_cast = reshape(shape = var_9837, x = q_199_cast)[name = tensor<string, []>("op_9838_cast")];
            tensor<int32, [4]> var_9839 = const()[name = tensor<string, []>("op_9839"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9840_cast = reshape(shape = var_9839, x = k_199_cast)[name = tensor<string, []>("op_9840_cast")];
            tensor<int32, [4]> var_9841 = const()[name = tensor<string, []>("op_9841"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_9842_cast = reshape(shape = var_9841, x = v_199_cast)[name = tensor<string, []>("op_9842_cast")];
            tensor<bool, []> attn_weights_397_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_397_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_397_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_397_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_397_cast = matmul(transpose_x = attn_weights_397_transpose_x_0, transpose_y = attn_weights_397_transpose_y_0, x = var_9838_cast, y = var_9840_cast)[name = tensor<string, []>("attn_weights_397_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_399_cast = mul(x = attn_weights_397_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_399_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_9846_cast = softmax(axis = var_6849, x = attn_weights_399_cast)[name = tensor<string, []>("op_9846_cast")];
            tensor<bool, []> attn_199_transpose_x_0 = const()[name = tensor<string, []>("attn_199_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_199_transpose_y_0 = const()[name = tensor<string, []>("attn_199_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_199_cast = matmul(transpose_x = attn_199_transpose_x_0, transpose_y = attn_199_transpose_y_0, x = var_9842_cast, y = var_9846_cast)[name = tensor<string, []>("attn_199_cast")];
            tensor<int32, [4]> var_9850 = const()[name = tensor<string, []>("op_9850"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_579_cast = reshape(shape = var_9850, x = attn_199_cast)[name = tensor<string, []>("input_579_cast")];
            tensor<int32, [2]> var_9855 = const()[name = tensor<string, []>("op_9855"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9857 = const()[name = tensor<string, []>("op_9857"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9859_pad_type_0 = const()[name = tensor<string, []>("op_9859_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9859_pad_0 = const()[name = tensor<string, []>("op_9859_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959057920))), lut = tensor<fp16, [4]>([-0x1.2b8p-6, -0x1.664p-8, 0x1.644p-8, 0x1.2b4p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959467584)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9859_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_9857, groups = var_6865, pad = var_9859_pad_0, pad_type = var_9859_pad_type_0, strides = var_9855, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_579_cast)[name = tensor<string, []>("op_9859_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_299_cast = add(x = var_9859_cast, y = inputs_297_cast)[name = tensor<string, []>("inputs_299_cast")];
            tensor<int32, [1]> var_9863 = const()[name = tensor<string, []>("op_9863"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_299_cast = reduce_mean(axes = var_9863, keep_dims = var_6860, x = inputs_299_cast)[name = tensor<string, []>("channels_mean_299_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_299_cast = sub(x = inputs_299_cast, y = channels_mean_299_cast)[name = tensor<string, []>("zero_mean_299_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_299_cast = mul(x = zero_mean_299_cast, y = zero_mean_299_cast)[name = tensor<string, []>("zero_mean_sq_299_cast")];
            tensor<int32, [1]> var_9867 = const()[name = tensor<string, []>("op_9867"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9868_cast = reduce_mean(axes = var_9867, keep_dims = var_6860, x = zero_mean_sq_299_cast)[name = tensor<string, []>("op_9868_cast")];
            tensor<fp16, []> var_9869_to_fp16 = const()[name = tensor<string, []>("op_9869_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9870_cast = add(x = var_9868_cast, y = var_9869_to_fp16)[name = tensor<string, []>("op_9870_cast")];
            tensor<fp16, []> denom_299_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_299_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_299_cast = rsqrt(epsilon = denom_299_epsilon_0_to_fp16, x = var_9870_cast)[name = tensor<string, []>("denom_299_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_299_cast = mul(x = zero_mean_299_cast, y = denom_299_cast)[name = tensor<string, []>("out_299_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9874_to_fp16 = const()[name = tensor<string, []>("op_9874_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959470208)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9875_cast = add(x = out_299_cast, y = var_9874_to_fp16)[name = tensor<string, []>("op_9875_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9877_to_fp16 = const()[name = tensor<string, []>("op_9877_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959472832)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_581_cast = mul(x = var_9875_cast, y = var_9877_to_fp16)[name = tensor<string, []>("input_581_cast")];
            tensor<int32, [2]> var_9885 = const()[name = tensor<string, []>("op_9885"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9887 = const()[name = tensor<string, []>("op_9887"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9889_pad_type_0 = const()[name = tensor<string, []>("op_9889_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9889_pad_0 = const()[name = tensor<string, []>("op_9889_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959475456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(969305920))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(969306112)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_9889_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_9887, groups = var_6865, pad = var_9889_pad_0, pad_type = var_9889_pad_type_0, strides = var_9885, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_581_cast)[name = tensor<string, []>("op_9889_cast")];
            tensor<int32, [2]> var_9890_split_sizes_0 = const()[name = tensor<string, []>("op_9890_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_9890_axis_0 = const()[name = tensor<string, []>("op_9890_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_9890_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_9890_cast_1 = split(axis = var_9890_axis_0, split_sizes = var_9890_split_sizes_0, x = var_9889_cast)[name = tensor<string, []>("op_9890_cast")];
            tensor<string, []> var_9892_mode_0 = const()[name = tensor<string, []>("op_9892_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_9892_cast = gelu(mode = var_9892_mode_0, x = var_9890_cast_1)[name = tensor<string, []>("op_9892_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_583_cast = mul(x = var_9890_cast_0, y = var_9892_cast)[name = tensor<string, []>("input_583_cast")];
            tensor<int32, [2]> var_9896 = const()[name = tensor<string, []>("op_9896"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9898 = const()[name = tensor<string, []>("op_9898"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9900_pad_type_0 = const()[name = tensor<string, []>("op_9900_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9900_pad_0 = const()[name = tensor<string, []>("op_9900_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(969326656))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(974241920))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(974242112)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9900_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_9898, groups = var_6865, pad = var_9900_pad_0, pad_type = var_9900_pad_type_0, strides = var_9896, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_583_cast)[name = tensor<string, []>("op_9900_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_301_cast = add(x = var_9900_cast, y = inputs_299_cast)[name = tensor<string, []>("inputs_301_cast")];
            tensor<int32, [1]> var_9910 = const()[name = tensor<string, []>("op_9910"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_301_cast = reduce_mean(axes = var_9910, keep_dims = var_6860, x = inputs_301_cast)[name = tensor<string, []>("channels_mean_301_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_301_cast = sub(x = inputs_301_cast, y = channels_mean_301_cast)[name = tensor<string, []>("zero_mean_301_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_301_cast = mul(x = zero_mean_301_cast, y = zero_mean_301_cast)[name = tensor<string, []>("zero_mean_sq_301_cast")];
            tensor<int32, [1]> var_9914 = const()[name = tensor<string, []>("op_9914"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9915_cast = reduce_mean(axes = var_9914, keep_dims = var_6860, x = zero_mean_sq_301_cast)[name = tensor<string, []>("op_9915_cast")];
            tensor<fp16, []> var_9916_to_fp16 = const()[name = tensor<string, []>("op_9916_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9917_cast = add(x = var_9915_cast, y = var_9916_to_fp16)[name = tensor<string, []>("op_9917_cast")];
            tensor<fp16, []> denom_301_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_301_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_301_cast = rsqrt(epsilon = denom_301_epsilon_0_to_fp16, x = var_9917_cast)[name = tensor<string, []>("denom_301_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_301_cast = mul(x = zero_mean_301_cast, y = denom_301_cast)[name = tensor<string, []>("out_301_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9921_to_fp16 = const()[name = tensor<string, []>("op_9921_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(974244736)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9922_cast = add(x = out_301_cast, y = var_9921_to_fp16)[name = tensor<string, []>("op_9922_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9924_to_fp16 = const()[name = tensor<string, []>("op_9924_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(974247360)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_397_cast = mul(x = var_9922_cast, y = var_9924_to_fp16)[name = tensor<string, []>("hidden_states_397_cast")];
            tensor<int32, [2]> var_9931 = const()[name = tensor<string, []>("op_9931"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9933 = const()[name = tensor<string, []>("op_9933"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_201_pad_type_0 = const()[name = tensor<string, []>("q_201_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_201_pad_0 = const()[name = tensor<string, []>("q_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(974249984))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(975069248))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_201_cast = conv(dilations = var_9933, groups = var_6865, pad = q_201_pad_0, pad_type = q_201_pad_type_0, strides = var_9931, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_397_cast)[name = tensor<string, []>("q_201_cast")];
            tensor<int32, [2]> var_9937 = const()[name = tensor<string, []>("op_9937"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9939 = const()[name = tensor<string, []>("op_9939"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_201_pad_type_0 = const()[name = tensor<string, []>("k_201_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_201_pad_0 = const()[name = tensor<string, []>("k_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(975069376))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(975888640))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_201_cast = conv(dilations = var_9939, groups = var_6865, pad = k_201_pad_0, pad_type = k_201_pad_type_0, strides = var_9937, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_397_cast)[name = tensor<string, []>("k_201_cast")];
            tensor<int32, [2]> var_9943 = const()[name = tensor<string, []>("op_9943"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9945 = const()[name = tensor<string, []>("op_9945"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_201_pad_type_0 = const()[name = tensor<string, []>("v_201_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_201_pad_0 = const()[name = tensor<string, []>("v_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(975888768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(977117632))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_201_cast = conv(dilations = var_9945, groups = var_6865, pad = v_201_pad_0, pad_type = v_201_pad_type_0, strides = var_9943, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_397_cast)[name = tensor<string, []>("v_201_cast")];
            tensor<int32, [4]> var_9949 = const()[name = tensor<string, []>("op_9949"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9950_cast = reshape(shape = var_9949, x = q_201_cast)[name = tensor<string, []>("op_9950_cast")];
            tensor<int32, [4]> var_9951 = const()[name = tensor<string, []>("op_9951"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9952_cast = reshape(shape = var_9951, x = k_201_cast)[name = tensor<string, []>("op_9952_cast")];
            tensor<int32, [4]> var_9953 = const()[name = tensor<string, []>("op_9953"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_9954_cast = reshape(shape = var_9953, x = v_201_cast)[name = tensor<string, []>("op_9954_cast")];
            tensor<bool, []> attn_weights_401_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_401_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_401_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_401_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_401_cast = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9950_cast, y = var_9952_cast)[name = tensor<string, []>("attn_weights_401_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_403_cast = mul(x = attn_weights_401_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_403_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_9958_cast = softmax(axis = var_6849, x = attn_weights_403_cast)[name = tensor<string, []>("op_9958_cast")];
            tensor<bool, []> attn_201_transpose_x_0 = const()[name = tensor<string, []>("attn_201_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_201_transpose_y_0 = const()[name = tensor<string, []>("attn_201_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_201_cast = matmul(transpose_x = attn_201_transpose_x_0, transpose_y = attn_201_transpose_y_0, x = var_9954_cast, y = var_9958_cast)[name = tensor<string, []>("attn_201_cast")];
            tensor<int32, [4]> var_9962 = const()[name = tensor<string, []>("op_9962"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_585_cast = reshape(shape = var_9962, x = attn_201_cast)[name = tensor<string, []>("input_585_cast")];
            tensor<int32, [2]> var_9967 = const()[name = tensor<string, []>("op_9967"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9969 = const()[name = tensor<string, []>("op_9969"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_9971_pad_type_0 = const()[name = tensor<string, []>("op_9971_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_9971_pad_0 = const()[name = tensor<string, []>("op_9971_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(977117824))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978346688))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978346880)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9971_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_9969, groups = var_6865, pad = var_9971_pad_0, pad_type = var_9971_pad_type_0, strides = var_9967, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_585_cast)[name = tensor<string, []>("op_9971_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_303_cast = add(x = var_9971_cast, y = inputs_301_cast)[name = tensor<string, []>("inputs_303_cast")];
            tensor<int32, [1]> var_9975 = const()[name = tensor<string, []>("op_9975"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_303_cast = reduce_mean(axes = var_9975, keep_dims = var_6860, x = inputs_303_cast)[name = tensor<string, []>("channels_mean_303_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_303_cast = sub(x = inputs_303_cast, y = channels_mean_303_cast)[name = tensor<string, []>("zero_mean_303_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_303_cast = mul(x = zero_mean_303_cast, y = zero_mean_303_cast)[name = tensor<string, []>("zero_mean_sq_303_cast")];
            tensor<int32, [1]> var_9979 = const()[name = tensor<string, []>("op_9979"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_9980_cast = reduce_mean(axes = var_9979, keep_dims = var_6860, x = zero_mean_sq_303_cast)[name = tensor<string, []>("op_9980_cast")];
            tensor<fp16, []> var_9981_to_fp16 = const()[name = tensor<string, []>("op_9981_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_9982_cast = add(x = var_9980_cast, y = var_9981_to_fp16)[name = tensor<string, []>("op_9982_cast")];
            tensor<fp16, []> denom_303_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_303_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_303_cast = rsqrt(epsilon = denom_303_epsilon_0_to_fp16, x = var_9982_cast)[name = tensor<string, []>("denom_303_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_303_cast = mul(x = zero_mean_303_cast, y = denom_303_cast)[name = tensor<string, []>("out_303_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9986_to_fp16 = const()[name = tensor<string, []>("op_9986_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978349504)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_9987_cast = add(x = out_303_cast, y = var_9986_to_fp16)[name = tensor<string, []>("op_9987_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_9989_to_fp16 = const()[name = tensor<string, []>("op_9989_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978352128)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_399_cast = mul(x = var_9987_cast, y = var_9989_to_fp16)[name = tensor<string, []>("hidden_states_399_cast")];
            tensor<int32, [2]> var_9996 = const()[name = tensor<string, []>("op_9996"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_9998 = const()[name = tensor<string, []>("op_9998"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_203_pad_type_0 = const()[name = tensor<string, []>("q_203_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_203_pad_0 = const()[name = tensor<string, []>("q_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978354752))), lut = tensor<fp16, [4]>([-0x1.048p-5, -0x1.3ap-7, 0x1.3cp-7, 0x1.05p-5]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_203_cast = conv(dilations = var_9998, groups = var_6865, pad = q_203_pad_0, pad_type = q_203_pad_type_0, strides = var_9996, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_399_cast)[name = tensor<string, []>("q_203_cast")];
            tensor<int32, [2]> var_10002 = const()[name = tensor<string, []>("op_10002"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10004 = const()[name = tensor<string, []>("op_10004"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_203_pad_type_0 = const()[name = tensor<string, []>("k_203_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_203_pad_0 = const()[name = tensor<string, []>("k_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978764416))), lut = tensor<fp16, [4]>([-0x1.ac4p-6, -0x1.f84p-8, 0x1.f88p-8, 0x1.ac4p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_203_cast = conv(dilations = var_10004, groups = var_6865, pad = k_203_pad_0, pad_type = k_203_pad_type_0, strides = var_10002, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_203_cast")];
            tensor<int32, [2]> var_10008 = const()[name = tensor<string, []>("op_10008"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10010 = const()[name = tensor<string, []>("op_10010"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_203_pad_type_0 = const()[name = tensor<string, []>("v_203_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_203_pad_0 = const()[name = tensor<string, []>("v_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(979419840))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(980730624))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_203_cast = conv(dilations = var_10010, groups = var_6865, pad = v_203_pad_0, pad_type = v_203_pad_type_0, strides = var_10008, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_203_cast")];
            tensor<int32, [4]> var_10014 = const()[name = tensor<string, []>("op_10014"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10015_cast = reshape(shape = var_10014, x = q_203_cast)[name = tensor<string, []>("op_10015_cast")];
            tensor<int32, [4]> var_10016 = const()[name = tensor<string, []>("op_10016"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10017_cast = reshape(shape = var_10016, x = k_203_cast)[name = tensor<string, []>("op_10017_cast")];
            tensor<int32, [4]> var_10018 = const()[name = tensor<string, []>("op_10018"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10019_cast = reshape(shape = var_10018, x = v_203_cast)[name = tensor<string, []>("op_10019_cast")];
            tensor<bool, []> attn_weights_405_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_405_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_405_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_405_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_405_cast = matmul(transpose_x = attn_weights_405_transpose_x_0, transpose_y = attn_weights_405_transpose_y_0, x = var_10015_cast, y = var_10017_cast)[name = tensor<string, []>("attn_weights_405_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_407_cast = mul(x = attn_weights_405_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_407_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_10023_cast = softmax(axis = var_6849, x = attn_weights_407_cast)[name = tensor<string, []>("op_10023_cast")];
            tensor<bool, []> attn_203_transpose_x_0 = const()[name = tensor<string, []>("attn_203_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_203_transpose_y_0 = const()[name = tensor<string, []>("attn_203_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_203_cast = matmul(transpose_x = attn_203_transpose_x_0, transpose_y = attn_203_transpose_y_0, x = var_10019_cast, y = var_10023_cast)[name = tensor<string, []>("attn_203_cast")];
            tensor<int32, [4]> var_10027 = const()[name = tensor<string, []>("op_10027"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_587_cast = reshape(shape = var_10027, x = attn_203_cast)[name = tensor<string, []>("input_587_cast")];
            tensor<int32, [2]> var_10032 = const()[name = tensor<string, []>("op_10032"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10034 = const()[name = tensor<string, []>("op_10034"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10036_pad_type_0 = const()[name = tensor<string, []>("op_10036_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10036_pad_0 = const()[name = tensor<string, []>("op_10036_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(980730752))), lut = tensor<fp16, [4]>([-0x1.1bcp-6, -0x1.55p-8, 0x1.52p-8, 0x1.1acp-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(981140416)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10036_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_10034, groups = var_6865, pad = var_10036_pad_0, pad_type = var_10036_pad_type_0, strides = var_10032, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_587_cast)[name = tensor<string, []>("op_10036_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_305_cast = add(x = var_10036_cast, y = inputs_303_cast)[name = tensor<string, []>("inputs_305_cast")];
            tensor<int32, [1]> var_10040 = const()[name = tensor<string, []>("op_10040"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_305_cast = reduce_mean(axes = var_10040, keep_dims = var_6860, x = inputs_305_cast)[name = tensor<string, []>("channels_mean_305_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_305_cast = sub(x = inputs_305_cast, y = channels_mean_305_cast)[name = tensor<string, []>("zero_mean_305_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_305_cast = mul(x = zero_mean_305_cast, y = zero_mean_305_cast)[name = tensor<string, []>("zero_mean_sq_305_cast")];
            tensor<int32, [1]> var_10044 = const()[name = tensor<string, []>("op_10044"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10045_cast = reduce_mean(axes = var_10044, keep_dims = var_6860, x = zero_mean_sq_305_cast)[name = tensor<string, []>("op_10045_cast")];
            tensor<fp16, []> var_10046_to_fp16 = const()[name = tensor<string, []>("op_10046_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10047_cast = add(x = var_10045_cast, y = var_10046_to_fp16)[name = tensor<string, []>("op_10047_cast")];
            tensor<fp16, []> denom_305_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_305_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_305_cast = rsqrt(epsilon = denom_305_epsilon_0_to_fp16, x = var_10047_cast)[name = tensor<string, []>("denom_305_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_305_cast = mul(x = zero_mean_305_cast, y = denom_305_cast)[name = tensor<string, []>("out_305_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10051_to_fp16 = const()[name = tensor<string, []>("op_10051_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(981143040)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10052_cast = add(x = out_305_cast, y = var_10051_to_fp16)[name = tensor<string, []>("op_10052_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10054_to_fp16 = const()[name = tensor<string, []>("op_10054_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(981145664)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_589_cast = mul(x = var_10052_cast, y = var_10054_to_fp16)[name = tensor<string, []>("input_589_cast")];
            tensor<int32, [2]> var_10062 = const()[name = tensor<string, []>("op_10062"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10064 = const()[name = tensor<string, []>("op_10064"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10066_pad_type_0 = const()[name = tensor<string, []>("op_10066_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10066_pad_0 = const()[name = tensor<string, []>("op_10066_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(981148288))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(990978752))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(990978944)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_10066_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_10064, groups = var_6865, pad = var_10066_pad_0, pad_type = var_10066_pad_type_0, strides = var_10062, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_589_cast)[name = tensor<string, []>("op_10066_cast")];
            tensor<int32, [2]> var_10067_split_sizes_0 = const()[name = tensor<string, []>("op_10067_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_10067_axis_0 = const()[name = tensor<string, []>("op_10067_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_10067_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_10067_cast_1 = split(axis = var_10067_axis_0, split_sizes = var_10067_split_sizes_0, x = var_10066_cast)[name = tensor<string, []>("op_10067_cast")];
            tensor<string, []> var_10069_mode_0 = const()[name = tensor<string, []>("op_10069_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_10069_cast = gelu(mode = var_10069_mode_0, x = var_10067_cast_1)[name = tensor<string, []>("op_10069_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_591_cast = mul(x = var_10067_cast_0, y = var_10069_cast)[name = tensor<string, []>("input_591_cast")];
            tensor<int32, [2]> var_10073 = const()[name = tensor<string, []>("op_10073"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10075 = const()[name = tensor<string, []>("op_10075"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10077_pad_type_0 = const()[name = tensor<string, []>("op_10077_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10077_pad_0 = const()[name = tensor<string, []>("op_10077_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(990999488))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(995914752))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(995914944)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10077_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_10075, groups = var_6865, pad = var_10077_pad_0, pad_type = var_10077_pad_type_0, strides = var_10073, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_591_cast)[name = tensor<string, []>("op_10077_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_307_cast = add(x = var_10077_cast, y = inputs_305_cast)[name = tensor<string, []>("inputs_307_cast")];
            tensor<int32, [1]> var_10087 = const()[name = tensor<string, []>("op_10087"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_307_cast = reduce_mean(axes = var_10087, keep_dims = var_6860, x = inputs_307_cast)[name = tensor<string, []>("channels_mean_307_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_307_cast = sub(x = inputs_307_cast, y = channels_mean_307_cast)[name = tensor<string, []>("zero_mean_307_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_307_cast = mul(x = zero_mean_307_cast, y = zero_mean_307_cast)[name = tensor<string, []>("zero_mean_sq_307_cast")];
            tensor<int32, [1]> var_10091 = const()[name = tensor<string, []>("op_10091"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10092_cast = reduce_mean(axes = var_10091, keep_dims = var_6860, x = zero_mean_sq_307_cast)[name = tensor<string, []>("op_10092_cast")];
            tensor<fp16, []> var_10093_to_fp16 = const()[name = tensor<string, []>("op_10093_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10094_cast = add(x = var_10092_cast, y = var_10093_to_fp16)[name = tensor<string, []>("op_10094_cast")];
            tensor<fp16, []> denom_307_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_307_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_307_cast = rsqrt(epsilon = denom_307_epsilon_0_to_fp16, x = var_10094_cast)[name = tensor<string, []>("denom_307_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_307_cast = mul(x = zero_mean_307_cast, y = denom_307_cast)[name = tensor<string, []>("out_307_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10098_to_fp16 = const()[name = tensor<string, []>("op_10098_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(995917568)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10099_cast = add(x = out_307_cast, y = var_10098_to_fp16)[name = tensor<string, []>("op_10099_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10101_to_fp16 = const()[name = tensor<string, []>("op_10101_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(995920192)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_403_cast = mul(x = var_10099_cast, y = var_10101_to_fp16)[name = tensor<string, []>("hidden_states_403_cast")];
            tensor<int32, [2]> var_10108 = const()[name = tensor<string, []>("op_10108"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10110 = const()[name = tensor<string, []>("op_10110"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_205_pad_type_0 = const()[name = tensor<string, []>("q_205_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_205_pad_0 = const()[name = tensor<string, []>("q_205_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(995922816))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(996742080))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_205_cast = conv(dilations = var_10110, groups = var_6865, pad = q_205_pad_0, pad_type = q_205_pad_type_0, strides = var_10108, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_403_cast)[name = tensor<string, []>("q_205_cast")];
            tensor<int32, [2]> var_10114 = const()[name = tensor<string, []>("op_10114"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10116 = const()[name = tensor<string, []>("op_10116"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_205_pad_type_0 = const()[name = tensor<string, []>("k_205_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_205_pad_0 = const()[name = tensor<string, []>("k_205_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(996742208))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(997561472))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_205_cast = conv(dilations = var_10116, groups = var_6865, pad = k_205_pad_0, pad_type = k_205_pad_type_0, strides = var_10114, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_403_cast)[name = tensor<string, []>("k_205_cast")];
            tensor<int32, [2]> var_10120 = const()[name = tensor<string, []>("op_10120"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10122 = const()[name = tensor<string, []>("op_10122"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_205_pad_type_0 = const()[name = tensor<string, []>("v_205_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_205_pad_0 = const()[name = tensor<string, []>("v_205_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(997561600))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(998790464))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_205_cast = conv(dilations = var_10122, groups = var_6865, pad = v_205_pad_0, pad_type = v_205_pad_type_0, strides = var_10120, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_403_cast)[name = tensor<string, []>("v_205_cast")];
            tensor<int32, [4]> var_10126 = const()[name = tensor<string, []>("op_10126"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10127_cast = reshape(shape = var_10126, x = q_205_cast)[name = tensor<string, []>("op_10127_cast")];
            tensor<int32, [4]> var_10128 = const()[name = tensor<string, []>("op_10128"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10129_cast = reshape(shape = var_10128, x = k_205_cast)[name = tensor<string, []>("op_10129_cast")];
            tensor<int32, [4]> var_10130 = const()[name = tensor<string, []>("op_10130"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10131_cast = reshape(shape = var_10130, x = v_205_cast)[name = tensor<string, []>("op_10131_cast")];
            tensor<bool, []> attn_weights_409_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_409_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_409_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_409_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_409_cast = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_10127_cast, y = var_10129_cast)[name = tensor<string, []>("attn_weights_409_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_411_cast = mul(x = attn_weights_409_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_411_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_10135_cast = softmax(axis = var_6849, x = attn_weights_411_cast)[name = tensor<string, []>("op_10135_cast")];
            tensor<bool, []> attn_205_transpose_x_0 = const()[name = tensor<string, []>("attn_205_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_205_transpose_y_0 = const()[name = tensor<string, []>("attn_205_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_205_cast = matmul(transpose_x = attn_205_transpose_x_0, transpose_y = attn_205_transpose_y_0, x = var_10131_cast, y = var_10135_cast)[name = tensor<string, []>("attn_205_cast")];
            tensor<int32, [4]> var_10139 = const()[name = tensor<string, []>("op_10139"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_593_cast = reshape(shape = var_10139, x = attn_205_cast)[name = tensor<string, []>("input_593_cast")];
            tensor<int32, [2]> var_10144 = const()[name = tensor<string, []>("op_10144"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10146 = const()[name = tensor<string, []>("op_10146"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10148_pad_type_0 = const()[name = tensor<string, []>("op_10148_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10148_pad_0 = const()[name = tensor<string, []>("op_10148_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(998790656))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1000019520))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1000019712)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10148_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_10146, groups = var_6865, pad = var_10148_pad_0, pad_type = var_10148_pad_type_0, strides = var_10144, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_593_cast)[name = tensor<string, []>("op_10148_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_309_cast = add(x = var_10148_cast, y = inputs_307_cast)[name = tensor<string, []>("inputs_309_cast")];
            tensor<int32, [1]> var_10152 = const()[name = tensor<string, []>("op_10152"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_309_cast = reduce_mean(axes = var_10152, keep_dims = var_6860, x = inputs_309_cast)[name = tensor<string, []>("channels_mean_309_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_309_cast = sub(x = inputs_309_cast, y = channels_mean_309_cast)[name = tensor<string, []>("zero_mean_309_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_309_cast = mul(x = zero_mean_309_cast, y = zero_mean_309_cast)[name = tensor<string, []>("zero_mean_sq_309_cast")];
            tensor<int32, [1]> var_10156 = const()[name = tensor<string, []>("op_10156"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10157_cast = reduce_mean(axes = var_10156, keep_dims = var_6860, x = zero_mean_sq_309_cast)[name = tensor<string, []>("op_10157_cast")];
            tensor<fp16, []> var_10158_to_fp16 = const()[name = tensor<string, []>("op_10158_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10159_cast = add(x = var_10157_cast, y = var_10158_to_fp16)[name = tensor<string, []>("op_10159_cast")];
            tensor<fp16, []> denom_309_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_309_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_309_cast = rsqrt(epsilon = denom_309_epsilon_0_to_fp16, x = var_10159_cast)[name = tensor<string, []>("denom_309_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_309_cast = mul(x = zero_mean_309_cast, y = denom_309_cast)[name = tensor<string, []>("out_309_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10163_to_fp16 = const()[name = tensor<string, []>("op_10163_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1000022336)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10164_cast = add(x = out_309_cast, y = var_10163_to_fp16)[name = tensor<string, []>("op_10164_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10166_to_fp16 = const()[name = tensor<string, []>("op_10166_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1000024960)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_405_cast = mul(x = var_10164_cast, y = var_10166_to_fp16)[name = tensor<string, []>("hidden_states_405_cast")];
            tensor<int32, [2]> var_10173 = const()[name = tensor<string, []>("op_10173"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10175 = const()[name = tensor<string, []>("op_10175"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_207_pad_type_0 = const()[name = tensor<string, []>("q_207_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_207_pad_0 = const()[name = tensor<string, []>("q_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1000027584))), lut = tensor<fp16, [4]>([-0x1.d34p-6, -0x1.1dp-7, 0x1.1ep-7, 0x1.d2cp-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_207_cast = conv(dilations = var_10175, groups = var_6865, pad = q_207_pad_0, pad_type = q_207_pad_type_0, strides = var_10173, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_405_cast)[name = tensor<string, []>("q_207_cast")];
            tensor<int32, [2]> var_10179 = const()[name = tensor<string, []>("op_10179"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10181 = const()[name = tensor<string, []>("op_10181"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_207_pad_type_0 = const()[name = tensor<string, []>("k_207_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_207_pad_0 = const()[name = tensor<string, []>("k_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1000437248))), lut = tensor<fp16, [4]>([-0x1.664p-6, -0x1.a54p-8, 0x1.b0cp-8, 0x1.69p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_207_cast = conv(dilations = var_10181, groups = var_6865, pad = k_207_pad_0, pad_type = k_207_pad_type_0, strides = var_10179, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_207_cast")];
            tensor<int32, [2]> var_10185 = const()[name = tensor<string, []>("op_10185"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10187 = const()[name = tensor<string, []>("op_10187"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_207_pad_type_0 = const()[name = tensor<string, []>("v_207_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_207_pad_0 = const()[name = tensor<string, []>("v_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1001092672))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1002403456))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_207_cast = conv(dilations = var_10187, groups = var_6865, pad = v_207_pad_0, pad_type = v_207_pad_type_0, strides = var_10185, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_207_cast")];
            tensor<int32, [4]> var_10191 = const()[name = tensor<string, []>("op_10191"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10192_cast = reshape(shape = var_10191, x = q_207_cast)[name = tensor<string, []>("op_10192_cast")];
            tensor<int32, [4]> var_10193 = const()[name = tensor<string, []>("op_10193"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10194_cast = reshape(shape = var_10193, x = k_207_cast)[name = tensor<string, []>("op_10194_cast")];
            tensor<int32, [4]> var_10195 = const()[name = tensor<string, []>("op_10195"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10196_cast = reshape(shape = var_10195, x = v_207_cast)[name = tensor<string, []>("op_10196_cast")];
            tensor<bool, []> attn_weights_413_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_413_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_413_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_413_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_413_cast = matmul(transpose_x = attn_weights_413_transpose_x_0, transpose_y = attn_weights_413_transpose_y_0, x = var_10192_cast, y = var_10194_cast)[name = tensor<string, []>("attn_weights_413_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_415_cast = mul(x = attn_weights_413_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_415_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_10200_cast = softmax(axis = var_6849, x = attn_weights_415_cast)[name = tensor<string, []>("op_10200_cast")];
            tensor<bool, []> attn_207_transpose_x_0 = const()[name = tensor<string, []>("attn_207_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_207_transpose_y_0 = const()[name = tensor<string, []>("attn_207_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_207_cast = matmul(transpose_x = attn_207_transpose_x_0, transpose_y = attn_207_transpose_y_0, x = var_10196_cast, y = var_10200_cast)[name = tensor<string, []>("attn_207_cast")];
            tensor<int32, [4]> var_10204 = const()[name = tensor<string, []>("op_10204"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_595_cast = reshape(shape = var_10204, x = attn_207_cast)[name = tensor<string, []>("input_595_cast")];
            tensor<int32, [2]> var_10209 = const()[name = tensor<string, []>("op_10209"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10211 = const()[name = tensor<string, []>("op_10211"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10213_pad_type_0 = const()[name = tensor<string, []>("op_10213_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10213_pad_0 = const()[name = tensor<string, []>("op_10213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1002403584))), lut = tensor<fp16, [4]>([-0x1.09p-6, -0x1.3ep-8, 0x1.3b8p-8, 0x1.084p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1002813248)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10213_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_10211, groups = var_6865, pad = var_10213_pad_0, pad_type = var_10213_pad_type_0, strides = var_10209, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_595_cast)[name = tensor<string, []>("op_10213_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_311_cast = add(x = var_10213_cast, y = inputs_309_cast)[name = tensor<string, []>("inputs_311_cast")];
            tensor<int32, [1]> var_10217 = const()[name = tensor<string, []>("op_10217"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_311_cast = reduce_mean(axes = var_10217, keep_dims = var_6860, x = inputs_311_cast)[name = tensor<string, []>("channels_mean_311_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_311_cast = sub(x = inputs_311_cast, y = channels_mean_311_cast)[name = tensor<string, []>("zero_mean_311_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_311_cast = mul(x = zero_mean_311_cast, y = zero_mean_311_cast)[name = tensor<string, []>("zero_mean_sq_311_cast")];
            tensor<int32, [1]> var_10221 = const()[name = tensor<string, []>("op_10221"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10222_cast = reduce_mean(axes = var_10221, keep_dims = var_6860, x = zero_mean_sq_311_cast)[name = tensor<string, []>("op_10222_cast")];
            tensor<fp16, []> var_10223_to_fp16 = const()[name = tensor<string, []>("op_10223_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10224_cast = add(x = var_10222_cast, y = var_10223_to_fp16)[name = tensor<string, []>("op_10224_cast")];
            tensor<fp16, []> denom_311_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_311_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_311_cast = rsqrt(epsilon = denom_311_epsilon_0_to_fp16, x = var_10224_cast)[name = tensor<string, []>("denom_311_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_311_cast = mul(x = zero_mean_311_cast, y = denom_311_cast)[name = tensor<string, []>("out_311_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10228_to_fp16 = const()[name = tensor<string, []>("op_10228_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1002815872)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10229_cast = add(x = out_311_cast, y = var_10228_to_fp16)[name = tensor<string, []>("op_10229_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10231_to_fp16 = const()[name = tensor<string, []>("op_10231_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1002818496)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_597_cast = mul(x = var_10229_cast, y = var_10231_to_fp16)[name = tensor<string, []>("input_597_cast")];
            tensor<int32, [2]> var_10239 = const()[name = tensor<string, []>("op_10239"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10241 = const()[name = tensor<string, []>("op_10241"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10243_pad_type_0 = const()[name = tensor<string, []>("op_10243_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10243_pad_0 = const()[name = tensor<string, []>("op_10243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1002821120))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1012651584))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1012651776)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_10243_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_10241, groups = var_6865, pad = var_10243_pad_0, pad_type = var_10243_pad_type_0, strides = var_10239, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_597_cast)[name = tensor<string, []>("op_10243_cast")];
            tensor<int32, [2]> var_10244_split_sizes_0 = const()[name = tensor<string, []>("op_10244_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_10244_axis_0 = const()[name = tensor<string, []>("op_10244_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_10244_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_10244_cast_1 = split(axis = var_10244_axis_0, split_sizes = var_10244_split_sizes_0, x = var_10243_cast)[name = tensor<string, []>("op_10244_cast")];
            tensor<string, []> var_10246_mode_0 = const()[name = tensor<string, []>("op_10246_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_10246_cast = gelu(mode = var_10246_mode_0, x = var_10244_cast_1)[name = tensor<string, []>("op_10246_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_599_cast = mul(x = var_10244_cast_0, y = var_10246_cast)[name = tensor<string, []>("input_599_cast")];
            tensor<int32, [2]> var_10250 = const()[name = tensor<string, []>("op_10250"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10252 = const()[name = tensor<string, []>("op_10252"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10254_pad_type_0 = const()[name = tensor<string, []>("op_10254_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10254_pad_0 = const()[name = tensor<string, []>("op_10254_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1012672320))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017587584))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017587776)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10254_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_10252, groups = var_6865, pad = var_10254_pad_0, pad_type = var_10254_pad_type_0, strides = var_10250, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_599_cast)[name = tensor<string, []>("op_10254_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_313_cast = add(x = var_10254_cast, y = inputs_311_cast)[name = tensor<string, []>("inputs_313_cast")];
            tensor<int32, [1]> var_10264 = const()[name = tensor<string, []>("op_10264"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_313_cast = reduce_mean(axes = var_10264, keep_dims = var_6860, x = inputs_313_cast)[name = tensor<string, []>("channels_mean_313_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_313_cast = sub(x = inputs_313_cast, y = channels_mean_313_cast)[name = tensor<string, []>("zero_mean_313_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_313_cast = mul(x = zero_mean_313_cast, y = zero_mean_313_cast)[name = tensor<string, []>("zero_mean_sq_313_cast")];
            tensor<int32, [1]> var_10268 = const()[name = tensor<string, []>("op_10268"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10269_cast = reduce_mean(axes = var_10268, keep_dims = var_6860, x = zero_mean_sq_313_cast)[name = tensor<string, []>("op_10269_cast")];
            tensor<fp16, []> var_10270_to_fp16 = const()[name = tensor<string, []>("op_10270_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10271_cast = add(x = var_10269_cast, y = var_10270_to_fp16)[name = tensor<string, []>("op_10271_cast")];
            tensor<fp16, []> denom_313_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_313_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_313_cast = rsqrt(epsilon = denom_313_epsilon_0_to_fp16, x = var_10271_cast)[name = tensor<string, []>("denom_313_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_313_cast = mul(x = zero_mean_313_cast, y = denom_313_cast)[name = tensor<string, []>("out_313_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10275_to_fp16 = const()[name = tensor<string, []>("op_10275_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017590400)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10276_cast = add(x = out_313_cast, y = var_10275_to_fp16)[name = tensor<string, []>("op_10276_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10278_to_fp16 = const()[name = tensor<string, []>("op_10278_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017593024)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_409_cast = mul(x = var_10276_cast, y = var_10278_to_fp16)[name = tensor<string, []>("hidden_states_409_cast")];
            tensor<int32, [2]> var_10285 = const()[name = tensor<string, []>("op_10285"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10287 = const()[name = tensor<string, []>("op_10287"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_209_pad_type_0 = const()[name = tensor<string, []>("q_209_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_209_pad_0 = const()[name = tensor<string, []>("q_209_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017595648))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1018414912))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_209_cast = conv(dilations = var_10287, groups = var_6865, pad = q_209_pad_0, pad_type = q_209_pad_type_0, strides = var_10285, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_409_cast)[name = tensor<string, []>("q_209_cast")];
            tensor<int32, [2]> var_10291 = const()[name = tensor<string, []>("op_10291"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10293 = const()[name = tensor<string, []>("op_10293"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_209_pad_type_0 = const()[name = tensor<string, []>("k_209_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_209_pad_0 = const()[name = tensor<string, []>("k_209_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1018415040))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1019234304))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_209_cast = conv(dilations = var_10293, groups = var_6865, pad = k_209_pad_0, pad_type = k_209_pad_type_0, strides = var_10291, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_409_cast)[name = tensor<string, []>("k_209_cast")];
            tensor<int32, [2]> var_10297 = const()[name = tensor<string, []>("op_10297"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10299 = const()[name = tensor<string, []>("op_10299"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_209_pad_type_0 = const()[name = tensor<string, []>("v_209_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_209_pad_0 = const()[name = tensor<string, []>("v_209_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1019234432))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1020463296))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_209_cast = conv(dilations = var_10299, groups = var_6865, pad = v_209_pad_0, pad_type = v_209_pad_type_0, strides = var_10297, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_409_cast)[name = tensor<string, []>("v_209_cast")];
            tensor<int32, [4]> var_10303 = const()[name = tensor<string, []>("op_10303"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10304_cast = reshape(shape = var_10303, x = q_209_cast)[name = tensor<string, []>("op_10304_cast")];
            tensor<int32, [4]> var_10305 = const()[name = tensor<string, []>("op_10305"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10306_cast = reshape(shape = var_10305, x = k_209_cast)[name = tensor<string, []>("op_10306_cast")];
            tensor<int32, [4]> var_10307 = const()[name = tensor<string, []>("op_10307"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10308_cast = reshape(shape = var_10307, x = v_209_cast)[name = tensor<string, []>("op_10308_cast")];
            tensor<bool, []> attn_weights_417_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_417_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_417_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_417_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_417_cast = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_10304_cast, y = var_10306_cast)[name = tensor<string, []>("attn_weights_417_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_419_cast = mul(x = attn_weights_417_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_419_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_10312_cast = softmax(axis = var_6849, x = attn_weights_419_cast)[name = tensor<string, []>("op_10312_cast")];
            tensor<bool, []> attn_209_transpose_x_0 = const()[name = tensor<string, []>("attn_209_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_209_transpose_y_0 = const()[name = tensor<string, []>("attn_209_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_209_cast = matmul(transpose_x = attn_209_transpose_x_0, transpose_y = attn_209_transpose_y_0, x = var_10308_cast, y = var_10312_cast)[name = tensor<string, []>("attn_209_cast")];
            tensor<int32, [4]> var_10316 = const()[name = tensor<string, []>("op_10316"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_601_cast = reshape(shape = var_10316, x = attn_209_cast)[name = tensor<string, []>("input_601_cast")];
            tensor<int32, [2]> var_10321 = const()[name = tensor<string, []>("op_10321"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10323 = const()[name = tensor<string, []>("op_10323"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10325_pad_type_0 = const()[name = tensor<string, []>("op_10325_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10325_pad_0 = const()[name = tensor<string, []>("op_10325_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1020463488))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1021692352))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1021692544)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10325_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_10323, groups = var_6865, pad = var_10325_pad_0, pad_type = var_10325_pad_type_0, strides = var_10321, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_601_cast)[name = tensor<string, []>("op_10325_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_315_cast = add(x = var_10325_cast, y = inputs_313_cast)[name = tensor<string, []>("inputs_315_cast")];
            tensor<int32, [1]> var_10329 = const()[name = tensor<string, []>("op_10329"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_315_cast = reduce_mean(axes = var_10329, keep_dims = var_6860, x = inputs_315_cast)[name = tensor<string, []>("channels_mean_315_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_315_cast = sub(x = inputs_315_cast, y = channels_mean_315_cast)[name = tensor<string, []>("zero_mean_315_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_315_cast = mul(x = zero_mean_315_cast, y = zero_mean_315_cast)[name = tensor<string, []>("zero_mean_sq_315_cast")];
            tensor<int32, [1]> var_10333 = const()[name = tensor<string, []>("op_10333"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10334_cast = reduce_mean(axes = var_10333, keep_dims = var_6860, x = zero_mean_sq_315_cast)[name = tensor<string, []>("op_10334_cast")];
            tensor<fp16, []> var_10335_to_fp16 = const()[name = tensor<string, []>("op_10335_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10336_cast = add(x = var_10334_cast, y = var_10335_to_fp16)[name = tensor<string, []>("op_10336_cast")];
            tensor<fp16, []> denom_315_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_315_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_315_cast = rsqrt(epsilon = denom_315_epsilon_0_to_fp16, x = var_10336_cast)[name = tensor<string, []>("denom_315_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_315_cast = mul(x = zero_mean_315_cast, y = denom_315_cast)[name = tensor<string, []>("out_315_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10340_to_fp16 = const()[name = tensor<string, []>("op_10340_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1021695168)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10341_cast = add(x = out_315_cast, y = var_10340_to_fp16)[name = tensor<string, []>("op_10341_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10343_to_fp16 = const()[name = tensor<string, []>("op_10343_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1021697792)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_411_cast = mul(x = var_10341_cast, y = var_10343_to_fp16)[name = tensor<string, []>("hidden_states_411_cast")];
            tensor<int32, [2]> var_10350 = const()[name = tensor<string, []>("op_10350"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10352 = const()[name = tensor<string, []>("op_10352"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_211_pad_type_0 = const()[name = tensor<string, []>("q_211_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_211_pad_0 = const()[name = tensor<string, []>("q_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1021700416))), lut = tensor<fp16, [4]>([-0x1.a5p-6, -0x1.048p-7, 0x1.054p-7, 0x1.a54p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_211_cast = conv(dilations = var_10352, groups = var_6865, pad = q_211_pad_0, pad_type = q_211_pad_type_0, strides = var_10350, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_411_cast)[name = tensor<string, []>("q_211_cast")];
            tensor<int32, [2]> var_10356 = const()[name = tensor<string, []>("op_10356"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10358 = const()[name = tensor<string, []>("op_10358"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_211_pad_type_0 = const()[name = tensor<string, []>("k_211_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_211_pad_0 = const()[name = tensor<string, []>("k_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1022110080))), lut = tensor<fp16, [4]>([-0x1.404p-6, -0x1.808p-8, 0x1.7ccp-8, 0x1.3f4p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_211_cast = conv(dilations = var_10358, groups = var_6865, pad = k_211_pad_0, pad_type = k_211_pad_type_0, strides = var_10356, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_211_cast")];
            tensor<int32, [2]> var_10362 = const()[name = tensor<string, []>("op_10362"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10364 = const()[name = tensor<string, []>("op_10364"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_211_pad_type_0 = const()[name = tensor<string, []>("v_211_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_211_pad_0 = const()[name = tensor<string, []>("v_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1022765504))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024076288))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_211_cast = conv(dilations = var_10364, groups = var_6865, pad = v_211_pad_0, pad_type = v_211_pad_type_0, strides = var_10362, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_211_cast")];
            tensor<int32, [4]> var_10368 = const()[name = tensor<string, []>("op_10368"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10369_cast = reshape(shape = var_10368, x = q_211_cast)[name = tensor<string, []>("op_10369_cast")];
            tensor<int32, [4]> var_10370 = const()[name = tensor<string, []>("op_10370"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10371_cast = reshape(shape = var_10370, x = k_211_cast)[name = tensor<string, []>("op_10371_cast")];
            tensor<int32, [4]> var_10372 = const()[name = tensor<string, []>("op_10372"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10373_cast = reshape(shape = var_10372, x = v_211_cast)[name = tensor<string, []>("op_10373_cast")];
            tensor<bool, []> attn_weights_421_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_421_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_421_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_421_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_421_cast = matmul(transpose_x = attn_weights_421_transpose_x_0, transpose_y = attn_weights_421_transpose_y_0, x = var_10369_cast, y = var_10371_cast)[name = tensor<string, []>("attn_weights_421_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_423_cast = mul(x = attn_weights_421_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_423_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_10377_cast = softmax(axis = var_6849, x = attn_weights_423_cast)[name = tensor<string, []>("op_10377_cast")];
            tensor<bool, []> attn_211_transpose_x_0 = const()[name = tensor<string, []>("attn_211_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_211_transpose_y_0 = const()[name = tensor<string, []>("attn_211_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_211_cast = matmul(transpose_x = attn_211_transpose_x_0, transpose_y = attn_211_transpose_y_0, x = var_10373_cast, y = var_10377_cast)[name = tensor<string, []>("attn_211_cast")];
            tensor<int32, [4]> var_10381 = const()[name = tensor<string, []>("op_10381"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_603_cast = reshape(shape = var_10381, x = attn_211_cast)[name = tensor<string, []>("input_603_cast")];
            tensor<int32, [2]> var_10386 = const()[name = tensor<string, []>("op_10386"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10388 = const()[name = tensor<string, []>("op_10388"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10390_pad_type_0 = const()[name = tensor<string, []>("op_10390_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10390_pad_0 = const()[name = tensor<string, []>("op_10390_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024076416))), lut = tensor<fp16, [4]>([-0x1.f0cp-7, -0x1.2bp-8, 0x1.2b8p-8, 0x1.f08p-7]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024486080)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10390_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_10388, groups = var_6865, pad = var_10390_pad_0, pad_type = var_10390_pad_type_0, strides = var_10386, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_603_cast)[name = tensor<string, []>("op_10390_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_317_cast = add(x = var_10390_cast, y = inputs_315_cast)[name = tensor<string, []>("inputs_317_cast")];
            tensor<int32, [1]> var_10394 = const()[name = tensor<string, []>("op_10394"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_317_cast = reduce_mean(axes = var_10394, keep_dims = var_6860, x = inputs_317_cast)[name = tensor<string, []>("channels_mean_317_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_317_cast = sub(x = inputs_317_cast, y = channels_mean_317_cast)[name = tensor<string, []>("zero_mean_317_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_317_cast = mul(x = zero_mean_317_cast, y = zero_mean_317_cast)[name = tensor<string, []>("zero_mean_sq_317_cast")];
            tensor<int32, [1]> var_10398 = const()[name = tensor<string, []>("op_10398"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10399_cast = reduce_mean(axes = var_10398, keep_dims = var_6860, x = zero_mean_sq_317_cast)[name = tensor<string, []>("op_10399_cast")];
            tensor<fp16, []> var_10400_to_fp16 = const()[name = tensor<string, []>("op_10400_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10401_cast = add(x = var_10399_cast, y = var_10400_to_fp16)[name = tensor<string, []>("op_10401_cast")];
            tensor<fp16, []> denom_317_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_317_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_317_cast = rsqrt(epsilon = denom_317_epsilon_0_to_fp16, x = var_10401_cast)[name = tensor<string, []>("denom_317_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_317_cast = mul(x = zero_mean_317_cast, y = denom_317_cast)[name = tensor<string, []>("out_317_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10405_to_fp16 = const()[name = tensor<string, []>("op_10405_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024488704)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10406_cast = add(x = out_317_cast, y = var_10405_to_fp16)[name = tensor<string, []>("op_10406_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10408_to_fp16 = const()[name = tensor<string, []>("op_10408_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024491328)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_605_cast = mul(x = var_10406_cast, y = var_10408_to_fp16)[name = tensor<string, []>("input_605_cast")];
            tensor<int32, [2]> var_10416 = const()[name = tensor<string, []>("op_10416"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10418 = const()[name = tensor<string, []>("op_10418"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10420_pad_type_0 = const()[name = tensor<string, []>("op_10420_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10420_pad_0 = const()[name = tensor<string, []>("op_10420_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024493952))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1034324416))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1034324608)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_10420_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_10418, groups = var_6865, pad = var_10420_pad_0, pad_type = var_10420_pad_type_0, strides = var_10416, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_605_cast)[name = tensor<string, []>("op_10420_cast")];
            tensor<int32, [2]> var_10421_split_sizes_0 = const()[name = tensor<string, []>("op_10421_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_10421_axis_0 = const()[name = tensor<string, []>("op_10421_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_10421_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_10421_cast_1 = split(axis = var_10421_axis_0, split_sizes = var_10421_split_sizes_0, x = var_10420_cast)[name = tensor<string, []>("op_10421_cast")];
            tensor<string, []> var_10423_mode_0 = const()[name = tensor<string, []>("op_10423_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_10423_cast = gelu(mode = var_10423_mode_0, x = var_10421_cast_1)[name = tensor<string, []>("op_10423_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_607_cast = mul(x = var_10421_cast_0, y = var_10423_cast)[name = tensor<string, []>("input_607_cast")];
            tensor<int32, [2]> var_10427 = const()[name = tensor<string, []>("op_10427"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10429 = const()[name = tensor<string, []>("op_10429"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10431_pad_type_0 = const()[name = tensor<string, []>("op_10431_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10431_pad_0 = const()[name = tensor<string, []>("op_10431_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1034345152))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039260416))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039260608)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10431_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_10429, groups = var_6865, pad = var_10431_pad_0, pad_type = var_10431_pad_type_0, strides = var_10427, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_607_cast)[name = tensor<string, []>("op_10431_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_319_cast = add(x = var_10431_cast, y = inputs_317_cast)[name = tensor<string, []>("inputs_319_cast")];
            tensor<int32, [1]> var_10441 = const()[name = tensor<string, []>("op_10441"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_319_cast = reduce_mean(axes = var_10441, keep_dims = var_6860, x = inputs_319_cast)[name = tensor<string, []>("channels_mean_319_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_319_cast = sub(x = inputs_319_cast, y = channels_mean_319_cast)[name = tensor<string, []>("zero_mean_319_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_319_cast = mul(x = zero_mean_319_cast, y = zero_mean_319_cast)[name = tensor<string, []>("zero_mean_sq_319_cast")];
            tensor<int32, [1]> var_10445 = const()[name = tensor<string, []>("op_10445"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10446_cast = reduce_mean(axes = var_10445, keep_dims = var_6860, x = zero_mean_sq_319_cast)[name = tensor<string, []>("op_10446_cast")];
            tensor<fp16, []> var_10447_to_fp16 = const()[name = tensor<string, []>("op_10447_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10448_cast = add(x = var_10446_cast, y = var_10447_to_fp16)[name = tensor<string, []>("op_10448_cast")];
            tensor<fp16, []> denom_319_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_319_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_319_cast = rsqrt(epsilon = denom_319_epsilon_0_to_fp16, x = var_10448_cast)[name = tensor<string, []>("denom_319_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_319_cast = mul(x = zero_mean_319_cast, y = denom_319_cast)[name = tensor<string, []>("out_319_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10452_to_fp16 = const()[name = tensor<string, []>("op_10452_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039263232)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10453_cast = add(x = out_319_cast, y = var_10452_to_fp16)[name = tensor<string, []>("op_10453_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10455_to_fp16 = const()[name = tensor<string, []>("op_10455_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039265856)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_415_cast = mul(x = var_10453_cast, y = var_10455_to_fp16)[name = tensor<string, []>("hidden_states_415_cast")];
            tensor<int32, [2]> var_10462 = const()[name = tensor<string, []>("op_10462"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10464 = const()[name = tensor<string, []>("op_10464"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_213_pad_type_0 = const()[name = tensor<string, []>("q_213_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_213_pad_0 = const()[name = tensor<string, []>("q_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039268480))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1040087744))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_213_cast = conv(dilations = var_10464, groups = var_6865, pad = q_213_pad_0, pad_type = q_213_pad_type_0, strides = var_10462, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_415_cast)[name = tensor<string, []>("q_213_cast")];
            tensor<int32, [2]> var_10468 = const()[name = tensor<string, []>("op_10468"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10470 = const()[name = tensor<string, []>("op_10470"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_213_pad_type_0 = const()[name = tensor<string, []>("k_213_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_213_pad_0 = const()[name = tensor<string, []>("k_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1040087872))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1040907136))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_213_cast = conv(dilations = var_10470, groups = var_6865, pad = k_213_pad_0, pad_type = k_213_pad_type_0, strides = var_10468, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_415_cast)[name = tensor<string, []>("k_213_cast")];
            tensor<int32, [2]> var_10474 = const()[name = tensor<string, []>("op_10474"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10476 = const()[name = tensor<string, []>("op_10476"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_213_pad_type_0 = const()[name = tensor<string, []>("v_213_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_213_pad_0 = const()[name = tensor<string, []>("v_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1040907264))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1042136128))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_213_cast = conv(dilations = var_10476, groups = var_6865, pad = v_213_pad_0, pad_type = v_213_pad_type_0, strides = var_10474, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_415_cast)[name = tensor<string, []>("v_213_cast")];
            tensor<int32, [4]> var_10480 = const()[name = tensor<string, []>("op_10480"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10481_cast = reshape(shape = var_10480, x = q_213_cast)[name = tensor<string, []>("op_10481_cast")];
            tensor<int32, [4]> var_10482 = const()[name = tensor<string, []>("op_10482"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10483_cast = reshape(shape = var_10482, x = k_213_cast)[name = tensor<string, []>("op_10483_cast")];
            tensor<int32, [4]> var_10484 = const()[name = tensor<string, []>("op_10484"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10485_cast = reshape(shape = var_10484, x = v_213_cast)[name = tensor<string, []>("op_10485_cast")];
            tensor<bool, []> attn_weights_425_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_425_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_425_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_425_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_425_cast = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_10481_cast, y = var_10483_cast)[name = tensor<string, []>("attn_weights_425_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_427_cast = mul(x = attn_weights_425_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_427_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_10489_cast = softmax(axis = var_6849, x = attn_weights_427_cast)[name = tensor<string, []>("op_10489_cast")];
            tensor<bool, []> attn_213_transpose_x_0 = const()[name = tensor<string, []>("attn_213_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_213_transpose_y_0 = const()[name = tensor<string, []>("attn_213_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_213_cast = matmul(transpose_x = attn_213_transpose_x_0, transpose_y = attn_213_transpose_y_0, x = var_10485_cast, y = var_10489_cast)[name = tensor<string, []>("attn_213_cast")];
            tensor<int32, [4]> var_10493 = const()[name = tensor<string, []>("op_10493"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_609_cast = reshape(shape = var_10493, x = attn_213_cast)[name = tensor<string, []>("input_609_cast")];
            tensor<int32, [2]> var_10498 = const()[name = tensor<string, []>("op_10498"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10500 = const()[name = tensor<string, []>("op_10500"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10502_pad_type_0 = const()[name = tensor<string, []>("op_10502_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10502_pad_0 = const()[name = tensor<string, []>("op_10502_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1042136320))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1043365184))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1043365376)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10502_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_10500, groups = var_6865, pad = var_10502_pad_0, pad_type = var_10502_pad_type_0, strides = var_10498, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_609_cast)[name = tensor<string, []>("op_10502_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_321_cast = add(x = var_10502_cast, y = inputs_319_cast)[name = tensor<string, []>("inputs_321_cast")];
            tensor<int32, [1]> var_10506 = const()[name = tensor<string, []>("op_10506"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_321_cast = reduce_mean(axes = var_10506, keep_dims = var_6860, x = inputs_321_cast)[name = tensor<string, []>("channels_mean_321_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_321_cast = sub(x = inputs_321_cast, y = channels_mean_321_cast)[name = tensor<string, []>("zero_mean_321_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_321_cast = mul(x = zero_mean_321_cast, y = zero_mean_321_cast)[name = tensor<string, []>("zero_mean_sq_321_cast")];
            tensor<int32, [1]> var_10510 = const()[name = tensor<string, []>("op_10510"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10511_cast = reduce_mean(axes = var_10510, keep_dims = var_6860, x = zero_mean_sq_321_cast)[name = tensor<string, []>("op_10511_cast")];
            tensor<fp16, []> var_10512_to_fp16 = const()[name = tensor<string, []>("op_10512_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10513_cast = add(x = var_10511_cast, y = var_10512_to_fp16)[name = tensor<string, []>("op_10513_cast")];
            tensor<fp16, []> denom_321_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_321_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_321_cast = rsqrt(epsilon = denom_321_epsilon_0_to_fp16, x = var_10513_cast)[name = tensor<string, []>("denom_321_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_321_cast = mul(x = zero_mean_321_cast, y = denom_321_cast)[name = tensor<string, []>("out_321_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10517_to_fp16 = const()[name = tensor<string, []>("op_10517_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1043368000)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10518_cast = add(x = out_321_cast, y = var_10517_to_fp16)[name = tensor<string, []>("op_10518_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10520_to_fp16 = const()[name = tensor<string, []>("op_10520_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1043370624)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_417_cast = mul(x = var_10518_cast, y = var_10520_to_fp16)[name = tensor<string, []>("hidden_states_417_cast")];
            tensor<int32, [2]> var_10527 = const()[name = tensor<string, []>("op_10527"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10529 = const()[name = tensor<string, []>("op_10529"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_215_pad_type_0 = const()[name = tensor<string, []>("q_215_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_215_pad_0 = const()[name = tensor<string, []>("q_215_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1043373248))), lut = tensor<fp16, [4]>([-0x1.8bcp-6, -0x1.eep-8, 0x1.ef4p-8, 0x1.8c4p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_215_cast = conv(dilations = var_10529, groups = var_6865, pad = q_215_pad_0, pad_type = q_215_pad_type_0, strides = var_10527, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_417_cast)[name = tensor<string, []>("q_215_cast")];
            tensor<int32, [2]> var_10533 = const()[name = tensor<string, []>("op_10533"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10535 = const()[name = tensor<string, []>("op_10535"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_215_pad_type_0 = const()[name = tensor<string, []>("k_215_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_215_pad_0 = const()[name = tensor<string, []>("k_215_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1043782912))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1045093696))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_215_cast = conv(dilations = var_10535, groups = var_6865, pad = k_215_pad_0, pad_type = k_215_pad_type_0, strides = var_10533, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_215_cast")];
            tensor<int32, [2]> var_10539 = const()[name = tensor<string, []>("op_10539"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10541 = const()[name = tensor<string, []>("op_10541"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_215_pad_type_0 = const()[name = tensor<string, []>("v_215_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_215_pad_0 = const()[name = tensor<string, []>("v_215_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1045093824))), lut = tensor<fp16, [4]>([-0x1.45cp-6, -0x1.8p-8, 0x1.7fp-8, 0x1.454p-6]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_215_cast = conv(dilations = var_10541, groups = var_6865, pad = v_215_pad_0, pad_type = v_215_pad_type_0, strides = var_10539, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_215_cast")];
            tensor<int32, [4]> var_10545 = const()[name = tensor<string, []>("op_10545"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10546_cast = reshape(shape = var_10545, x = q_215_cast)[name = tensor<string, []>("op_10546_cast")];
            tensor<int32, [4]> var_10547 = const()[name = tensor<string, []>("op_10547"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10548_cast = reshape(shape = var_10547, x = k_215_cast)[name = tensor<string, []>("op_10548_cast")];
            tensor<int32, [4]> var_10549 = const()[name = tensor<string, []>("op_10549"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10550_cast = reshape(shape = var_10549, x = v_215_cast)[name = tensor<string, []>("op_10550_cast")];
            tensor<bool, []> attn_weights_429_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_429_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_429_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_429_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_429_cast = matmul(transpose_x = attn_weights_429_transpose_x_0, transpose_y = attn_weights_429_transpose_y_0, x = var_10546_cast, y = var_10548_cast)[name = tensor<string, []>("attn_weights_429_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_431_cast = mul(x = attn_weights_429_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_431_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_10554_cast = softmax(axis = var_6849, x = attn_weights_431_cast)[name = tensor<string, []>("op_10554_cast")];
            tensor<bool, []> attn_215_transpose_x_0 = const()[name = tensor<string, []>("attn_215_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_215_transpose_y_0 = const()[name = tensor<string, []>("attn_215_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_215_cast = matmul(transpose_x = attn_215_transpose_x_0, transpose_y = attn_215_transpose_y_0, x = var_10550_cast, y = var_10554_cast)[name = tensor<string, []>("attn_215_cast")];
            tensor<int32, [4]> var_10558 = const()[name = tensor<string, []>("op_10558"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_611_cast = reshape(shape = var_10558, x = attn_215_cast)[name = tensor<string, []>("input_611_cast")];
            tensor<int32, [2]> var_10563 = const()[name = tensor<string, []>("op_10563"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10565 = const()[name = tensor<string, []>("op_10565"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10567_pad_type_0 = const()[name = tensor<string, []>("op_10567_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10567_pad_0 = const()[name = tensor<string, []>("op_10567_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1045749248))), lut = tensor<fp16, [4]>([-0x1.9fcp-7, -0x1.f98p-9, 0x1.f8cp-9, 0x1.ap-7]), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1046158912)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10567_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_10565, groups = var_6865, pad = var_10567_pad_0, pad_type = var_10567_pad_type_0, strides = var_10563, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_611_cast)[name = tensor<string, []>("op_10567_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_323_cast = add(x = var_10567_cast, y = inputs_321_cast)[name = tensor<string, []>("inputs_323_cast")];
            tensor<int32, [1]> var_10571 = const()[name = tensor<string, []>("op_10571"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_323_cast = reduce_mean(axes = var_10571, keep_dims = var_6860, x = inputs_323_cast)[name = tensor<string, []>("channels_mean_323_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_323_cast = sub(x = inputs_323_cast, y = channels_mean_323_cast)[name = tensor<string, []>("zero_mean_323_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_323_cast = mul(x = zero_mean_323_cast, y = zero_mean_323_cast)[name = tensor<string, []>("zero_mean_sq_323_cast")];
            tensor<int32, [1]> var_10575 = const()[name = tensor<string, []>("op_10575"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10576_cast = reduce_mean(axes = var_10575, keep_dims = var_6860, x = zero_mean_sq_323_cast)[name = tensor<string, []>("op_10576_cast")];
            tensor<fp16, []> var_10577_to_fp16 = const()[name = tensor<string, []>("op_10577_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10578_cast = add(x = var_10576_cast, y = var_10577_to_fp16)[name = tensor<string, []>("op_10578_cast")];
            tensor<fp16, []> denom_323_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_323_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_323_cast = rsqrt(epsilon = denom_323_epsilon_0_to_fp16, x = var_10578_cast)[name = tensor<string, []>("denom_323_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_323_cast = mul(x = zero_mean_323_cast, y = denom_323_cast)[name = tensor<string, []>("out_323_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10582_to_fp16 = const()[name = tensor<string, []>("op_10582_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1046161536)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10583_cast = add(x = out_323_cast, y = var_10582_to_fp16)[name = tensor<string, []>("op_10583_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10585_to_fp16 = const()[name = tensor<string, []>("op_10585_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1046164160)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_613_cast = mul(x = var_10583_cast, y = var_10585_to_fp16)[name = tensor<string, []>("input_613_cast")];
            tensor<int32, [2]> var_10593 = const()[name = tensor<string, []>("op_10593"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10595 = const()[name = tensor<string, []>("op_10595"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10597_pad_type_0 = const()[name = tensor<string, []>("op_10597_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10597_pad_0 = const()[name = tensor<string, []>("op_10597_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1046166784))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1055997248))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1055997440)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_10597_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_10595, groups = var_6865, pad = var_10597_pad_0, pad_type = var_10597_pad_type_0, strides = var_10593, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_613_cast)[name = tensor<string, []>("op_10597_cast")];
            tensor<int32, [2]> var_10598_split_sizes_0 = const()[name = tensor<string, []>("op_10598_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_10598_axis_0 = const()[name = tensor<string, []>("op_10598_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_10598_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_10598_cast_1 = split(axis = var_10598_axis_0, split_sizes = var_10598_split_sizes_0, x = var_10597_cast)[name = tensor<string, []>("op_10598_cast")];
            tensor<string, []> var_10600_mode_0 = const()[name = tensor<string, []>("op_10600_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_10600_cast = gelu(mode = var_10600_mode_0, x = var_10598_cast_1)[name = tensor<string, []>("op_10600_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_615_cast = mul(x = var_10598_cast_0, y = var_10600_cast)[name = tensor<string, []>("input_615_cast")];
            tensor<int32, [2]> var_10604 = const()[name = tensor<string, []>("op_10604"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10606 = const()[name = tensor<string, []>("op_10606"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10608_pad_type_0 = const()[name = tensor<string, []>("op_10608_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10608_pad_0 = const()[name = tensor<string, []>("op_10608_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1056017984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1060933248))), name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1060933440)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10608_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_10606, groups = var_6865, pad = var_10608_pad_0, pad_type = var_10608_pad_type_0, strides = var_10604, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_615_cast)[name = tensor<string, []>("op_10608_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_421_cast = add(x = var_10608_cast, y = inputs_323_cast)[name = tensor<string, []>("hidden_states_421_cast")];
            tensor<int32, [4]> var_10610 = const()[name = tensor<string, []>("op_10610"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> input_617_cast = reshape(shape = var_10610, x = hidden_states_421_cast)[name = tensor<string, []>("input_617_cast")];
            tensor<int32, [2]> var_10614 = const()[name = tensor<string, []>("op_10614"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10616 = const()[name = tensor<string, []>("op_10616"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_423_pad_type_0 = const()[name = tensor<string, []>("hidden_states_423_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_423_pad_0 = const()[name = tensor<string, []>("hidden_states_423_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1060936064))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062574528))), name = tensor<string, []>("up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062575104)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_423_cast = conv(bias = up_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_10616, groups = var_6865, pad = hidden_states_423_pad_0, pad_type = hidden_states_423_pad_type_0, strides = var_10614, weight = up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized, x = input_617_cast)[name = tensor<string, []>("hidden_states_423_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_425_cast = add(x = hidden_states_423_cast, y = hidden_states_357_cast)[name = tensor<string, []>("hidden_states_425_cast")];
            tensor<bool, []> input_619_interleave_0 = const()[name = tensor<string, []>("input_619_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 1920, 32, 32]> input_619_cast = concat(axis = var_6865, interleave = input_619_interleave_0, values = (hidden_states_425_cast, input_115_cast))[name = tensor<string, []>("input_619_cast")];
            tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([2, 32, 60, 32, 32])];
            tensor<fp16, [2, 32, 60, 32, 32]> reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_619_cast)[name = tensor<string, []>("reshape_108_cast")];
            tensor<int32, [3]> reduce_mean_81_axes_0 = const()[name = tensor<string, []>("reduce_mean_81_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_81_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_81_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_81_cast = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast)[name = tensor<string, []>("reduce_mean_81_cast")];
            tensor<fp16, [2, 32, 60, 32, 32]> sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor<string, []>("sub_54_cast")];
            tensor<fp16, [2, 32, 60, 32, 32]> square_27_cast = square(x = sub_54_cast)[name = tensor<string, []>("square_27_cast")];
            tensor<int32, [3]> reduce_mean_83_axes_0 = const()[name = tensor<string, []>("reduce_mean_83_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_83_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_83_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_83_cast = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast)[name = tensor<string, []>("reduce_mean_83_cast")];
            tensor<fp16, []> add_54_y_0_to_fp16 = const()[name = tensor<string, []>("add_54_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_54_cast = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16)[name = tensor<string, []>("add_54_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_27_cast = sqrt(x = add_54_cast)[name = tensor<string, []>("sqrt_27_cast")];
            tensor<fp16, [2, 32, 60, 32, 32]> real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast)[name = tensor<string, []>("real_div_27_cast")];
            tensor<int32, [4]> reshape_109_shape_0 = const()[name = tensor<string, []>("reshape_109_shape_0"), val = tensor<int32, [4]>([2, 1920, 32, 32])];
            tensor<fp16, [2, 1920, 32, 32]> reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor<string, []>("reshape_109_cast")];
            tensor<fp16, [1920]> add_55_mean_0_to_fp16 = const()[name = tensor<string, []>("add_55_mean_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062577728)))];
            tensor<fp16, [1920]> add_55_variance_0_to_fp16 = const()[name = tensor<string, []>("add_55_variance_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062581632)))];
            tensor<fp16, [1920]> add_55_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_55_gamma_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062585536)))];
            tensor<fp16, [1920]> add_55_beta_0_to_fp16 = const()[name = tensor<string, []>("add_55_beta_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062589440)))];
            tensor<fp16, []> add_55_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_55_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1920, 32, 32]> add_55_cast = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast)[name = tensor<string, []>("add_55_cast")];
            tensor<fp16, [2, 1920, 32, 32]> input_623_cast = silu(x = add_55_cast)[name = tensor<string, []>("input_623_cast")];
            tensor<int32, [2]> var_10634 = const()[name = tensor<string, []>("op_10634"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10636 = const()[name = tensor<string, []>("op_10636"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_427_pad_type_0 = const()[name = tensor<string, []>("hidden_states_427_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_427_pad_0 = const()[name = tensor<string, []>("hidden_states_427_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1920, 3, 3]> up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [16588800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062593344))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1079182208))), name = tensor<string, []>("up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1920, 3, 3])];
            tensor<fp16, [1280]> up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1079182400)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_427_cast = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_10636, groups = var_6865, pad = hidden_states_427_pad_0, pad_type = hidden_states_427_pad_type_0, strides = var_10634, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized, x = input_623_cast)[name = tensor<string, []>("hidden_states_427_cast")];
            tensor<int32, [2]> var_10642 = const()[name = tensor<string, []>("op_10642"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10644 = const()[name = tensor<string, []>("op_10644"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_21_pad_type_0 = const()[name = tensor<string, []>("temb_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_21_pad_0 = const()[name = tensor<string, []>("temb_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1079185024))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1080413888))), name = tensor<string, []>("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1080414080)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_21_cast = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_10644, groups = var_6865, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_10642, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_21_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_627_cast = add(x = hidden_states_427_cast, y = temb_21_cast)[name = tensor<string, []>("input_627_cast")];
            tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_627_cast)[name = tensor<string, []>("reshape_112_cast")];
            tensor<int32, [3]> reduce_mean_84_axes_0 = const()[name = tensor<string, []>("reduce_mean_84_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_84_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_84_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_84_cast = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast)[name = tensor<string, []>("reduce_mean_84_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor<string, []>("sub_56_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_28_cast = square(x = sub_56_cast)[name = tensor<string, []>("square_28_cast")];
            tensor<int32, [3]> reduce_mean_86_axes_0 = const()[name = tensor<string, []>("reduce_mean_86_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_86_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_86_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_86_cast = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast)[name = tensor<string, []>("reduce_mean_86_cast")];
            tensor<fp16, []> add_56_y_0_to_fp16 = const()[name = tensor<string, []>("add_56_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_56_cast = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16)[name = tensor<string, []>("add_56_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_28_cast = sqrt(x = add_56_cast)[name = tensor<string, []>("sqrt_28_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast)[name = tensor<string, []>("real_div_28_cast")];
            tensor<int32, [4]> reshape_113_shape_0 = const()[name = tensor<string, []>("reshape_113_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast)[name = tensor<string, []>("reshape_113_cast")];
            tensor<fp16, [1280]> add_57_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_57_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1080416704)))];
            tensor<fp16, [1280]> add_57_beta_0_to_fp16 = const()[name = tensor<string, []>("add_57_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1080419328)))];
            tensor<fp16, []> add_57_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_57_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_57_cast = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_113_cast)[name = tensor<string, []>("add_57_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_631_cast = silu(x = add_57_cast)[name = tensor<string, []>("input_631_cast")];
            tensor<int32, [2]> var_10654 = const()[name = tensor<string, []>("op_10654"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10656 = const()[name = tensor<string, []>("op_10656"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_429_pad_type_0 = const()[name = tensor<string, []>("hidden_states_429_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_429_pad_0 = const()[name = tensor<string, []>("hidden_states_429_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1080421952))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091481216))), name = tensor<string, []>("up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091481408)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_429_cast = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_10656, groups = var_6865, pad = hidden_states_429_pad_0, pad_type = hidden_states_429_pad_type_0, strides = var_10654, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized, x = input_631_cast)[name = tensor<string, []>("hidden_states_429_cast")];
            tensor<int32, [2]> var_10661 = const()[name = tensor<string, []>("op_10661"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10663 = const()[name = tensor<string, []>("op_10663"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_9_pad_type_0 = const()[name = tensor<string, []>("x_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_9_pad_0 = const()[name = tensor<string, []>("x_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1920, 1, 1]> up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091484032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1093941696))), name = tensor<string, []>("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1920, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1093942272)))];
            tensor<fp16, [2, 1280, 32, 32]> x_9_cast = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_10663, groups = var_6865, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_10661, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_619_cast)[name = tensor<string, []>("x_9_cast")];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_431_cast = add(x = x_9_cast, y = hidden_states_429_cast)[name = tensor<string, []>("hidden_states_431_cast")];
            tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_116_cast = reshape(shape = reshape_116_shape_0, x = hidden_states_431_cast)[name = tensor<string, []>("reshape_116_cast")];
            tensor<int32, [3]> reduce_mean_87_axes_0 = const()[name = tensor<string, []>("reduce_mean_87_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_87_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_87_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_87_cast = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast)[name = tensor<string, []>("reduce_mean_87_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor<string, []>("sub_58_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> square_29_cast = square(x = sub_58_cast)[name = tensor<string, []>("square_29_cast")];
            tensor<int32, [3]> reduce_mean_89_axes_0 = const()[name = tensor<string, []>("reduce_mean_89_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_89_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_89_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_89_cast = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast)[name = tensor<string, []>("reduce_mean_89_cast")];
            tensor<fp16, []> add_58_y_0_to_fp16 = const()[name = tensor<string, []>("add_58_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_58_cast = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16)[name = tensor<string, []>("add_58_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_29_cast = sqrt(x = add_58_cast)[name = tensor<string, []>("sqrt_29_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast)[name = tensor<string, []>("real_div_29_cast")];
            tensor<int32, [4]> reshape_117_shape_0 = const()[name = tensor<string, []>("reshape_117_shape_0"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor<string, []>("reshape_117_cast")];
            tensor<fp16, [1280]> add_59_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_59_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1093944896)))];
            tensor<fp16, [1280]> add_59_beta_0_to_fp16 = const()[name = tensor<string, []>("add_59_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1093947520)))];
            tensor<fp16, []> add_59_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_59_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 32, 32]> add_59_cast = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_117_cast)[name = tensor<string, []>("add_59_cast")];
            tensor<int32, [2]> var_10701 = const()[name = tensor<string, []>("op_10701"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10703 = const()[name = tensor<string, []>("op_10703"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_433_pad_type_0 = const()[name = tensor<string, []>("hidden_states_433_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_433_pad_0 = const()[name = tensor<string, []>("hidden_states_433_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1093950144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1095179008))), name = tensor<string, []>("up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1095179200)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_433_cast = conv(bias = up_blocks_0_attentions_2_proj_in_bias_to_fp16, dilations = var_10703, groups = var_6865, pad = hidden_states_433_pad_0, pad_type = hidden_states_433_pad_type_0, strides = var_10701, weight = up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized, x = add_59_cast)[name = tensor<string, []>("hidden_states_433_cast")];
            tensor<int32, [4]> var_10708 = const()[name = tensor<string, []>("op_10708"), val = tensor<int32, [4]>([2, 1280, 1, 1024])];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_325_cast = reshape(shape = var_10708, x = hidden_states_433_cast)[name = tensor<string, []>("inputs_325_cast")];
            tensor<int32, [1]> var_10718 = const()[name = tensor<string, []>("op_10718"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_325_cast = reduce_mean(axes = var_10718, keep_dims = var_6860, x = inputs_325_cast)[name = tensor<string, []>("channels_mean_325_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_325_cast = sub(x = inputs_325_cast, y = channels_mean_325_cast)[name = tensor<string, []>("zero_mean_325_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_325_cast = mul(x = zero_mean_325_cast, y = zero_mean_325_cast)[name = tensor<string, []>("zero_mean_sq_325_cast")];
            tensor<int32, [1]> var_10722 = const()[name = tensor<string, []>("op_10722"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10723_cast = reduce_mean(axes = var_10722, keep_dims = var_6860, x = zero_mean_sq_325_cast)[name = tensor<string, []>("op_10723_cast")];
            tensor<fp16, []> var_10724_to_fp16 = const()[name = tensor<string, []>("op_10724_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10725_cast = add(x = var_10723_cast, y = var_10724_to_fp16)[name = tensor<string, []>("op_10725_cast")];
            tensor<fp16, []> denom_325_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_325_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_325_cast = rsqrt(epsilon = denom_325_epsilon_0_to_fp16, x = var_10725_cast)[name = tensor<string, []>("denom_325_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_325_cast = mul(x = zero_mean_325_cast, y = denom_325_cast)[name = tensor<string, []>("out_325_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10729_to_fp16 = const()[name = tensor<string, []>("op_10729_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1095181824)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10730_cast = add(x = out_325_cast, y = var_10729_to_fp16)[name = tensor<string, []>("op_10730_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10732_to_fp16 = const()[name = tensor<string, []>("op_10732_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1095184448)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_435_cast = mul(x = var_10730_cast, y = var_10732_to_fp16)[name = tensor<string, []>("hidden_states_435_cast")];
            tensor<int32, [2]> var_10739 = const()[name = tensor<string, []>("op_10739"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10741 = const()[name = tensor<string, []>("op_10741"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_217_pad_type_0 = const()[name = tensor<string, []>("q_217_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_217_pad_0 = const()[name = tensor<string, []>("q_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1095187072))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1096006336))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_217_cast = conv(dilations = var_10741, groups = var_6865, pad = q_217_pad_0, pad_type = q_217_pad_type_0, strides = var_10739, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_435_cast)[name = tensor<string, []>("q_217_cast")];
            tensor<int32, [2]> var_10745 = const()[name = tensor<string, []>("op_10745"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10747 = const()[name = tensor<string, []>("op_10747"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_217_pad_type_0 = const()[name = tensor<string, []>("k_217_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_217_pad_0 = const()[name = tensor<string, []>("k_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1096006464))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1096825728))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_217_cast = conv(dilations = var_10747, groups = var_6865, pad = k_217_pad_0, pad_type = k_217_pad_type_0, strides = var_10745, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_435_cast)[name = tensor<string, []>("k_217_cast")];
            tensor<int32, [2]> var_10751 = const()[name = tensor<string, []>("op_10751"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10753 = const()[name = tensor<string, []>("op_10753"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_217_pad_type_0 = const()[name = tensor<string, []>("v_217_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_217_pad_0 = const()[name = tensor<string, []>("v_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1096825856))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1098054720))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_217_cast = conv(dilations = var_10753, groups = var_6865, pad = v_217_pad_0, pad_type = v_217_pad_type_0, strides = var_10751, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_435_cast)[name = tensor<string, []>("v_217_cast")];
            tensor<int32, [4]> var_10757 = const()[name = tensor<string, []>("op_10757"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10758_cast = reshape(shape = var_10757, x = q_217_cast)[name = tensor<string, []>("op_10758_cast")];
            tensor<int32, [4]> var_10759 = const()[name = tensor<string, []>("op_10759"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10760_cast = reshape(shape = var_10759, x = k_217_cast)[name = tensor<string, []>("op_10760_cast")];
            tensor<int32, [4]> var_10761 = const()[name = tensor<string, []>("op_10761"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10762_cast = reshape(shape = var_10761, x = v_217_cast)[name = tensor<string, []>("op_10762_cast")];
            tensor<bool, []> attn_weights_433_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_433_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_433_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_433_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_433_cast = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_10758_cast, y = var_10760_cast)[name = tensor<string, []>("attn_weights_433_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_435_cast = mul(x = attn_weights_433_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_435_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_10766_cast = softmax(axis = var_6849, x = attn_weights_435_cast)[name = tensor<string, []>("op_10766_cast")];
            tensor<bool, []> attn_217_transpose_x_0 = const()[name = tensor<string, []>("attn_217_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_217_transpose_y_0 = const()[name = tensor<string, []>("attn_217_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_217_cast = matmul(transpose_x = attn_217_transpose_x_0, transpose_y = attn_217_transpose_y_0, x = var_10762_cast, y = var_10766_cast)[name = tensor<string, []>("attn_217_cast")];
            tensor<int32, [4]> var_10770 = const()[name = tensor<string, []>("op_10770"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_635_cast = reshape(shape = var_10770, x = attn_217_cast)[name = tensor<string, []>("input_635_cast")];
            tensor<int32, [2]> var_10775 = const()[name = tensor<string, []>("op_10775"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10777 = const()[name = tensor<string, []>("op_10777"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10779_pad_type_0 = const()[name = tensor<string, []>("op_10779_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10779_pad_0 = const()[name = tensor<string, []>("op_10779_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1098054912))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1099283776))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1099283968)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10779_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_10777, groups = var_6865, pad = var_10779_pad_0, pad_type = var_10779_pad_type_0, strides = var_10775, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_635_cast)[name = tensor<string, []>("op_10779_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_327_cast = add(x = var_10779_cast, y = inputs_325_cast)[name = tensor<string, []>("inputs_327_cast")];
            tensor<int32, [1]> var_10783 = const()[name = tensor<string, []>("op_10783"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_327_cast = reduce_mean(axes = var_10783, keep_dims = var_6860, x = inputs_327_cast)[name = tensor<string, []>("channels_mean_327_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_327_cast = sub(x = inputs_327_cast, y = channels_mean_327_cast)[name = tensor<string, []>("zero_mean_327_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_327_cast = mul(x = zero_mean_327_cast, y = zero_mean_327_cast)[name = tensor<string, []>("zero_mean_sq_327_cast")];
            tensor<int32, [1]> var_10787 = const()[name = tensor<string, []>("op_10787"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10788_cast = reduce_mean(axes = var_10787, keep_dims = var_6860, x = zero_mean_sq_327_cast)[name = tensor<string, []>("op_10788_cast")];
            tensor<fp16, []> var_10789_to_fp16 = const()[name = tensor<string, []>("op_10789_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10790_cast = add(x = var_10788_cast, y = var_10789_to_fp16)[name = tensor<string, []>("op_10790_cast")];
            tensor<fp16, []> denom_327_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_327_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_327_cast = rsqrt(epsilon = denom_327_epsilon_0_to_fp16, x = var_10790_cast)[name = tensor<string, []>("denom_327_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_327_cast = mul(x = zero_mean_327_cast, y = denom_327_cast)[name = tensor<string, []>("out_327_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10794_to_fp16 = const()[name = tensor<string, []>("op_10794_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1099286592)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10795_cast = add(x = out_327_cast, y = var_10794_to_fp16)[name = tensor<string, []>("op_10795_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10797_to_fp16 = const()[name = tensor<string, []>("op_10797_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1099289216)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_437_cast = mul(x = var_10795_cast, y = var_10797_to_fp16)[name = tensor<string, []>("hidden_states_437_cast")];
            tensor<int32, [2]> var_10804 = const()[name = tensor<string, []>("op_10804"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10806 = const()[name = tensor<string, []>("op_10806"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_219_pad_type_0 = const()[name = tensor<string, []>("q_219_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_219_pad_0 = const()[name = tensor<string, []>("q_219_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1099291840))), lut = tensor<fp16, [4]>([-0x1.c78p-6, -0x1.154p-7, 0x1.148p-7, 0x1.c7p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_219_cast = conv(dilations = var_10806, groups = var_6865, pad = q_219_pad_0, pad_type = q_219_pad_type_0, strides = var_10804, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_437_cast)[name = tensor<string, []>("q_219_cast")];
            tensor<int32, [2]> var_10810 = const()[name = tensor<string, []>("op_10810"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10812 = const()[name = tensor<string, []>("op_10812"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_219_pad_type_0 = const()[name = tensor<string, []>("k_219_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_219_pad_0 = const()[name = tensor<string, []>("k_219_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1099701504))), lut = tensor<fp16, [4]>([-0x1.a8p-6, -0x1.ef8p-8, 0x1.f2cp-8, 0x1.a9p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_219_cast = conv(dilations = var_10812, groups = var_6865, pad = k_219_pad_0, pad_type = k_219_pad_type_0, strides = var_10810, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_219_cast")];
            tensor<int32, [2]> var_10816 = const()[name = tensor<string, []>("op_10816"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10818 = const()[name = tensor<string, []>("op_10818"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_219_pad_type_0 = const()[name = tensor<string, []>("v_219_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_219_pad_0 = const()[name = tensor<string, []>("v_219_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1100356928))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1101667712))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_219_cast = conv(dilations = var_10818, groups = var_6865, pad = v_219_pad_0, pad_type = v_219_pad_type_0, strides = var_10816, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_219_cast")];
            tensor<int32, [4]> var_10822 = const()[name = tensor<string, []>("op_10822"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10823_cast = reshape(shape = var_10822, x = q_219_cast)[name = tensor<string, []>("op_10823_cast")];
            tensor<int32, [4]> var_10824 = const()[name = tensor<string, []>("op_10824"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10825_cast = reshape(shape = var_10824, x = k_219_cast)[name = tensor<string, []>("op_10825_cast")];
            tensor<int32, [4]> var_10826 = const()[name = tensor<string, []>("op_10826"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_10827_cast = reshape(shape = var_10826, x = v_219_cast)[name = tensor<string, []>("op_10827_cast")];
            tensor<bool, []> attn_weights_437_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_437_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_437_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_437_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_437_cast = matmul(transpose_x = attn_weights_437_transpose_x_0, transpose_y = attn_weights_437_transpose_y_0, x = var_10823_cast, y = var_10825_cast)[name = tensor<string, []>("attn_weights_437_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_439_cast = mul(x = attn_weights_437_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_439_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_10831_cast = softmax(axis = var_6849, x = attn_weights_439_cast)[name = tensor<string, []>("op_10831_cast")];
            tensor<bool, []> attn_219_transpose_x_0 = const()[name = tensor<string, []>("attn_219_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_219_transpose_y_0 = const()[name = tensor<string, []>("attn_219_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_219_cast = matmul(transpose_x = attn_219_transpose_x_0, transpose_y = attn_219_transpose_y_0, x = var_10827_cast, y = var_10831_cast)[name = tensor<string, []>("attn_219_cast")];
            tensor<int32, [4]> var_10835 = const()[name = tensor<string, []>("op_10835"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_637_cast = reshape(shape = var_10835, x = attn_219_cast)[name = tensor<string, []>("input_637_cast")];
            tensor<int32, [2]> var_10840 = const()[name = tensor<string, []>("op_10840"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10842 = const()[name = tensor<string, []>("op_10842"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10844_pad_type_0 = const()[name = tensor<string, []>("op_10844_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10844_pad_0 = const()[name = tensor<string, []>("op_10844_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1101667840))), lut = tensor<fp16, [4]>([-0x1.48cp-7, -0x1.828p-9, 0x1.83cp-9, 0x1.49cp-7]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102077504)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10844_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_10842, groups = var_6865, pad = var_10844_pad_0, pad_type = var_10844_pad_type_0, strides = var_10840, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_637_cast)[name = tensor<string, []>("op_10844_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_329_cast = add(x = var_10844_cast, y = inputs_327_cast)[name = tensor<string, []>("inputs_329_cast")];
            tensor<int32, [1]> var_10848 = const()[name = tensor<string, []>("op_10848"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_329_cast = reduce_mean(axes = var_10848, keep_dims = var_6860, x = inputs_329_cast)[name = tensor<string, []>("channels_mean_329_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_329_cast = sub(x = inputs_329_cast, y = channels_mean_329_cast)[name = tensor<string, []>("zero_mean_329_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_329_cast = mul(x = zero_mean_329_cast, y = zero_mean_329_cast)[name = tensor<string, []>("zero_mean_sq_329_cast")];
            tensor<int32, [1]> var_10852 = const()[name = tensor<string, []>("op_10852"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10853_cast = reduce_mean(axes = var_10852, keep_dims = var_6860, x = zero_mean_sq_329_cast)[name = tensor<string, []>("op_10853_cast")];
            tensor<fp16, []> var_10854_to_fp16 = const()[name = tensor<string, []>("op_10854_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10855_cast = add(x = var_10853_cast, y = var_10854_to_fp16)[name = tensor<string, []>("op_10855_cast")];
            tensor<fp16, []> denom_329_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_329_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_329_cast = rsqrt(epsilon = denom_329_epsilon_0_to_fp16, x = var_10855_cast)[name = tensor<string, []>("denom_329_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_329_cast = mul(x = zero_mean_329_cast, y = denom_329_cast)[name = tensor<string, []>("out_329_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10859_to_fp16 = const()[name = tensor<string, []>("op_10859_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102080128)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10860_cast = add(x = out_329_cast, y = var_10859_to_fp16)[name = tensor<string, []>("op_10860_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10862_to_fp16 = const()[name = tensor<string, []>("op_10862_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102082752)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_639_cast = mul(x = var_10860_cast, y = var_10862_to_fp16)[name = tensor<string, []>("input_639_cast")];
            tensor<int32, [2]> var_10870 = const()[name = tensor<string, []>("op_10870"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10872 = const()[name = tensor<string, []>("op_10872"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10874_pad_type_0 = const()[name = tensor<string, []>("op_10874_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10874_pad_0 = const()[name = tensor<string, []>("op_10874_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102085376))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1111915840))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1111916032)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_10874_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_10872, groups = var_6865, pad = var_10874_pad_0, pad_type = var_10874_pad_type_0, strides = var_10870, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_639_cast)[name = tensor<string, []>("op_10874_cast")];
            tensor<int32, [2]> var_10875_split_sizes_0 = const()[name = tensor<string, []>("op_10875_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_10875_axis_0 = const()[name = tensor<string, []>("op_10875_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_10875_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_10875_cast_1 = split(axis = var_10875_axis_0, split_sizes = var_10875_split_sizes_0, x = var_10874_cast)[name = tensor<string, []>("op_10875_cast")];
            tensor<string, []> var_10877_mode_0 = const()[name = tensor<string, []>("op_10877_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_10877_cast = gelu(mode = var_10877_mode_0, x = var_10875_cast_1)[name = tensor<string, []>("op_10877_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_641_cast = mul(x = var_10875_cast_0, y = var_10877_cast)[name = tensor<string, []>("input_641_cast")];
            tensor<int32, [2]> var_10881 = const()[name = tensor<string, []>("op_10881"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10883 = const()[name = tensor<string, []>("op_10883"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10885_pad_type_0 = const()[name = tensor<string, []>("op_10885_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10885_pad_0 = const()[name = tensor<string, []>("op_10885_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1111936576))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1116851840))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1116852032)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10885_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_10883, groups = var_6865, pad = var_10885_pad_0, pad_type = var_10885_pad_type_0, strides = var_10881, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_641_cast)[name = tensor<string, []>("op_10885_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_331_cast = add(x = var_10885_cast, y = inputs_329_cast)[name = tensor<string, []>("inputs_331_cast")];
            tensor<int32, [1]> var_10895 = const()[name = tensor<string, []>("op_10895"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_331_cast = reduce_mean(axes = var_10895, keep_dims = var_6860, x = inputs_331_cast)[name = tensor<string, []>("channels_mean_331_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_331_cast = sub(x = inputs_331_cast, y = channels_mean_331_cast)[name = tensor<string, []>("zero_mean_331_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_331_cast = mul(x = zero_mean_331_cast, y = zero_mean_331_cast)[name = tensor<string, []>("zero_mean_sq_331_cast")];
            tensor<int32, [1]> var_10899 = const()[name = tensor<string, []>("op_10899"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10900_cast = reduce_mean(axes = var_10899, keep_dims = var_6860, x = zero_mean_sq_331_cast)[name = tensor<string, []>("op_10900_cast")];
            tensor<fp16, []> var_10901_to_fp16 = const()[name = tensor<string, []>("op_10901_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10902_cast = add(x = var_10900_cast, y = var_10901_to_fp16)[name = tensor<string, []>("op_10902_cast")];
            tensor<fp16, []> denom_331_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_331_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_331_cast = rsqrt(epsilon = denom_331_epsilon_0_to_fp16, x = var_10902_cast)[name = tensor<string, []>("denom_331_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_331_cast = mul(x = zero_mean_331_cast, y = denom_331_cast)[name = tensor<string, []>("out_331_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10906_to_fp16 = const()[name = tensor<string, []>("op_10906_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1116854656)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10907_cast = add(x = out_331_cast, y = var_10906_to_fp16)[name = tensor<string, []>("op_10907_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10909_to_fp16 = const()[name = tensor<string, []>("op_10909_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1116857280)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_441_cast = mul(x = var_10907_cast, y = var_10909_to_fp16)[name = tensor<string, []>("hidden_states_441_cast")];
            tensor<int32, [2]> var_10916 = const()[name = tensor<string, []>("op_10916"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10918 = const()[name = tensor<string, []>("op_10918"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_221_pad_type_0 = const()[name = tensor<string, []>("q_221_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_221_pad_0 = const()[name = tensor<string, []>("q_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1116859904))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117679168))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_221_cast = conv(dilations = var_10918, groups = var_6865, pad = q_221_pad_0, pad_type = q_221_pad_type_0, strides = var_10916, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_441_cast)[name = tensor<string, []>("q_221_cast")];
            tensor<int32, [2]> var_10922 = const()[name = tensor<string, []>("op_10922"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10924 = const()[name = tensor<string, []>("op_10924"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_221_pad_type_0 = const()[name = tensor<string, []>("k_221_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_221_pad_0 = const()[name = tensor<string, []>("k_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117679296))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1118498560))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_221_cast = conv(dilations = var_10924, groups = var_6865, pad = k_221_pad_0, pad_type = k_221_pad_type_0, strides = var_10922, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_441_cast)[name = tensor<string, []>("k_221_cast")];
            tensor<int32, [2]> var_10928 = const()[name = tensor<string, []>("op_10928"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10930 = const()[name = tensor<string, []>("op_10930"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_221_pad_type_0 = const()[name = tensor<string, []>("v_221_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_221_pad_0 = const()[name = tensor<string, []>("v_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1118498688))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1119727552))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_221_cast = conv(dilations = var_10930, groups = var_6865, pad = v_221_pad_0, pad_type = v_221_pad_type_0, strides = var_10928, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_441_cast)[name = tensor<string, []>("v_221_cast")];
            tensor<int32, [4]> var_10934 = const()[name = tensor<string, []>("op_10934"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10935_cast = reshape(shape = var_10934, x = q_221_cast)[name = tensor<string, []>("op_10935_cast")];
            tensor<int32, [4]> var_10936 = const()[name = tensor<string, []>("op_10936"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10937_cast = reshape(shape = var_10936, x = k_221_cast)[name = tensor<string, []>("op_10937_cast")];
            tensor<int32, [4]> var_10938 = const()[name = tensor<string, []>("op_10938"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_10939_cast = reshape(shape = var_10938, x = v_221_cast)[name = tensor<string, []>("op_10939_cast")];
            tensor<bool, []> attn_weights_441_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_441_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_441_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_441_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_441_cast = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_10935_cast, y = var_10937_cast)[name = tensor<string, []>("attn_weights_441_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_443_cast = mul(x = attn_weights_441_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_443_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_10943_cast = softmax(axis = var_6849, x = attn_weights_443_cast)[name = tensor<string, []>("op_10943_cast")];
            tensor<bool, []> attn_221_transpose_x_0 = const()[name = tensor<string, []>("attn_221_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_221_transpose_y_0 = const()[name = tensor<string, []>("attn_221_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_221_cast = matmul(transpose_x = attn_221_transpose_x_0, transpose_y = attn_221_transpose_y_0, x = var_10939_cast, y = var_10943_cast)[name = tensor<string, []>("attn_221_cast")];
            tensor<int32, [4]> var_10947 = const()[name = tensor<string, []>("op_10947"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_643_cast = reshape(shape = var_10947, x = attn_221_cast)[name = tensor<string, []>("input_643_cast")];
            tensor<int32, [2]> var_10952 = const()[name = tensor<string, []>("op_10952"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10954 = const()[name = tensor<string, []>("op_10954"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_10956_pad_type_0 = const()[name = tensor<string, []>("op_10956_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_10956_pad_0 = const()[name = tensor<string, []>("op_10956_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1119727744))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1120956608))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1120956800)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10956_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_10954, groups = var_6865, pad = var_10956_pad_0, pad_type = var_10956_pad_type_0, strides = var_10952, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_643_cast)[name = tensor<string, []>("op_10956_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_333_cast = add(x = var_10956_cast, y = inputs_331_cast)[name = tensor<string, []>("inputs_333_cast")];
            tensor<int32, [1]> var_10960 = const()[name = tensor<string, []>("op_10960"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_333_cast = reduce_mean(axes = var_10960, keep_dims = var_6860, x = inputs_333_cast)[name = tensor<string, []>("channels_mean_333_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_333_cast = sub(x = inputs_333_cast, y = channels_mean_333_cast)[name = tensor<string, []>("zero_mean_333_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_333_cast = mul(x = zero_mean_333_cast, y = zero_mean_333_cast)[name = tensor<string, []>("zero_mean_sq_333_cast")];
            tensor<int32, [1]> var_10964 = const()[name = tensor<string, []>("op_10964"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_10965_cast = reduce_mean(axes = var_10964, keep_dims = var_6860, x = zero_mean_sq_333_cast)[name = tensor<string, []>("op_10965_cast")];
            tensor<fp16, []> var_10966_to_fp16 = const()[name = tensor<string, []>("op_10966_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_10967_cast = add(x = var_10965_cast, y = var_10966_to_fp16)[name = tensor<string, []>("op_10967_cast")];
            tensor<fp16, []> denom_333_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_333_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_333_cast = rsqrt(epsilon = denom_333_epsilon_0_to_fp16, x = var_10967_cast)[name = tensor<string, []>("denom_333_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_333_cast = mul(x = zero_mean_333_cast, y = denom_333_cast)[name = tensor<string, []>("out_333_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10971_to_fp16 = const()[name = tensor<string, []>("op_10971_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1120959424)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_10972_cast = add(x = out_333_cast, y = var_10971_to_fp16)[name = tensor<string, []>("op_10972_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_10974_to_fp16 = const()[name = tensor<string, []>("op_10974_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1120962048)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_443_cast = mul(x = var_10972_cast, y = var_10974_to_fp16)[name = tensor<string, []>("hidden_states_443_cast")];
            tensor<int32, [2]> var_10981 = const()[name = tensor<string, []>("op_10981"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10983 = const()[name = tensor<string, []>("op_10983"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_223_pad_type_0 = const()[name = tensor<string, []>("q_223_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_223_pad_0 = const()[name = tensor<string, []>("q_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1120964672))), lut = tensor<fp16, [4]>([-0x1.304p-5, -0x1.668p-7, 0x1.624p-7, 0x1.2f8p-5]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_223_cast = conv(dilations = var_10983, groups = var_6865, pad = q_223_pad_0, pad_type = q_223_pad_type_0, strides = var_10981, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_443_cast)[name = tensor<string, []>("q_223_cast")];
            tensor<int32, [2]> var_10987 = const()[name = tensor<string, []>("op_10987"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10989 = const()[name = tensor<string, []>("op_10989"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_223_pad_type_0 = const()[name = tensor<string, []>("k_223_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_223_pad_0 = const()[name = tensor<string, []>("k_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1121374336))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1122685120))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_223_cast = conv(dilations = var_10989, groups = var_6865, pad = k_223_pad_0, pad_type = k_223_pad_type_0, strides = var_10987, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_223_cast")];
            tensor<int32, [2]> var_10993 = const()[name = tensor<string, []>("op_10993"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_10995 = const()[name = tensor<string, []>("op_10995"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_223_pad_type_0 = const()[name = tensor<string, []>("v_223_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_223_pad_0 = const()[name = tensor<string, []>("v_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1122685248))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1123996032))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_223_cast = conv(dilations = var_10995, groups = var_6865, pad = v_223_pad_0, pad_type = v_223_pad_type_0, strides = var_10993, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_223_cast")];
            tensor<int32, [4]> var_10999 = const()[name = tensor<string, []>("op_10999"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11000_cast = reshape(shape = var_10999, x = q_223_cast)[name = tensor<string, []>("op_11000_cast")];
            tensor<int32, [4]> var_11001 = const()[name = tensor<string, []>("op_11001"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11002_cast = reshape(shape = var_11001, x = k_223_cast)[name = tensor<string, []>("op_11002_cast")];
            tensor<int32, [4]> var_11003 = const()[name = tensor<string, []>("op_11003"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11004_cast = reshape(shape = var_11003, x = v_223_cast)[name = tensor<string, []>("op_11004_cast")];
            tensor<bool, []> attn_weights_445_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_445_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_445_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_445_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_445_cast = matmul(transpose_x = attn_weights_445_transpose_x_0, transpose_y = attn_weights_445_transpose_y_0, x = var_11000_cast, y = var_11002_cast)[name = tensor<string, []>("attn_weights_445_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_447_cast = mul(x = attn_weights_445_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_447_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_11008_cast = softmax(axis = var_6849, x = attn_weights_447_cast)[name = tensor<string, []>("op_11008_cast")];
            tensor<bool, []> attn_223_transpose_x_0 = const()[name = tensor<string, []>("attn_223_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_223_transpose_y_0 = const()[name = tensor<string, []>("attn_223_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_223_cast = matmul(transpose_x = attn_223_transpose_x_0, transpose_y = attn_223_transpose_y_0, x = var_11004_cast, y = var_11008_cast)[name = tensor<string, []>("attn_223_cast")];
            tensor<int32, [4]> var_11012 = const()[name = tensor<string, []>("op_11012"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_645_cast = reshape(shape = var_11012, x = attn_223_cast)[name = tensor<string, []>("input_645_cast")];
            tensor<int32, [2]> var_11017 = const()[name = tensor<string, []>("op_11017"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11019 = const()[name = tensor<string, []>("op_11019"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11021_pad_type_0 = const()[name = tensor<string, []>("op_11021_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11021_pad_0 = const()[name = tensor<string, []>("op_11021_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1123996160))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1124815424))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1124815552)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11021_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_11019, groups = var_6865, pad = var_11021_pad_0, pad_type = var_11021_pad_type_0, strides = var_11017, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_645_cast)[name = tensor<string, []>("op_11021_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_335_cast = add(x = var_11021_cast, y = inputs_333_cast)[name = tensor<string, []>("inputs_335_cast")];
            tensor<int32, [1]> var_11025 = const()[name = tensor<string, []>("op_11025"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_335_cast = reduce_mean(axes = var_11025, keep_dims = var_6860, x = inputs_335_cast)[name = tensor<string, []>("channels_mean_335_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_335_cast = sub(x = inputs_335_cast, y = channels_mean_335_cast)[name = tensor<string, []>("zero_mean_335_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_335_cast = mul(x = zero_mean_335_cast, y = zero_mean_335_cast)[name = tensor<string, []>("zero_mean_sq_335_cast")];
            tensor<int32, [1]> var_11029 = const()[name = tensor<string, []>("op_11029"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11030_cast = reduce_mean(axes = var_11029, keep_dims = var_6860, x = zero_mean_sq_335_cast)[name = tensor<string, []>("op_11030_cast")];
            tensor<fp16, []> var_11031_to_fp16 = const()[name = tensor<string, []>("op_11031_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11032_cast = add(x = var_11030_cast, y = var_11031_to_fp16)[name = tensor<string, []>("op_11032_cast")];
            tensor<fp16, []> denom_335_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_335_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_335_cast = rsqrt(epsilon = denom_335_epsilon_0_to_fp16, x = var_11032_cast)[name = tensor<string, []>("denom_335_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_335_cast = mul(x = zero_mean_335_cast, y = denom_335_cast)[name = tensor<string, []>("out_335_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11036_to_fp16 = const()[name = tensor<string, []>("op_11036_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1124818176)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11037_cast = add(x = out_335_cast, y = var_11036_to_fp16)[name = tensor<string, []>("op_11037_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11039_to_fp16 = const()[name = tensor<string, []>("op_11039_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1124820800)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_647_cast = mul(x = var_11037_cast, y = var_11039_to_fp16)[name = tensor<string, []>("input_647_cast")];
            tensor<int32, [2]> var_11047 = const()[name = tensor<string, []>("op_11047"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11049 = const()[name = tensor<string, []>("op_11049"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11051_pad_type_0 = const()[name = tensor<string, []>("op_11051_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11051_pad_0 = const()[name = tensor<string, []>("op_11051_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1124823424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134653888))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134654080)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_11051_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_11049, groups = var_6865, pad = var_11051_pad_0, pad_type = var_11051_pad_type_0, strides = var_11047, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_647_cast)[name = tensor<string, []>("op_11051_cast")];
            tensor<int32, [2]> var_11052_split_sizes_0 = const()[name = tensor<string, []>("op_11052_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_11052_axis_0 = const()[name = tensor<string, []>("op_11052_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_11052_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_11052_cast_1 = split(axis = var_11052_axis_0, split_sizes = var_11052_split_sizes_0, x = var_11051_cast)[name = tensor<string, []>("op_11052_cast")];
            tensor<string, []> var_11054_mode_0 = const()[name = tensor<string, []>("op_11054_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_11054_cast = gelu(mode = var_11054_mode_0, x = var_11052_cast_1)[name = tensor<string, []>("op_11054_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_649_cast = mul(x = var_11052_cast_0, y = var_11054_cast)[name = tensor<string, []>("input_649_cast")];
            tensor<int32, [2]> var_11058 = const()[name = tensor<string, []>("op_11058"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11060 = const()[name = tensor<string, []>("op_11060"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11062_pad_type_0 = const()[name = tensor<string, []>("op_11062_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11062_pad_0 = const()[name = tensor<string, []>("op_11062_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134674624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1139589888))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1139590080)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11062_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_11060, groups = var_6865, pad = var_11062_pad_0, pad_type = var_11062_pad_type_0, strides = var_11058, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_649_cast)[name = tensor<string, []>("op_11062_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_337_cast = add(x = var_11062_cast, y = inputs_335_cast)[name = tensor<string, []>("inputs_337_cast")];
            tensor<int32, [1]> var_11072 = const()[name = tensor<string, []>("op_11072"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_337_cast = reduce_mean(axes = var_11072, keep_dims = var_6860, x = inputs_337_cast)[name = tensor<string, []>("channels_mean_337_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_337_cast = sub(x = inputs_337_cast, y = channels_mean_337_cast)[name = tensor<string, []>("zero_mean_337_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_337_cast = mul(x = zero_mean_337_cast, y = zero_mean_337_cast)[name = tensor<string, []>("zero_mean_sq_337_cast")];
            tensor<int32, [1]> var_11076 = const()[name = tensor<string, []>("op_11076"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11077_cast = reduce_mean(axes = var_11076, keep_dims = var_6860, x = zero_mean_sq_337_cast)[name = tensor<string, []>("op_11077_cast")];
            tensor<fp16, []> var_11078_to_fp16 = const()[name = tensor<string, []>("op_11078_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11079_cast = add(x = var_11077_cast, y = var_11078_to_fp16)[name = tensor<string, []>("op_11079_cast")];
            tensor<fp16, []> denom_337_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_337_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_337_cast = rsqrt(epsilon = denom_337_epsilon_0_to_fp16, x = var_11079_cast)[name = tensor<string, []>("denom_337_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_337_cast = mul(x = zero_mean_337_cast, y = denom_337_cast)[name = tensor<string, []>("out_337_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11083_to_fp16 = const()[name = tensor<string, []>("op_11083_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1139592704)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11084_cast = add(x = out_337_cast, y = var_11083_to_fp16)[name = tensor<string, []>("op_11084_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11086_to_fp16 = const()[name = tensor<string, []>("op_11086_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1139595328)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_447_cast = mul(x = var_11084_cast, y = var_11086_to_fp16)[name = tensor<string, []>("hidden_states_447_cast")];
            tensor<int32, [2]> var_11093 = const()[name = tensor<string, []>("op_11093"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11095 = const()[name = tensor<string, []>("op_11095"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_225_pad_type_0 = const()[name = tensor<string, []>("q_225_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_225_pad_0 = const()[name = tensor<string, []>("q_225_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1139597952))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1140417216))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_225_cast = conv(dilations = var_11095, groups = var_6865, pad = q_225_pad_0, pad_type = q_225_pad_type_0, strides = var_11093, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_447_cast)[name = tensor<string, []>("q_225_cast")];
            tensor<int32, [2]> var_11099 = const()[name = tensor<string, []>("op_11099"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11101 = const()[name = tensor<string, []>("op_11101"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_225_pad_type_0 = const()[name = tensor<string, []>("k_225_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_225_pad_0 = const()[name = tensor<string, []>("k_225_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1140417344))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1141236608))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_225_cast = conv(dilations = var_11101, groups = var_6865, pad = k_225_pad_0, pad_type = k_225_pad_type_0, strides = var_11099, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_447_cast)[name = tensor<string, []>("k_225_cast")];
            tensor<int32, [2]> var_11105 = const()[name = tensor<string, []>("op_11105"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11107 = const()[name = tensor<string, []>("op_11107"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_225_pad_type_0 = const()[name = tensor<string, []>("v_225_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_225_pad_0 = const()[name = tensor<string, []>("v_225_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1141236736))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1142465600))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_225_cast = conv(dilations = var_11107, groups = var_6865, pad = v_225_pad_0, pad_type = v_225_pad_type_0, strides = var_11105, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_447_cast)[name = tensor<string, []>("v_225_cast")];
            tensor<int32, [4]> var_11111 = const()[name = tensor<string, []>("op_11111"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11112_cast = reshape(shape = var_11111, x = q_225_cast)[name = tensor<string, []>("op_11112_cast")];
            tensor<int32, [4]> var_11113 = const()[name = tensor<string, []>("op_11113"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11114_cast = reshape(shape = var_11113, x = k_225_cast)[name = tensor<string, []>("op_11114_cast")];
            tensor<int32, [4]> var_11115 = const()[name = tensor<string, []>("op_11115"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11116_cast = reshape(shape = var_11115, x = v_225_cast)[name = tensor<string, []>("op_11116_cast")];
            tensor<bool, []> attn_weights_449_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_449_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_449_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_449_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_449_cast = matmul(transpose_x = attn_weights_449_transpose_x_0, transpose_y = attn_weights_449_transpose_y_0, x = var_11112_cast, y = var_11114_cast)[name = tensor<string, []>("attn_weights_449_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_451_cast = mul(x = attn_weights_449_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_451_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_11120_cast = softmax(axis = var_6849, x = attn_weights_451_cast)[name = tensor<string, []>("op_11120_cast")];
            tensor<bool, []> attn_225_transpose_x_0 = const()[name = tensor<string, []>("attn_225_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_225_transpose_y_0 = const()[name = tensor<string, []>("attn_225_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_225_cast = matmul(transpose_x = attn_225_transpose_x_0, transpose_y = attn_225_transpose_y_0, x = var_11116_cast, y = var_11120_cast)[name = tensor<string, []>("attn_225_cast")];
            tensor<int32, [4]> var_11124 = const()[name = tensor<string, []>("op_11124"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_651_cast = reshape(shape = var_11124, x = attn_225_cast)[name = tensor<string, []>("input_651_cast")];
            tensor<int32, [2]> var_11129 = const()[name = tensor<string, []>("op_11129"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11131 = const()[name = tensor<string, []>("op_11131"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11133_pad_type_0 = const()[name = tensor<string, []>("op_11133_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11133_pad_0 = const()[name = tensor<string, []>("op_11133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1142465792))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1143694656))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1143694848)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11133_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_11131, groups = var_6865, pad = var_11133_pad_0, pad_type = var_11133_pad_type_0, strides = var_11129, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_651_cast)[name = tensor<string, []>("op_11133_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_339_cast = add(x = var_11133_cast, y = inputs_337_cast)[name = tensor<string, []>("inputs_339_cast")];
            tensor<int32, [1]> var_11137 = const()[name = tensor<string, []>("op_11137"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_339_cast = reduce_mean(axes = var_11137, keep_dims = var_6860, x = inputs_339_cast)[name = tensor<string, []>("channels_mean_339_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_339_cast = sub(x = inputs_339_cast, y = channels_mean_339_cast)[name = tensor<string, []>("zero_mean_339_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_339_cast = mul(x = zero_mean_339_cast, y = zero_mean_339_cast)[name = tensor<string, []>("zero_mean_sq_339_cast")];
            tensor<int32, [1]> var_11141 = const()[name = tensor<string, []>("op_11141"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11142_cast = reduce_mean(axes = var_11141, keep_dims = var_6860, x = zero_mean_sq_339_cast)[name = tensor<string, []>("op_11142_cast")];
            tensor<fp16, []> var_11143_to_fp16 = const()[name = tensor<string, []>("op_11143_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11144_cast = add(x = var_11142_cast, y = var_11143_to_fp16)[name = tensor<string, []>("op_11144_cast")];
            tensor<fp16, []> denom_339_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_339_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_339_cast = rsqrt(epsilon = denom_339_epsilon_0_to_fp16, x = var_11144_cast)[name = tensor<string, []>("denom_339_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_339_cast = mul(x = zero_mean_339_cast, y = denom_339_cast)[name = tensor<string, []>("out_339_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11148_to_fp16 = const()[name = tensor<string, []>("op_11148_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1143697472)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11149_cast = add(x = out_339_cast, y = var_11148_to_fp16)[name = tensor<string, []>("op_11149_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11151_to_fp16 = const()[name = tensor<string, []>("op_11151_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1143700096)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_449_cast = mul(x = var_11149_cast, y = var_11151_to_fp16)[name = tensor<string, []>("hidden_states_449_cast")];
            tensor<int32, [2]> var_11158 = const()[name = tensor<string, []>("op_11158"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11160 = const()[name = tensor<string, []>("op_11160"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_227_pad_type_0 = const()[name = tensor<string, []>("q_227_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_227_pad_0 = const()[name = tensor<string, []>("q_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1143702720))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1144521984))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_227_cast = conv(dilations = var_11160, groups = var_6865, pad = q_227_pad_0, pad_type = q_227_pad_type_0, strides = var_11158, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_449_cast)[name = tensor<string, []>("q_227_cast")];
            tensor<int32, [2]> var_11164 = const()[name = tensor<string, []>("op_11164"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11166 = const()[name = tensor<string, []>("op_11166"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_227_pad_type_0 = const()[name = tensor<string, []>("k_227_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_227_pad_0 = const()[name = tensor<string, []>("k_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1144522112))), lut = tensor<fp16, [4]>([-0x1.fdcp-6, -0x1.22cp-7, 0x1.24cp-7, 0x1.ff4p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_227_cast = conv(dilations = var_11166, groups = var_6865, pad = k_227_pad_0, pad_type = k_227_pad_type_0, strides = var_11164, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_227_cast")];
            tensor<int32, [2]> var_11170 = const()[name = tensor<string, []>("op_11170"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11172 = const()[name = tensor<string, []>("op_11172"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_227_pad_type_0 = const()[name = tensor<string, []>("v_227_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_227_pad_0 = const()[name = tensor<string, []>("v_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1145177536))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1146488320))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_227_cast = conv(dilations = var_11172, groups = var_6865, pad = v_227_pad_0, pad_type = v_227_pad_type_0, strides = var_11170, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_227_cast")];
            tensor<int32, [4]> var_11176 = const()[name = tensor<string, []>("op_11176"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11177_cast = reshape(shape = var_11176, x = q_227_cast)[name = tensor<string, []>("op_11177_cast")];
            tensor<int32, [4]> var_11178 = const()[name = tensor<string, []>("op_11178"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11179_cast = reshape(shape = var_11178, x = k_227_cast)[name = tensor<string, []>("op_11179_cast")];
            tensor<int32, [4]> var_11180 = const()[name = tensor<string, []>("op_11180"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11181_cast = reshape(shape = var_11180, x = v_227_cast)[name = tensor<string, []>("op_11181_cast")];
            tensor<bool, []> attn_weights_453_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_453_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_453_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_453_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_453_cast = matmul(transpose_x = attn_weights_453_transpose_x_0, transpose_y = attn_weights_453_transpose_y_0, x = var_11177_cast, y = var_11179_cast)[name = tensor<string, []>("attn_weights_453_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_455_cast = mul(x = attn_weights_453_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_455_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_11185_cast = softmax(axis = var_6849, x = attn_weights_455_cast)[name = tensor<string, []>("op_11185_cast")];
            tensor<bool, []> attn_227_transpose_x_0 = const()[name = tensor<string, []>("attn_227_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_227_transpose_y_0 = const()[name = tensor<string, []>("attn_227_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_227_cast = matmul(transpose_x = attn_227_transpose_x_0, transpose_y = attn_227_transpose_y_0, x = var_11181_cast, y = var_11185_cast)[name = tensor<string, []>("attn_227_cast")];
            tensor<int32, [4]> var_11189 = const()[name = tensor<string, []>("op_11189"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_653_cast = reshape(shape = var_11189, x = attn_227_cast)[name = tensor<string, []>("input_653_cast")];
            tensor<int32, [2]> var_11194 = const()[name = tensor<string, []>("op_11194"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11196 = const()[name = tensor<string, []>("op_11196"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11198_pad_type_0 = const()[name = tensor<string, []>("op_11198_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11198_pad_0 = const()[name = tensor<string, []>("op_11198_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1146488448))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1147307712))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1147307840)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11198_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_11196, groups = var_6865, pad = var_11198_pad_0, pad_type = var_11198_pad_type_0, strides = var_11194, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_653_cast)[name = tensor<string, []>("op_11198_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_341_cast = add(x = var_11198_cast, y = inputs_339_cast)[name = tensor<string, []>("inputs_341_cast")];
            tensor<int32, [1]> var_11202 = const()[name = tensor<string, []>("op_11202"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_341_cast = reduce_mean(axes = var_11202, keep_dims = var_6860, x = inputs_341_cast)[name = tensor<string, []>("channels_mean_341_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_341_cast = sub(x = inputs_341_cast, y = channels_mean_341_cast)[name = tensor<string, []>("zero_mean_341_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_341_cast = mul(x = zero_mean_341_cast, y = zero_mean_341_cast)[name = tensor<string, []>("zero_mean_sq_341_cast")];
            tensor<int32, [1]> var_11206 = const()[name = tensor<string, []>("op_11206"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11207_cast = reduce_mean(axes = var_11206, keep_dims = var_6860, x = zero_mean_sq_341_cast)[name = tensor<string, []>("op_11207_cast")];
            tensor<fp16, []> var_11208_to_fp16 = const()[name = tensor<string, []>("op_11208_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11209_cast = add(x = var_11207_cast, y = var_11208_to_fp16)[name = tensor<string, []>("op_11209_cast")];
            tensor<fp16, []> denom_341_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_341_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_341_cast = rsqrt(epsilon = denom_341_epsilon_0_to_fp16, x = var_11209_cast)[name = tensor<string, []>("denom_341_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_341_cast = mul(x = zero_mean_341_cast, y = denom_341_cast)[name = tensor<string, []>("out_341_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11213_to_fp16 = const()[name = tensor<string, []>("op_11213_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1147310464)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11214_cast = add(x = out_341_cast, y = var_11213_to_fp16)[name = tensor<string, []>("op_11214_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11216_to_fp16 = const()[name = tensor<string, []>("op_11216_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1147313088)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_655_cast = mul(x = var_11214_cast, y = var_11216_to_fp16)[name = tensor<string, []>("input_655_cast")];
            tensor<int32, [2]> var_11224 = const()[name = tensor<string, []>("op_11224"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11226 = const()[name = tensor<string, []>("op_11226"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11228_pad_type_0 = const()[name = tensor<string, []>("op_11228_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11228_pad_0 = const()[name = tensor<string, []>("op_11228_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1147315712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157146176))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157146368)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_11228_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_11226, groups = var_6865, pad = var_11228_pad_0, pad_type = var_11228_pad_type_0, strides = var_11224, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_655_cast)[name = tensor<string, []>("op_11228_cast")];
            tensor<int32, [2]> var_11229_split_sizes_0 = const()[name = tensor<string, []>("op_11229_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_11229_axis_0 = const()[name = tensor<string, []>("op_11229_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_11229_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_11229_cast_1 = split(axis = var_11229_axis_0, split_sizes = var_11229_split_sizes_0, x = var_11228_cast)[name = tensor<string, []>("op_11229_cast")];
            tensor<string, []> var_11231_mode_0 = const()[name = tensor<string, []>("op_11231_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_11231_cast = gelu(mode = var_11231_mode_0, x = var_11229_cast_1)[name = tensor<string, []>("op_11231_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_657_cast = mul(x = var_11229_cast_0, y = var_11231_cast)[name = tensor<string, []>("input_657_cast")];
            tensor<int32, [2]> var_11235 = const()[name = tensor<string, []>("op_11235"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11237 = const()[name = tensor<string, []>("op_11237"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11239_pad_type_0 = const()[name = tensor<string, []>("op_11239_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11239_pad_0 = const()[name = tensor<string, []>("op_11239_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157166912))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162082176))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162082368)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11239_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_11237, groups = var_6865, pad = var_11239_pad_0, pad_type = var_11239_pad_type_0, strides = var_11235, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_657_cast)[name = tensor<string, []>("op_11239_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_343_cast = add(x = var_11239_cast, y = inputs_341_cast)[name = tensor<string, []>("inputs_343_cast")];
            tensor<int32, [1]> var_11249 = const()[name = tensor<string, []>("op_11249"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_343_cast = reduce_mean(axes = var_11249, keep_dims = var_6860, x = inputs_343_cast)[name = tensor<string, []>("channels_mean_343_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_343_cast = sub(x = inputs_343_cast, y = channels_mean_343_cast)[name = tensor<string, []>("zero_mean_343_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_343_cast = mul(x = zero_mean_343_cast, y = zero_mean_343_cast)[name = tensor<string, []>("zero_mean_sq_343_cast")];
            tensor<int32, [1]> var_11253 = const()[name = tensor<string, []>("op_11253"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11254_cast = reduce_mean(axes = var_11253, keep_dims = var_6860, x = zero_mean_sq_343_cast)[name = tensor<string, []>("op_11254_cast")];
            tensor<fp16, []> var_11255_to_fp16 = const()[name = tensor<string, []>("op_11255_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11256_cast = add(x = var_11254_cast, y = var_11255_to_fp16)[name = tensor<string, []>("op_11256_cast")];
            tensor<fp16, []> denom_343_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_343_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_343_cast = rsqrt(epsilon = denom_343_epsilon_0_to_fp16, x = var_11256_cast)[name = tensor<string, []>("denom_343_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_343_cast = mul(x = zero_mean_343_cast, y = denom_343_cast)[name = tensor<string, []>("out_343_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11260_to_fp16 = const()[name = tensor<string, []>("op_11260_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162084992)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11261_cast = add(x = out_343_cast, y = var_11260_to_fp16)[name = tensor<string, []>("op_11261_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11263_to_fp16 = const()[name = tensor<string, []>("op_11263_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162087616)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_453_cast = mul(x = var_11261_cast, y = var_11263_to_fp16)[name = tensor<string, []>("hidden_states_453_cast")];
            tensor<int32, [2]> var_11270 = const()[name = tensor<string, []>("op_11270"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11272 = const()[name = tensor<string, []>("op_11272"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_229_pad_type_0 = const()[name = tensor<string, []>("q_229_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_229_pad_0 = const()[name = tensor<string, []>("q_229_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162090240))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162909504))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_229_cast = conv(dilations = var_11272, groups = var_6865, pad = q_229_pad_0, pad_type = q_229_pad_type_0, strides = var_11270, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_453_cast)[name = tensor<string, []>("q_229_cast")];
            tensor<int32, [2]> var_11276 = const()[name = tensor<string, []>("op_11276"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11278 = const()[name = tensor<string, []>("op_11278"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_229_pad_type_0 = const()[name = tensor<string, []>("k_229_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_229_pad_0 = const()[name = tensor<string, []>("k_229_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162909632))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1163728896))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_229_cast = conv(dilations = var_11278, groups = var_6865, pad = k_229_pad_0, pad_type = k_229_pad_type_0, strides = var_11276, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_453_cast)[name = tensor<string, []>("k_229_cast")];
            tensor<int32, [2]> var_11282 = const()[name = tensor<string, []>("op_11282"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11284 = const()[name = tensor<string, []>("op_11284"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_229_pad_type_0 = const()[name = tensor<string, []>("v_229_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_229_pad_0 = const()[name = tensor<string, []>("v_229_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1163729024))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1164957888))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_229_cast = conv(dilations = var_11284, groups = var_6865, pad = v_229_pad_0, pad_type = v_229_pad_type_0, strides = var_11282, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_453_cast)[name = tensor<string, []>("v_229_cast")];
            tensor<int32, [4]> var_11288 = const()[name = tensor<string, []>("op_11288"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11289_cast = reshape(shape = var_11288, x = q_229_cast)[name = tensor<string, []>("op_11289_cast")];
            tensor<int32, [4]> var_11290 = const()[name = tensor<string, []>("op_11290"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11291_cast = reshape(shape = var_11290, x = k_229_cast)[name = tensor<string, []>("op_11291_cast")];
            tensor<int32, [4]> var_11292 = const()[name = tensor<string, []>("op_11292"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11293_cast = reshape(shape = var_11292, x = v_229_cast)[name = tensor<string, []>("op_11293_cast")];
            tensor<bool, []> attn_weights_457_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_457_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_457_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_457_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_457_cast = matmul(transpose_x = attn_weights_457_transpose_x_0, transpose_y = attn_weights_457_transpose_y_0, x = var_11289_cast, y = var_11291_cast)[name = tensor<string, []>("attn_weights_457_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_459_cast = mul(x = attn_weights_457_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_459_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_11297_cast = softmax(axis = var_6849, x = attn_weights_459_cast)[name = tensor<string, []>("op_11297_cast")];
            tensor<bool, []> attn_229_transpose_x_0 = const()[name = tensor<string, []>("attn_229_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_229_transpose_y_0 = const()[name = tensor<string, []>("attn_229_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_229_cast = matmul(transpose_x = attn_229_transpose_x_0, transpose_y = attn_229_transpose_y_0, x = var_11293_cast, y = var_11297_cast)[name = tensor<string, []>("attn_229_cast")];
            tensor<int32, [4]> var_11301 = const()[name = tensor<string, []>("op_11301"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_659_cast = reshape(shape = var_11301, x = attn_229_cast)[name = tensor<string, []>("input_659_cast")];
            tensor<int32, [2]> var_11306 = const()[name = tensor<string, []>("op_11306"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11308 = const()[name = tensor<string, []>("op_11308"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11310_pad_type_0 = const()[name = tensor<string, []>("op_11310_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11310_pad_0 = const()[name = tensor<string, []>("op_11310_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1164958080))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166186944))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166187136)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11310_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_11308, groups = var_6865, pad = var_11310_pad_0, pad_type = var_11310_pad_type_0, strides = var_11306, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_659_cast)[name = tensor<string, []>("op_11310_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_345_cast = add(x = var_11310_cast, y = inputs_343_cast)[name = tensor<string, []>("inputs_345_cast")];
            tensor<int32, [1]> var_11314 = const()[name = tensor<string, []>("op_11314"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_345_cast = reduce_mean(axes = var_11314, keep_dims = var_6860, x = inputs_345_cast)[name = tensor<string, []>("channels_mean_345_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_345_cast = sub(x = inputs_345_cast, y = channels_mean_345_cast)[name = tensor<string, []>("zero_mean_345_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_345_cast = mul(x = zero_mean_345_cast, y = zero_mean_345_cast)[name = tensor<string, []>("zero_mean_sq_345_cast")];
            tensor<int32, [1]> var_11318 = const()[name = tensor<string, []>("op_11318"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11319_cast = reduce_mean(axes = var_11318, keep_dims = var_6860, x = zero_mean_sq_345_cast)[name = tensor<string, []>("op_11319_cast")];
            tensor<fp16, []> var_11320_to_fp16 = const()[name = tensor<string, []>("op_11320_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11321_cast = add(x = var_11319_cast, y = var_11320_to_fp16)[name = tensor<string, []>("op_11321_cast")];
            tensor<fp16, []> denom_345_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_345_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_345_cast = rsqrt(epsilon = denom_345_epsilon_0_to_fp16, x = var_11321_cast)[name = tensor<string, []>("denom_345_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_345_cast = mul(x = zero_mean_345_cast, y = denom_345_cast)[name = tensor<string, []>("out_345_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11325_to_fp16 = const()[name = tensor<string, []>("op_11325_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166189760)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11326_cast = add(x = out_345_cast, y = var_11325_to_fp16)[name = tensor<string, []>("op_11326_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11328_to_fp16 = const()[name = tensor<string, []>("op_11328_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166192384)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_455_cast = mul(x = var_11326_cast, y = var_11328_to_fp16)[name = tensor<string, []>("hidden_states_455_cast")];
            tensor<int32, [2]> var_11335 = const()[name = tensor<string, []>("op_11335"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11337 = const()[name = tensor<string, []>("op_11337"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_231_pad_type_0 = const()[name = tensor<string, []>("q_231_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_231_pad_0 = const()[name = tensor<string, []>("q_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166195008))), lut = tensor<fp16, [4]>([-0x1.028p-5, -0x1.36cp-7, 0x1.348p-7, 0x1.024p-5]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_231_cast = conv(dilations = var_11337, groups = var_6865, pad = q_231_pad_0, pad_type = q_231_pad_type_0, strides = var_11335, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_455_cast)[name = tensor<string, []>("q_231_cast")];
            tensor<int32, [2]> var_11341 = const()[name = tensor<string, []>("op_11341"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11343 = const()[name = tensor<string, []>("op_11343"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_231_pad_type_0 = const()[name = tensor<string, []>("k_231_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_231_pad_0 = const()[name = tensor<string, []>("k_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166604672))), lut = tensor<fp16, [4]>([-0x1.b3cp-6, -0x1.f7cp-8, 0x1.fb8p-8, 0x1.b6p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_231_cast = conv(dilations = var_11343, groups = var_6865, pad = k_231_pad_0, pad_type = k_231_pad_type_0, strides = var_11341, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_231_cast")];
            tensor<int32, [2]> var_11347 = const()[name = tensor<string, []>("op_11347"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11349 = const()[name = tensor<string, []>("op_11349"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_231_pad_type_0 = const()[name = tensor<string, []>("v_231_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_231_pad_0 = const()[name = tensor<string, []>("v_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1167260096))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1168570880))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_231_cast = conv(dilations = var_11349, groups = var_6865, pad = v_231_pad_0, pad_type = v_231_pad_type_0, strides = var_11347, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_231_cast")];
            tensor<int32, [4]> var_11353 = const()[name = tensor<string, []>("op_11353"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11354_cast = reshape(shape = var_11353, x = q_231_cast)[name = tensor<string, []>("op_11354_cast")];
            tensor<int32, [4]> var_11355 = const()[name = tensor<string, []>("op_11355"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11356_cast = reshape(shape = var_11355, x = k_231_cast)[name = tensor<string, []>("op_11356_cast")];
            tensor<int32, [4]> var_11357 = const()[name = tensor<string, []>("op_11357"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11358_cast = reshape(shape = var_11357, x = v_231_cast)[name = tensor<string, []>("op_11358_cast")];
            tensor<bool, []> attn_weights_461_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_461_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_461_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_461_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_461_cast = matmul(transpose_x = attn_weights_461_transpose_x_0, transpose_y = attn_weights_461_transpose_y_0, x = var_11354_cast, y = var_11356_cast)[name = tensor<string, []>("attn_weights_461_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_463_cast = mul(x = attn_weights_461_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_463_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_11362_cast = softmax(axis = var_6849, x = attn_weights_463_cast)[name = tensor<string, []>("op_11362_cast")];
            tensor<bool, []> attn_231_transpose_x_0 = const()[name = tensor<string, []>("attn_231_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_231_transpose_y_0 = const()[name = tensor<string, []>("attn_231_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_231_cast = matmul(transpose_x = attn_231_transpose_x_0, transpose_y = attn_231_transpose_y_0, x = var_11358_cast, y = var_11362_cast)[name = tensor<string, []>("attn_231_cast")];
            tensor<int32, [4]> var_11366 = const()[name = tensor<string, []>("op_11366"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_661_cast = reshape(shape = var_11366, x = attn_231_cast)[name = tensor<string, []>("input_661_cast")];
            tensor<int32, [2]> var_11371 = const()[name = tensor<string, []>("op_11371"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11373 = const()[name = tensor<string, []>("op_11373"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11375_pad_type_0 = const()[name = tensor<string, []>("op_11375_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11375_pad_0 = const()[name = tensor<string, []>("op_11375_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1168571008))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1169390272))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1169390400)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11375_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_11373, groups = var_6865, pad = var_11375_pad_0, pad_type = var_11375_pad_type_0, strides = var_11371, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_661_cast)[name = tensor<string, []>("op_11375_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_347_cast = add(x = var_11375_cast, y = inputs_345_cast)[name = tensor<string, []>("inputs_347_cast")];
            tensor<int32, [1]> var_11379 = const()[name = tensor<string, []>("op_11379"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_347_cast = reduce_mean(axes = var_11379, keep_dims = var_6860, x = inputs_347_cast)[name = tensor<string, []>("channels_mean_347_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_347_cast = sub(x = inputs_347_cast, y = channels_mean_347_cast)[name = tensor<string, []>("zero_mean_347_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_347_cast = mul(x = zero_mean_347_cast, y = zero_mean_347_cast)[name = tensor<string, []>("zero_mean_sq_347_cast")];
            tensor<int32, [1]> var_11383 = const()[name = tensor<string, []>("op_11383"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11384_cast = reduce_mean(axes = var_11383, keep_dims = var_6860, x = zero_mean_sq_347_cast)[name = tensor<string, []>("op_11384_cast")];
            tensor<fp16, []> var_11385_to_fp16 = const()[name = tensor<string, []>("op_11385_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11386_cast = add(x = var_11384_cast, y = var_11385_to_fp16)[name = tensor<string, []>("op_11386_cast")];
            tensor<fp16, []> denom_347_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_347_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_347_cast = rsqrt(epsilon = denom_347_epsilon_0_to_fp16, x = var_11386_cast)[name = tensor<string, []>("denom_347_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_347_cast = mul(x = zero_mean_347_cast, y = denom_347_cast)[name = tensor<string, []>("out_347_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11390_to_fp16 = const()[name = tensor<string, []>("op_11390_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1169393024)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11391_cast = add(x = out_347_cast, y = var_11390_to_fp16)[name = tensor<string, []>("op_11391_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11393_to_fp16 = const()[name = tensor<string, []>("op_11393_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1169395648)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_663_cast = mul(x = var_11391_cast, y = var_11393_to_fp16)[name = tensor<string, []>("input_663_cast")];
            tensor<int32, [2]> var_11401 = const()[name = tensor<string, []>("op_11401"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11403 = const()[name = tensor<string, []>("op_11403"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11405_pad_type_0 = const()[name = tensor<string, []>("op_11405_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11405_pad_0 = const()[name = tensor<string, []>("op_11405_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1169398272))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1179228736))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1179228928)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_11405_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_11403, groups = var_6865, pad = var_11405_pad_0, pad_type = var_11405_pad_type_0, strides = var_11401, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_663_cast)[name = tensor<string, []>("op_11405_cast")];
            tensor<int32, [2]> var_11406_split_sizes_0 = const()[name = tensor<string, []>("op_11406_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_11406_axis_0 = const()[name = tensor<string, []>("op_11406_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_11406_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_11406_cast_1 = split(axis = var_11406_axis_0, split_sizes = var_11406_split_sizes_0, x = var_11405_cast)[name = tensor<string, []>("op_11406_cast")];
            tensor<string, []> var_11408_mode_0 = const()[name = tensor<string, []>("op_11408_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_11408_cast = gelu(mode = var_11408_mode_0, x = var_11406_cast_1)[name = tensor<string, []>("op_11408_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_665_cast = mul(x = var_11406_cast_0, y = var_11408_cast)[name = tensor<string, []>("input_665_cast")];
            tensor<int32, [2]> var_11412 = const()[name = tensor<string, []>("op_11412"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11414 = const()[name = tensor<string, []>("op_11414"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11416_pad_type_0 = const()[name = tensor<string, []>("op_11416_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11416_pad_0 = const()[name = tensor<string, []>("op_11416_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1179249472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184164736))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184164928)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11416_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_11414, groups = var_6865, pad = var_11416_pad_0, pad_type = var_11416_pad_type_0, strides = var_11412, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_665_cast)[name = tensor<string, []>("op_11416_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_349_cast = add(x = var_11416_cast, y = inputs_347_cast)[name = tensor<string, []>("inputs_349_cast")];
            tensor<int32, [1]> var_11426 = const()[name = tensor<string, []>("op_11426"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_349_cast = reduce_mean(axes = var_11426, keep_dims = var_6860, x = inputs_349_cast)[name = tensor<string, []>("channels_mean_349_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_349_cast = sub(x = inputs_349_cast, y = channels_mean_349_cast)[name = tensor<string, []>("zero_mean_349_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_349_cast = mul(x = zero_mean_349_cast, y = zero_mean_349_cast)[name = tensor<string, []>("zero_mean_sq_349_cast")];
            tensor<int32, [1]> var_11430 = const()[name = tensor<string, []>("op_11430"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11431_cast = reduce_mean(axes = var_11430, keep_dims = var_6860, x = zero_mean_sq_349_cast)[name = tensor<string, []>("op_11431_cast")];
            tensor<fp16, []> var_11432_to_fp16 = const()[name = tensor<string, []>("op_11432_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11433_cast = add(x = var_11431_cast, y = var_11432_to_fp16)[name = tensor<string, []>("op_11433_cast")];
            tensor<fp16, []> denom_349_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_349_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_349_cast = rsqrt(epsilon = denom_349_epsilon_0_to_fp16, x = var_11433_cast)[name = tensor<string, []>("denom_349_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_349_cast = mul(x = zero_mean_349_cast, y = denom_349_cast)[name = tensor<string, []>("out_349_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11437_to_fp16 = const()[name = tensor<string, []>("op_11437_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184167552)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11438_cast = add(x = out_349_cast, y = var_11437_to_fp16)[name = tensor<string, []>("op_11438_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11440_to_fp16 = const()[name = tensor<string, []>("op_11440_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184170176)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_459_cast = mul(x = var_11438_cast, y = var_11440_to_fp16)[name = tensor<string, []>("hidden_states_459_cast")];
            tensor<int32, [2]> var_11447 = const()[name = tensor<string, []>("op_11447"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11449 = const()[name = tensor<string, []>("op_11449"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_233_pad_type_0 = const()[name = tensor<string, []>("q_233_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_233_pad_0 = const()[name = tensor<string, []>("q_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184172800))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184992064))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_233_cast = conv(dilations = var_11449, groups = var_6865, pad = q_233_pad_0, pad_type = q_233_pad_type_0, strides = var_11447, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_459_cast)[name = tensor<string, []>("q_233_cast")];
            tensor<int32, [2]> var_11453 = const()[name = tensor<string, []>("op_11453"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11455 = const()[name = tensor<string, []>("op_11455"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_233_pad_type_0 = const()[name = tensor<string, []>("k_233_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_233_pad_0 = const()[name = tensor<string, []>("k_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184992192))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1185811456))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_233_cast = conv(dilations = var_11455, groups = var_6865, pad = k_233_pad_0, pad_type = k_233_pad_type_0, strides = var_11453, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_459_cast)[name = tensor<string, []>("k_233_cast")];
            tensor<int32, [2]> var_11459 = const()[name = tensor<string, []>("op_11459"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11461 = const()[name = tensor<string, []>("op_11461"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_233_pad_type_0 = const()[name = tensor<string, []>("v_233_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_233_pad_0 = const()[name = tensor<string, []>("v_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1185811584))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1187040448))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_233_cast = conv(dilations = var_11461, groups = var_6865, pad = v_233_pad_0, pad_type = v_233_pad_type_0, strides = var_11459, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_459_cast)[name = tensor<string, []>("v_233_cast")];
            tensor<int32, [4]> var_11465 = const()[name = tensor<string, []>("op_11465"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11466_cast = reshape(shape = var_11465, x = q_233_cast)[name = tensor<string, []>("op_11466_cast")];
            tensor<int32, [4]> var_11467 = const()[name = tensor<string, []>("op_11467"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11468_cast = reshape(shape = var_11467, x = k_233_cast)[name = tensor<string, []>("op_11468_cast")];
            tensor<int32, [4]> var_11469 = const()[name = tensor<string, []>("op_11469"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11470_cast = reshape(shape = var_11469, x = v_233_cast)[name = tensor<string, []>("op_11470_cast")];
            tensor<bool, []> attn_weights_465_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_465_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_465_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_465_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_465_cast = matmul(transpose_x = attn_weights_465_transpose_x_0, transpose_y = attn_weights_465_transpose_y_0, x = var_11466_cast, y = var_11468_cast)[name = tensor<string, []>("attn_weights_465_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_467_cast = mul(x = attn_weights_465_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_467_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_11474_cast = softmax(axis = var_6849, x = attn_weights_467_cast)[name = tensor<string, []>("op_11474_cast")];
            tensor<bool, []> attn_233_transpose_x_0 = const()[name = tensor<string, []>("attn_233_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_233_transpose_y_0 = const()[name = tensor<string, []>("attn_233_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_233_cast = matmul(transpose_x = attn_233_transpose_x_0, transpose_y = attn_233_transpose_y_0, x = var_11470_cast, y = var_11474_cast)[name = tensor<string, []>("attn_233_cast")];
            tensor<int32, [4]> var_11478 = const()[name = tensor<string, []>("op_11478"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_667_cast = reshape(shape = var_11478, x = attn_233_cast)[name = tensor<string, []>("input_667_cast")];
            tensor<int32, [2]> var_11483 = const()[name = tensor<string, []>("op_11483"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11485 = const()[name = tensor<string, []>("op_11485"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11487_pad_type_0 = const()[name = tensor<string, []>("op_11487_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11487_pad_0 = const()[name = tensor<string, []>("op_11487_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1187040640))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1188269504))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1188269696)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11487_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_11485, groups = var_6865, pad = var_11487_pad_0, pad_type = var_11487_pad_type_0, strides = var_11483, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_667_cast)[name = tensor<string, []>("op_11487_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_351_cast = add(x = var_11487_cast, y = inputs_349_cast)[name = tensor<string, []>("inputs_351_cast")];
            tensor<int32, [1]> var_11491 = const()[name = tensor<string, []>("op_11491"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_351_cast = reduce_mean(axes = var_11491, keep_dims = var_6860, x = inputs_351_cast)[name = tensor<string, []>("channels_mean_351_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_351_cast = sub(x = inputs_351_cast, y = channels_mean_351_cast)[name = tensor<string, []>("zero_mean_351_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_351_cast = mul(x = zero_mean_351_cast, y = zero_mean_351_cast)[name = tensor<string, []>("zero_mean_sq_351_cast")];
            tensor<int32, [1]> var_11495 = const()[name = tensor<string, []>("op_11495"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11496_cast = reduce_mean(axes = var_11495, keep_dims = var_6860, x = zero_mean_sq_351_cast)[name = tensor<string, []>("op_11496_cast")];
            tensor<fp16, []> var_11497_to_fp16 = const()[name = tensor<string, []>("op_11497_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11498_cast = add(x = var_11496_cast, y = var_11497_to_fp16)[name = tensor<string, []>("op_11498_cast")];
            tensor<fp16, []> denom_351_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_351_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_351_cast = rsqrt(epsilon = denom_351_epsilon_0_to_fp16, x = var_11498_cast)[name = tensor<string, []>("denom_351_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_351_cast = mul(x = zero_mean_351_cast, y = denom_351_cast)[name = tensor<string, []>("out_351_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11502_to_fp16 = const()[name = tensor<string, []>("op_11502_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1188272320)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11503_cast = add(x = out_351_cast, y = var_11502_to_fp16)[name = tensor<string, []>("op_11503_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11505_to_fp16 = const()[name = tensor<string, []>("op_11505_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1188274944)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_461_cast = mul(x = var_11503_cast, y = var_11505_to_fp16)[name = tensor<string, []>("hidden_states_461_cast")];
            tensor<int32, [2]> var_11512 = const()[name = tensor<string, []>("op_11512"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11514 = const()[name = tensor<string, []>("op_11514"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_235_pad_type_0 = const()[name = tensor<string, []>("q_235_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_235_pad_0 = const()[name = tensor<string, []>("q_235_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1188277568))), lut = tensor<fp16, [4]>([-0x1.c8cp-6, -0x1.17p-7, 0x1.178p-7, 0x1.c9p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_235_cast = conv(dilations = var_11514, groups = var_6865, pad = q_235_pad_0, pad_type = q_235_pad_type_0, strides = var_11512, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_461_cast)[name = tensor<string, []>("q_235_cast")];
            tensor<int32, [2]> var_11518 = const()[name = tensor<string, []>("op_11518"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11520 = const()[name = tensor<string, []>("op_11520"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_235_pad_type_0 = const()[name = tensor<string, []>("k_235_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_235_pad_0 = const()[name = tensor<string, []>("k_235_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1188687232))), lut = tensor<fp16, [4]>([-0x1.718p-6, -0x1.b18p-8, 0x1.b68p-8, 0x1.728p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_235_cast = conv(dilations = var_11520, groups = var_6865, pad = k_235_pad_0, pad_type = k_235_pad_type_0, strides = var_11518, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_235_cast")];
            tensor<int32, [2]> var_11524 = const()[name = tensor<string, []>("op_11524"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11526 = const()[name = tensor<string, []>("op_11526"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_235_pad_type_0 = const()[name = tensor<string, []>("v_235_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_235_pad_0 = const()[name = tensor<string, []>("v_235_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1189342656))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1190653440))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_235_cast = conv(dilations = var_11526, groups = var_6865, pad = v_235_pad_0, pad_type = v_235_pad_type_0, strides = var_11524, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_235_cast")];
            tensor<int32, [4]> var_11530 = const()[name = tensor<string, []>("op_11530"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11531_cast = reshape(shape = var_11530, x = q_235_cast)[name = tensor<string, []>("op_11531_cast")];
            tensor<int32, [4]> var_11532 = const()[name = tensor<string, []>("op_11532"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11533_cast = reshape(shape = var_11532, x = k_235_cast)[name = tensor<string, []>("op_11533_cast")];
            tensor<int32, [4]> var_11534 = const()[name = tensor<string, []>("op_11534"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11535_cast = reshape(shape = var_11534, x = v_235_cast)[name = tensor<string, []>("op_11535_cast")];
            tensor<bool, []> attn_weights_469_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_469_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_469_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_469_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_469_cast = matmul(transpose_x = attn_weights_469_transpose_x_0, transpose_y = attn_weights_469_transpose_y_0, x = var_11531_cast, y = var_11533_cast)[name = tensor<string, []>("attn_weights_469_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_471_cast = mul(x = attn_weights_469_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_471_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_11539_cast = softmax(axis = var_6849, x = attn_weights_471_cast)[name = tensor<string, []>("op_11539_cast")];
            tensor<bool, []> attn_235_transpose_x_0 = const()[name = tensor<string, []>("attn_235_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_235_transpose_y_0 = const()[name = tensor<string, []>("attn_235_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_235_cast = matmul(transpose_x = attn_235_transpose_x_0, transpose_y = attn_235_transpose_y_0, x = var_11535_cast, y = var_11539_cast)[name = tensor<string, []>("attn_235_cast")];
            tensor<int32, [4]> var_11543 = const()[name = tensor<string, []>("op_11543"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_669_cast = reshape(shape = var_11543, x = attn_235_cast)[name = tensor<string, []>("input_669_cast")];
            tensor<int32, [2]> var_11548 = const()[name = tensor<string, []>("op_11548"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11550 = const()[name = tensor<string, []>("op_11550"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11552_pad_type_0 = const()[name = tensor<string, []>("op_11552_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11552_pad_0 = const()[name = tensor<string, []>("op_11552_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1190653568))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1191472832))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1191472960)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11552_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_11550, groups = var_6865, pad = var_11552_pad_0, pad_type = var_11552_pad_type_0, strides = var_11548, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_669_cast)[name = tensor<string, []>("op_11552_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_353_cast = add(x = var_11552_cast, y = inputs_351_cast)[name = tensor<string, []>("inputs_353_cast")];
            tensor<int32, [1]> var_11556 = const()[name = tensor<string, []>("op_11556"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_353_cast = reduce_mean(axes = var_11556, keep_dims = var_6860, x = inputs_353_cast)[name = tensor<string, []>("channels_mean_353_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_353_cast = sub(x = inputs_353_cast, y = channels_mean_353_cast)[name = tensor<string, []>("zero_mean_353_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_353_cast = mul(x = zero_mean_353_cast, y = zero_mean_353_cast)[name = tensor<string, []>("zero_mean_sq_353_cast")];
            tensor<int32, [1]> var_11560 = const()[name = tensor<string, []>("op_11560"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11561_cast = reduce_mean(axes = var_11560, keep_dims = var_6860, x = zero_mean_sq_353_cast)[name = tensor<string, []>("op_11561_cast")];
            tensor<fp16, []> var_11562_to_fp16 = const()[name = tensor<string, []>("op_11562_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11563_cast = add(x = var_11561_cast, y = var_11562_to_fp16)[name = tensor<string, []>("op_11563_cast")];
            tensor<fp16, []> denom_353_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_353_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_353_cast = rsqrt(epsilon = denom_353_epsilon_0_to_fp16, x = var_11563_cast)[name = tensor<string, []>("denom_353_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_353_cast = mul(x = zero_mean_353_cast, y = denom_353_cast)[name = tensor<string, []>("out_353_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11567_to_fp16 = const()[name = tensor<string, []>("op_11567_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1191475584)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11568_cast = add(x = out_353_cast, y = var_11567_to_fp16)[name = tensor<string, []>("op_11568_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11570_to_fp16 = const()[name = tensor<string, []>("op_11570_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1191478208)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_671_cast = mul(x = var_11568_cast, y = var_11570_to_fp16)[name = tensor<string, []>("input_671_cast")];
            tensor<int32, [2]> var_11578 = const()[name = tensor<string, []>("op_11578"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11580 = const()[name = tensor<string, []>("op_11580"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11582_pad_type_0 = const()[name = tensor<string, []>("op_11582_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11582_pad_0 = const()[name = tensor<string, []>("op_11582_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1191480832))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1201311296))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1201311488)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_11582_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_11580, groups = var_6865, pad = var_11582_pad_0, pad_type = var_11582_pad_type_0, strides = var_11578, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_671_cast)[name = tensor<string, []>("op_11582_cast")];
            tensor<int32, [2]> var_11583_split_sizes_0 = const()[name = tensor<string, []>("op_11583_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_11583_axis_0 = const()[name = tensor<string, []>("op_11583_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_11583_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_11583_cast_1 = split(axis = var_11583_axis_0, split_sizes = var_11583_split_sizes_0, x = var_11582_cast)[name = tensor<string, []>("op_11583_cast")];
            tensor<string, []> var_11585_mode_0 = const()[name = tensor<string, []>("op_11585_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_11585_cast = gelu(mode = var_11585_mode_0, x = var_11583_cast_1)[name = tensor<string, []>("op_11585_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_673_cast = mul(x = var_11583_cast_0, y = var_11585_cast)[name = tensor<string, []>("input_673_cast")];
            tensor<int32, [2]> var_11589 = const()[name = tensor<string, []>("op_11589"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11591 = const()[name = tensor<string, []>("op_11591"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11593_pad_type_0 = const()[name = tensor<string, []>("op_11593_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11593_pad_0 = const()[name = tensor<string, []>("op_11593_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1201332032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1206247296))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1206247488)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11593_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_11591, groups = var_6865, pad = var_11593_pad_0, pad_type = var_11593_pad_type_0, strides = var_11589, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_673_cast)[name = tensor<string, []>("op_11593_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_355_cast = add(x = var_11593_cast, y = inputs_353_cast)[name = tensor<string, []>("inputs_355_cast")];
            tensor<int32, [1]> var_11603 = const()[name = tensor<string, []>("op_11603"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_355_cast = reduce_mean(axes = var_11603, keep_dims = var_6860, x = inputs_355_cast)[name = tensor<string, []>("channels_mean_355_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_355_cast = sub(x = inputs_355_cast, y = channels_mean_355_cast)[name = tensor<string, []>("zero_mean_355_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_355_cast = mul(x = zero_mean_355_cast, y = zero_mean_355_cast)[name = tensor<string, []>("zero_mean_sq_355_cast")];
            tensor<int32, [1]> var_11607 = const()[name = tensor<string, []>("op_11607"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11608_cast = reduce_mean(axes = var_11607, keep_dims = var_6860, x = zero_mean_sq_355_cast)[name = tensor<string, []>("op_11608_cast")];
            tensor<fp16, []> var_11609_to_fp16 = const()[name = tensor<string, []>("op_11609_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11610_cast = add(x = var_11608_cast, y = var_11609_to_fp16)[name = tensor<string, []>("op_11610_cast")];
            tensor<fp16, []> denom_355_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_355_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_355_cast = rsqrt(epsilon = denom_355_epsilon_0_to_fp16, x = var_11610_cast)[name = tensor<string, []>("denom_355_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_355_cast = mul(x = zero_mean_355_cast, y = denom_355_cast)[name = tensor<string, []>("out_355_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11614_to_fp16 = const()[name = tensor<string, []>("op_11614_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1206250112)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11615_cast = add(x = out_355_cast, y = var_11614_to_fp16)[name = tensor<string, []>("op_11615_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11617_to_fp16 = const()[name = tensor<string, []>("op_11617_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1206252736)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_465_cast = mul(x = var_11615_cast, y = var_11617_to_fp16)[name = tensor<string, []>("hidden_states_465_cast")];
            tensor<int32, [2]> var_11624 = const()[name = tensor<string, []>("op_11624"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11626 = const()[name = tensor<string, []>("op_11626"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_237_pad_type_0 = const()[name = tensor<string, []>("q_237_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_237_pad_0 = const()[name = tensor<string, []>("q_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1206255360))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1207074624))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_237_cast = conv(dilations = var_11626, groups = var_6865, pad = q_237_pad_0, pad_type = q_237_pad_type_0, strides = var_11624, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_465_cast)[name = tensor<string, []>("q_237_cast")];
            tensor<int32, [2]> var_11630 = const()[name = tensor<string, []>("op_11630"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11632 = const()[name = tensor<string, []>("op_11632"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_237_pad_type_0 = const()[name = tensor<string, []>("k_237_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_237_pad_0 = const()[name = tensor<string, []>("k_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1207074752))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1207894016))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_237_cast = conv(dilations = var_11632, groups = var_6865, pad = k_237_pad_0, pad_type = k_237_pad_type_0, strides = var_11630, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_465_cast)[name = tensor<string, []>("k_237_cast")];
            tensor<int32, [2]> var_11636 = const()[name = tensor<string, []>("op_11636"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11638 = const()[name = tensor<string, []>("op_11638"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_237_pad_type_0 = const()[name = tensor<string, []>("v_237_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_237_pad_0 = const()[name = tensor<string, []>("v_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1207894144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209123008))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_237_cast = conv(dilations = var_11638, groups = var_6865, pad = v_237_pad_0, pad_type = v_237_pad_type_0, strides = var_11636, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_465_cast)[name = tensor<string, []>("v_237_cast")];
            tensor<int32, [4]> var_11642 = const()[name = tensor<string, []>("op_11642"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11643_cast = reshape(shape = var_11642, x = q_237_cast)[name = tensor<string, []>("op_11643_cast")];
            tensor<int32, [4]> var_11644 = const()[name = tensor<string, []>("op_11644"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11645_cast = reshape(shape = var_11644, x = k_237_cast)[name = tensor<string, []>("op_11645_cast")];
            tensor<int32, [4]> var_11646 = const()[name = tensor<string, []>("op_11646"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11647_cast = reshape(shape = var_11646, x = v_237_cast)[name = tensor<string, []>("op_11647_cast")];
            tensor<bool, []> attn_weights_473_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_473_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_473_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_473_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_473_cast = matmul(transpose_x = attn_weights_473_transpose_x_0, transpose_y = attn_weights_473_transpose_y_0, x = var_11643_cast, y = var_11645_cast)[name = tensor<string, []>("attn_weights_473_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_475_cast = mul(x = attn_weights_473_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_475_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_11651_cast = softmax(axis = var_6849, x = attn_weights_475_cast)[name = tensor<string, []>("op_11651_cast")];
            tensor<bool, []> attn_237_transpose_x_0 = const()[name = tensor<string, []>("attn_237_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_237_transpose_y_0 = const()[name = tensor<string, []>("attn_237_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_237_cast = matmul(transpose_x = attn_237_transpose_x_0, transpose_y = attn_237_transpose_y_0, x = var_11647_cast, y = var_11651_cast)[name = tensor<string, []>("attn_237_cast")];
            tensor<int32, [4]> var_11655 = const()[name = tensor<string, []>("op_11655"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_675_cast = reshape(shape = var_11655, x = attn_237_cast)[name = tensor<string, []>("input_675_cast")];
            tensor<int32, [2]> var_11660 = const()[name = tensor<string, []>("op_11660"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11662 = const()[name = tensor<string, []>("op_11662"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11664_pad_type_0 = const()[name = tensor<string, []>("op_11664_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11664_pad_0 = const()[name = tensor<string, []>("op_11664_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209123200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1210352064))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1210352256)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11664_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_11662, groups = var_6865, pad = var_11664_pad_0, pad_type = var_11664_pad_type_0, strides = var_11660, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_675_cast)[name = tensor<string, []>("op_11664_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_357_cast = add(x = var_11664_cast, y = inputs_355_cast)[name = tensor<string, []>("inputs_357_cast")];
            tensor<int32, [1]> var_11668 = const()[name = tensor<string, []>("op_11668"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_357_cast = reduce_mean(axes = var_11668, keep_dims = var_6860, x = inputs_357_cast)[name = tensor<string, []>("channels_mean_357_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_357_cast = sub(x = inputs_357_cast, y = channels_mean_357_cast)[name = tensor<string, []>("zero_mean_357_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_357_cast = mul(x = zero_mean_357_cast, y = zero_mean_357_cast)[name = tensor<string, []>("zero_mean_sq_357_cast")];
            tensor<int32, [1]> var_11672 = const()[name = tensor<string, []>("op_11672"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11673_cast = reduce_mean(axes = var_11672, keep_dims = var_6860, x = zero_mean_sq_357_cast)[name = tensor<string, []>("op_11673_cast")];
            tensor<fp16, []> var_11674_to_fp16 = const()[name = tensor<string, []>("op_11674_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11675_cast = add(x = var_11673_cast, y = var_11674_to_fp16)[name = tensor<string, []>("op_11675_cast")];
            tensor<fp16, []> denom_357_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_357_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_357_cast = rsqrt(epsilon = denom_357_epsilon_0_to_fp16, x = var_11675_cast)[name = tensor<string, []>("denom_357_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_357_cast = mul(x = zero_mean_357_cast, y = denom_357_cast)[name = tensor<string, []>("out_357_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11679_to_fp16 = const()[name = tensor<string, []>("op_11679_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1210354880)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11680_cast = add(x = out_357_cast, y = var_11679_to_fp16)[name = tensor<string, []>("op_11680_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11682_to_fp16 = const()[name = tensor<string, []>("op_11682_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1210357504)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_467_cast = mul(x = var_11680_cast, y = var_11682_to_fp16)[name = tensor<string, []>("hidden_states_467_cast")];
            tensor<int32, [2]> var_11689 = const()[name = tensor<string, []>("op_11689"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11691 = const()[name = tensor<string, []>("op_11691"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_239_pad_type_0 = const()[name = tensor<string, []>("q_239_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_239_pad_0 = const()[name = tensor<string, []>("q_239_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1210360128))), lut = tensor<fp16, [4]>([-0x1.b08p-6, -0x1.0c4p-7, 0x1.09p-7, 0x1.af4p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_239_cast = conv(dilations = var_11691, groups = var_6865, pad = q_239_pad_0, pad_type = q_239_pad_type_0, strides = var_11689, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_467_cast)[name = tensor<string, []>("q_239_cast")];
            tensor<int32, [2]> var_11695 = const()[name = tensor<string, []>("op_11695"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11697 = const()[name = tensor<string, []>("op_11697"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_239_pad_type_0 = const()[name = tensor<string, []>("k_239_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_239_pad_0 = const()[name = tensor<string, []>("k_239_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1210769792))), lut = tensor<fp16, [4]>([-0x1.50cp-6, -0x1.90cp-8, 0x1.8e4p-8, 0x1.504p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_239_cast = conv(dilations = var_11697, groups = var_6865, pad = k_239_pad_0, pad_type = k_239_pad_type_0, strides = var_11695, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_239_cast")];
            tensor<int32, [2]> var_11701 = const()[name = tensor<string, []>("op_11701"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11703 = const()[name = tensor<string, []>("op_11703"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_239_pad_type_0 = const()[name = tensor<string, []>("v_239_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_239_pad_0 = const()[name = tensor<string, []>("v_239_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1211425216))), lut = tensor<fp16, [4]>([-0x1.80cp-6, -0x1.ac8p-8, 0x1.b34p-8, 0x1.82cp-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_239_cast = conv(dilations = var_11703, groups = var_6865, pad = v_239_pad_0, pad_type = v_239_pad_type_0, strides = var_11701, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_239_cast")];
            tensor<int32, [4]> var_11707 = const()[name = tensor<string, []>("op_11707"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11708_cast = reshape(shape = var_11707, x = q_239_cast)[name = tensor<string, []>("op_11708_cast")];
            tensor<int32, [4]> var_11709 = const()[name = tensor<string, []>("op_11709"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11710_cast = reshape(shape = var_11709, x = k_239_cast)[name = tensor<string, []>("op_11710_cast")];
            tensor<int32, [4]> var_11711 = const()[name = tensor<string, []>("op_11711"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11712_cast = reshape(shape = var_11711, x = v_239_cast)[name = tensor<string, []>("op_11712_cast")];
            tensor<bool, []> attn_weights_477_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_477_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_477_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_477_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_477_cast = matmul(transpose_x = attn_weights_477_transpose_x_0, transpose_y = attn_weights_477_transpose_y_0, x = var_11708_cast, y = var_11710_cast)[name = tensor<string, []>("attn_weights_477_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_479_cast = mul(x = attn_weights_477_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_479_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_11716_cast = softmax(axis = var_6849, x = attn_weights_479_cast)[name = tensor<string, []>("op_11716_cast")];
            tensor<bool, []> attn_239_transpose_x_0 = const()[name = tensor<string, []>("attn_239_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_239_transpose_y_0 = const()[name = tensor<string, []>("attn_239_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_239_cast = matmul(transpose_x = attn_239_transpose_x_0, transpose_y = attn_239_transpose_y_0, x = var_11712_cast, y = var_11716_cast)[name = tensor<string, []>("attn_239_cast")];
            tensor<int32, [4]> var_11720 = const()[name = tensor<string, []>("op_11720"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_677_cast = reshape(shape = var_11720, x = attn_239_cast)[name = tensor<string, []>("input_677_cast")];
            tensor<int32, [2]> var_11725 = const()[name = tensor<string, []>("op_11725"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11727 = const()[name = tensor<string, []>("op_11727"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11729_pad_type_0 = const()[name = tensor<string, []>("op_11729_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11729_pad_0 = const()[name = tensor<string, []>("op_11729_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212080640))), lut = tensor<fp16, [2]>([-0x1.7e8p-8, 0x1.7fp-8]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212285504)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11729_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_11727, groups = var_6865, pad = var_11729_pad_0, pad_type = var_11729_pad_type_0, strides = var_11725, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_677_cast)[name = tensor<string, []>("op_11729_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_359_cast = add(x = var_11729_cast, y = inputs_357_cast)[name = tensor<string, []>("inputs_359_cast")];
            tensor<int32, [1]> var_11733 = const()[name = tensor<string, []>("op_11733"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_359_cast = reduce_mean(axes = var_11733, keep_dims = var_6860, x = inputs_359_cast)[name = tensor<string, []>("channels_mean_359_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_359_cast = sub(x = inputs_359_cast, y = channels_mean_359_cast)[name = tensor<string, []>("zero_mean_359_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_359_cast = mul(x = zero_mean_359_cast, y = zero_mean_359_cast)[name = tensor<string, []>("zero_mean_sq_359_cast")];
            tensor<int32, [1]> var_11737 = const()[name = tensor<string, []>("op_11737"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11738_cast = reduce_mean(axes = var_11737, keep_dims = var_6860, x = zero_mean_sq_359_cast)[name = tensor<string, []>("op_11738_cast")];
            tensor<fp16, []> var_11739_to_fp16 = const()[name = tensor<string, []>("op_11739_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11740_cast = add(x = var_11738_cast, y = var_11739_to_fp16)[name = tensor<string, []>("op_11740_cast")];
            tensor<fp16, []> denom_359_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_359_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_359_cast = rsqrt(epsilon = denom_359_epsilon_0_to_fp16, x = var_11740_cast)[name = tensor<string, []>("denom_359_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_359_cast = mul(x = zero_mean_359_cast, y = denom_359_cast)[name = tensor<string, []>("out_359_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11744_to_fp16 = const()[name = tensor<string, []>("op_11744_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212288128)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11745_cast = add(x = out_359_cast, y = var_11744_to_fp16)[name = tensor<string, []>("op_11745_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11747_to_fp16 = const()[name = tensor<string, []>("op_11747_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212290752)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_679_cast = mul(x = var_11745_cast, y = var_11747_to_fp16)[name = tensor<string, []>("input_679_cast")];
            tensor<int32, [2]> var_11755 = const()[name = tensor<string, []>("op_11755"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11757 = const()[name = tensor<string, []>("op_11757"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11759_pad_type_0 = const()[name = tensor<string, []>("op_11759_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11759_pad_0 = const()[name = tensor<string, []>("op_11759_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212293376))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1222123840))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1222124032)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_11759_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_11757, groups = var_6865, pad = var_11759_pad_0, pad_type = var_11759_pad_type_0, strides = var_11755, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_679_cast)[name = tensor<string, []>("op_11759_cast")];
            tensor<int32, [2]> var_11760_split_sizes_0 = const()[name = tensor<string, []>("op_11760_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_11760_axis_0 = const()[name = tensor<string, []>("op_11760_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_11760_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_11760_cast_1 = split(axis = var_11760_axis_0, split_sizes = var_11760_split_sizes_0, x = var_11759_cast)[name = tensor<string, []>("op_11760_cast")];
            tensor<string, []> var_11762_mode_0 = const()[name = tensor<string, []>("op_11762_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_11762_cast = gelu(mode = var_11762_mode_0, x = var_11760_cast_1)[name = tensor<string, []>("op_11762_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_681_cast = mul(x = var_11760_cast_0, y = var_11762_cast)[name = tensor<string, []>("input_681_cast")];
            tensor<int32, [2]> var_11766 = const()[name = tensor<string, []>("op_11766"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11768 = const()[name = tensor<string, []>("op_11768"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11770_pad_type_0 = const()[name = tensor<string, []>("op_11770_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11770_pad_0 = const()[name = tensor<string, []>("op_11770_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1222144576))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227059840))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227060032)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11770_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_11768, groups = var_6865, pad = var_11770_pad_0, pad_type = var_11770_pad_type_0, strides = var_11766, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_681_cast)[name = tensor<string, []>("op_11770_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_361_cast = add(x = var_11770_cast, y = inputs_359_cast)[name = tensor<string, []>("inputs_361_cast")];
            tensor<int32, [1]> var_11780 = const()[name = tensor<string, []>("op_11780"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_361_cast = reduce_mean(axes = var_11780, keep_dims = var_6860, x = inputs_361_cast)[name = tensor<string, []>("channels_mean_361_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_361_cast = sub(x = inputs_361_cast, y = channels_mean_361_cast)[name = tensor<string, []>("zero_mean_361_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_361_cast = mul(x = zero_mean_361_cast, y = zero_mean_361_cast)[name = tensor<string, []>("zero_mean_sq_361_cast")];
            tensor<int32, [1]> var_11784 = const()[name = tensor<string, []>("op_11784"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11785_cast = reduce_mean(axes = var_11784, keep_dims = var_6860, x = zero_mean_sq_361_cast)[name = tensor<string, []>("op_11785_cast")];
            tensor<fp16, []> var_11786_to_fp16 = const()[name = tensor<string, []>("op_11786_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11787_cast = add(x = var_11785_cast, y = var_11786_to_fp16)[name = tensor<string, []>("op_11787_cast")];
            tensor<fp16, []> denom_361_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_361_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_361_cast = rsqrt(epsilon = denom_361_epsilon_0_to_fp16, x = var_11787_cast)[name = tensor<string, []>("denom_361_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_361_cast = mul(x = zero_mean_361_cast, y = denom_361_cast)[name = tensor<string, []>("out_361_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11791_to_fp16 = const()[name = tensor<string, []>("op_11791_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227062656)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11792_cast = add(x = out_361_cast, y = var_11791_to_fp16)[name = tensor<string, []>("op_11792_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11794_to_fp16 = const()[name = tensor<string, []>("op_11794_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227065280)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_471_cast = mul(x = var_11792_cast, y = var_11794_to_fp16)[name = tensor<string, []>("hidden_states_471_cast")];
            tensor<int32, [2]> var_11801 = const()[name = tensor<string, []>("op_11801"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11803 = const()[name = tensor<string, []>("op_11803"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_241_pad_type_0 = const()[name = tensor<string, []>("q_241_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_241_pad_0 = const()[name = tensor<string, []>("q_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227067904))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227887168))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_241_cast = conv(dilations = var_11803, groups = var_6865, pad = q_241_pad_0, pad_type = q_241_pad_type_0, strides = var_11801, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_471_cast)[name = tensor<string, []>("q_241_cast")];
            tensor<int32, [2]> var_11807 = const()[name = tensor<string, []>("op_11807"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11809 = const()[name = tensor<string, []>("op_11809"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_241_pad_type_0 = const()[name = tensor<string, []>("k_241_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_241_pad_0 = const()[name = tensor<string, []>("k_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227887296))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1228706560))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_241_cast = conv(dilations = var_11809, groups = var_6865, pad = k_241_pad_0, pad_type = k_241_pad_type_0, strides = var_11807, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_471_cast)[name = tensor<string, []>("k_241_cast")];
            tensor<int32, [2]> var_11813 = const()[name = tensor<string, []>("op_11813"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11815 = const()[name = tensor<string, []>("op_11815"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_241_pad_type_0 = const()[name = tensor<string, []>("v_241_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_241_pad_0 = const()[name = tensor<string, []>("v_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1228706688))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1229935552))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_241_cast = conv(dilations = var_11815, groups = var_6865, pad = v_241_pad_0, pad_type = v_241_pad_type_0, strides = var_11813, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_471_cast)[name = tensor<string, []>("v_241_cast")];
            tensor<int32, [4]> var_11819 = const()[name = tensor<string, []>("op_11819"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11820_cast = reshape(shape = var_11819, x = q_241_cast)[name = tensor<string, []>("op_11820_cast")];
            tensor<int32, [4]> var_11821 = const()[name = tensor<string, []>("op_11821"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11822_cast = reshape(shape = var_11821, x = k_241_cast)[name = tensor<string, []>("op_11822_cast")];
            tensor<int32, [4]> var_11823 = const()[name = tensor<string, []>("op_11823"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11824_cast = reshape(shape = var_11823, x = v_241_cast)[name = tensor<string, []>("op_11824_cast")];
            tensor<bool, []> attn_weights_481_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_481_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_481_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_481_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_481_cast = matmul(transpose_x = attn_weights_481_transpose_x_0, transpose_y = attn_weights_481_transpose_y_0, x = var_11820_cast, y = var_11822_cast)[name = tensor<string, []>("attn_weights_481_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_483_cast = mul(x = attn_weights_481_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_483_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_11828_cast = softmax(axis = var_6849, x = attn_weights_483_cast)[name = tensor<string, []>("op_11828_cast")];
            tensor<bool, []> attn_241_transpose_x_0 = const()[name = tensor<string, []>("attn_241_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_241_transpose_y_0 = const()[name = tensor<string, []>("attn_241_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_241_cast = matmul(transpose_x = attn_241_transpose_x_0, transpose_y = attn_241_transpose_y_0, x = var_11824_cast, y = var_11828_cast)[name = tensor<string, []>("attn_241_cast")];
            tensor<int32, [4]> var_11832 = const()[name = tensor<string, []>("op_11832"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_683_cast = reshape(shape = var_11832, x = attn_241_cast)[name = tensor<string, []>("input_683_cast")];
            tensor<int32, [2]> var_11837 = const()[name = tensor<string, []>("op_11837"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11839 = const()[name = tensor<string, []>("op_11839"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11841_pad_type_0 = const()[name = tensor<string, []>("op_11841_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11841_pad_0 = const()[name = tensor<string, []>("op_11841_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1229935744))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231164608))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231164800)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11841_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_11839, groups = var_6865, pad = var_11841_pad_0, pad_type = var_11841_pad_type_0, strides = var_11837, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_683_cast)[name = tensor<string, []>("op_11841_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_363_cast = add(x = var_11841_cast, y = inputs_361_cast)[name = tensor<string, []>("inputs_363_cast")];
            tensor<int32, [1]> var_11845 = const()[name = tensor<string, []>("op_11845"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_363_cast = reduce_mean(axes = var_11845, keep_dims = var_6860, x = inputs_363_cast)[name = tensor<string, []>("channels_mean_363_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_363_cast = sub(x = inputs_363_cast, y = channels_mean_363_cast)[name = tensor<string, []>("zero_mean_363_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_363_cast = mul(x = zero_mean_363_cast, y = zero_mean_363_cast)[name = tensor<string, []>("zero_mean_sq_363_cast")];
            tensor<int32, [1]> var_11849 = const()[name = tensor<string, []>("op_11849"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11850_cast = reduce_mean(axes = var_11849, keep_dims = var_6860, x = zero_mean_sq_363_cast)[name = tensor<string, []>("op_11850_cast")];
            tensor<fp16, []> var_11851_to_fp16 = const()[name = tensor<string, []>("op_11851_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11852_cast = add(x = var_11850_cast, y = var_11851_to_fp16)[name = tensor<string, []>("op_11852_cast")];
            tensor<fp16, []> denom_363_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_363_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_363_cast = rsqrt(epsilon = denom_363_epsilon_0_to_fp16, x = var_11852_cast)[name = tensor<string, []>("denom_363_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_363_cast = mul(x = zero_mean_363_cast, y = denom_363_cast)[name = tensor<string, []>("out_363_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11856_to_fp16 = const()[name = tensor<string, []>("op_11856_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231167424)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11857_cast = add(x = out_363_cast, y = var_11856_to_fp16)[name = tensor<string, []>("op_11857_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11859_to_fp16 = const()[name = tensor<string, []>("op_11859_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231170048)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_473_cast = mul(x = var_11857_cast, y = var_11859_to_fp16)[name = tensor<string, []>("hidden_states_473_cast")];
            tensor<int32, [2]> var_11866 = const()[name = tensor<string, []>("op_11866"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11868 = const()[name = tensor<string, []>("op_11868"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_243_pad_type_0 = const()[name = tensor<string, []>("q_243_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_243_pad_0 = const()[name = tensor<string, []>("q_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231172672))), lut = tensor<fp16, [4]>([-0x1.81cp-6, -0x1.e1p-8, 0x1.e7p-8, 0x1.83cp-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_243_cast = conv(dilations = var_11868, groups = var_6865, pad = q_243_pad_0, pad_type = q_243_pad_type_0, strides = var_11866, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_473_cast)[name = tensor<string, []>("q_243_cast")];
            tensor<int32, [2]> var_11872 = const()[name = tensor<string, []>("op_11872"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11874 = const()[name = tensor<string, []>("op_11874"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_243_pad_type_0 = const()[name = tensor<string, []>("k_243_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_243_pad_0 = const()[name = tensor<string, []>("k_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231582336))), lut = tensor<fp16, [4]>([-0x1.22p-6, -0x1.5dp-8, 0x1.5f8p-8, 0x1.224p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_243_cast = conv(dilations = var_11874, groups = var_6865, pad = k_243_pad_0, pad_type = k_243_pad_type_0, strides = var_11872, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_243_cast")];
            tensor<int32, [2]> var_11878 = const()[name = tensor<string, []>("op_11878"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11880 = const()[name = tensor<string, []>("op_11880"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_243_pad_type_0 = const()[name = tensor<string, []>("v_243_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_243_pad_0 = const()[name = tensor<string, []>("v_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1232237760))), lut = tensor<fp16, [4]>([-0x1.4cp-6, -0x1.7bp-8, 0x1.798p-8, 0x1.4b8p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_243_cast = conv(dilations = var_11880, groups = var_6865, pad = v_243_pad_0, pad_type = v_243_pad_type_0, strides = var_11878, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_243_cast")];
            tensor<int32, [4]> var_11884 = const()[name = tensor<string, []>("op_11884"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11885_cast = reshape(shape = var_11884, x = q_243_cast)[name = tensor<string, []>("op_11885_cast")];
            tensor<int32, [4]> var_11886 = const()[name = tensor<string, []>("op_11886"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11887_cast = reshape(shape = var_11886, x = k_243_cast)[name = tensor<string, []>("op_11887_cast")];
            tensor<int32, [4]> var_11888 = const()[name = tensor<string, []>("op_11888"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_11889_cast = reshape(shape = var_11888, x = v_243_cast)[name = tensor<string, []>("op_11889_cast")];
            tensor<bool, []> attn_weights_485_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_485_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_485_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_485_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_485_cast = matmul(transpose_x = attn_weights_485_transpose_x_0, transpose_y = attn_weights_485_transpose_y_0, x = var_11885_cast, y = var_11887_cast)[name = tensor<string, []>("attn_weights_485_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_487_cast = mul(x = attn_weights_485_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_487_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_11893_cast = softmax(axis = var_6849, x = attn_weights_487_cast)[name = tensor<string, []>("op_11893_cast")];
            tensor<bool, []> attn_243_transpose_x_0 = const()[name = tensor<string, []>("attn_243_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_243_transpose_y_0 = const()[name = tensor<string, []>("attn_243_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_243_cast = matmul(transpose_x = attn_243_transpose_x_0, transpose_y = attn_243_transpose_y_0, x = var_11889_cast, y = var_11893_cast)[name = tensor<string, []>("attn_243_cast")];
            tensor<int32, [4]> var_11897 = const()[name = tensor<string, []>("op_11897"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_685_cast = reshape(shape = var_11897, x = attn_243_cast)[name = tensor<string, []>("input_685_cast")];
            tensor<int32, [2]> var_11902 = const()[name = tensor<string, []>("op_11902"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11904 = const()[name = tensor<string, []>("op_11904"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11906_pad_type_0 = const()[name = tensor<string, []>("op_11906_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11906_pad_0 = const()[name = tensor<string, []>("op_11906_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1232893184))), lut = tensor<fp16, [4]>([-0x1.4bcp-7, -0x1.8a8p-9, 0x1.84cp-9, 0x1.4a8p-7]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1233302848)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11906_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_11904, groups = var_6865, pad = var_11906_pad_0, pad_type = var_11906_pad_type_0, strides = var_11902, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_685_cast)[name = tensor<string, []>("op_11906_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_365_cast = add(x = var_11906_cast, y = inputs_363_cast)[name = tensor<string, []>("inputs_365_cast")];
            tensor<int32, [1]> var_11910 = const()[name = tensor<string, []>("op_11910"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_365_cast = reduce_mean(axes = var_11910, keep_dims = var_6860, x = inputs_365_cast)[name = tensor<string, []>("channels_mean_365_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_365_cast = sub(x = inputs_365_cast, y = channels_mean_365_cast)[name = tensor<string, []>("zero_mean_365_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_365_cast = mul(x = zero_mean_365_cast, y = zero_mean_365_cast)[name = tensor<string, []>("zero_mean_sq_365_cast")];
            tensor<int32, [1]> var_11914 = const()[name = tensor<string, []>("op_11914"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11915_cast = reduce_mean(axes = var_11914, keep_dims = var_6860, x = zero_mean_sq_365_cast)[name = tensor<string, []>("op_11915_cast")];
            tensor<fp16, []> var_11916_to_fp16 = const()[name = tensor<string, []>("op_11916_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11917_cast = add(x = var_11915_cast, y = var_11916_to_fp16)[name = tensor<string, []>("op_11917_cast")];
            tensor<fp16, []> denom_365_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_365_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_365_cast = rsqrt(epsilon = denom_365_epsilon_0_to_fp16, x = var_11917_cast)[name = tensor<string, []>("denom_365_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_365_cast = mul(x = zero_mean_365_cast, y = denom_365_cast)[name = tensor<string, []>("out_365_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11921_to_fp16 = const()[name = tensor<string, []>("op_11921_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1233305472)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11922_cast = add(x = out_365_cast, y = var_11921_to_fp16)[name = tensor<string, []>("op_11922_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11924_to_fp16 = const()[name = tensor<string, []>("op_11924_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1233308096)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_687_cast = mul(x = var_11922_cast, y = var_11924_to_fp16)[name = tensor<string, []>("input_687_cast")];
            tensor<int32, [2]> var_11932 = const()[name = tensor<string, []>("op_11932"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11934 = const()[name = tensor<string, []>("op_11934"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11936_pad_type_0 = const()[name = tensor<string, []>("op_11936_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11936_pad_0 = const()[name = tensor<string, []>("op_11936_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1233310720))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1243141184))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1243141376)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_11936_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_11934, groups = var_6865, pad = var_11936_pad_0, pad_type = var_11936_pad_type_0, strides = var_11932, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_687_cast)[name = tensor<string, []>("op_11936_cast")];
            tensor<int32, [2]> var_11937_split_sizes_0 = const()[name = tensor<string, []>("op_11937_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_11937_axis_0 = const()[name = tensor<string, []>("op_11937_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_11937_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_11937_cast_1 = split(axis = var_11937_axis_0, split_sizes = var_11937_split_sizes_0, x = var_11936_cast)[name = tensor<string, []>("op_11937_cast")];
            tensor<string, []> var_11939_mode_0 = const()[name = tensor<string, []>("op_11939_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_11939_cast = gelu(mode = var_11939_mode_0, x = var_11937_cast_1)[name = tensor<string, []>("op_11939_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_689_cast = mul(x = var_11937_cast_0, y = var_11939_cast)[name = tensor<string, []>("input_689_cast")];
            tensor<int32, [2]> var_11943 = const()[name = tensor<string, []>("op_11943"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11945 = const()[name = tensor<string, []>("op_11945"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_11947_pad_type_0 = const()[name = tensor<string, []>("op_11947_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_11947_pad_0 = const()[name = tensor<string, []>("op_11947_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1243161920))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248077184))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248077376)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11947_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_11945, groups = var_6865, pad = var_11947_pad_0, pad_type = var_11947_pad_type_0, strides = var_11943, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_689_cast)[name = tensor<string, []>("op_11947_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_367_cast = add(x = var_11947_cast, y = inputs_365_cast)[name = tensor<string, []>("inputs_367_cast")];
            tensor<int32, [1]> var_11957 = const()[name = tensor<string, []>("op_11957"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_367_cast = reduce_mean(axes = var_11957, keep_dims = var_6860, x = inputs_367_cast)[name = tensor<string, []>("channels_mean_367_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_367_cast = sub(x = inputs_367_cast, y = channels_mean_367_cast)[name = tensor<string, []>("zero_mean_367_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_367_cast = mul(x = zero_mean_367_cast, y = zero_mean_367_cast)[name = tensor<string, []>("zero_mean_sq_367_cast")];
            tensor<int32, [1]> var_11961 = const()[name = tensor<string, []>("op_11961"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_11962_cast = reduce_mean(axes = var_11961, keep_dims = var_6860, x = zero_mean_sq_367_cast)[name = tensor<string, []>("op_11962_cast")];
            tensor<fp16, []> var_11963_to_fp16 = const()[name = tensor<string, []>("op_11963_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_11964_cast = add(x = var_11962_cast, y = var_11963_to_fp16)[name = tensor<string, []>("op_11964_cast")];
            tensor<fp16, []> denom_367_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_367_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_367_cast = rsqrt(epsilon = denom_367_epsilon_0_to_fp16, x = var_11964_cast)[name = tensor<string, []>("denom_367_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_367_cast = mul(x = zero_mean_367_cast, y = denom_367_cast)[name = tensor<string, []>("out_367_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11968_to_fp16 = const()[name = tensor<string, []>("op_11968_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248080000)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_11969_cast = add(x = out_367_cast, y = var_11968_to_fp16)[name = tensor<string, []>("op_11969_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_11971_to_fp16 = const()[name = tensor<string, []>("op_11971_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248082624)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_477_cast = mul(x = var_11969_cast, y = var_11971_to_fp16)[name = tensor<string, []>("hidden_states_477_cast")];
            tensor<int32, [2]> var_11978 = const()[name = tensor<string, []>("op_11978"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11980 = const()[name = tensor<string, []>("op_11980"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_245_pad_type_0 = const()[name = tensor<string, []>("q_245_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_245_pad_0 = const()[name = tensor<string, []>("q_245_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248085248))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248904512))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_245_cast = conv(dilations = var_11980, groups = var_6865, pad = q_245_pad_0, pad_type = q_245_pad_type_0, strides = var_11978, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_477_cast)[name = tensor<string, []>("q_245_cast")];
            tensor<int32, [2]> var_11984 = const()[name = tensor<string, []>("op_11984"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11986 = const()[name = tensor<string, []>("op_11986"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_245_pad_type_0 = const()[name = tensor<string, []>("k_245_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_245_pad_0 = const()[name = tensor<string, []>("k_245_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248904640))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1249723904))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_245_cast = conv(dilations = var_11986, groups = var_6865, pad = k_245_pad_0, pad_type = k_245_pad_type_0, strides = var_11984, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_477_cast)[name = tensor<string, []>("k_245_cast")];
            tensor<int32, [2]> var_11990 = const()[name = tensor<string, []>("op_11990"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_11992 = const()[name = tensor<string, []>("op_11992"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_245_pad_type_0 = const()[name = tensor<string, []>("v_245_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_245_pad_0 = const()[name = tensor<string, []>("v_245_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1249724032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1250952896))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_245_cast = conv(dilations = var_11992, groups = var_6865, pad = v_245_pad_0, pad_type = v_245_pad_type_0, strides = var_11990, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_477_cast)[name = tensor<string, []>("v_245_cast")];
            tensor<int32, [4]> var_11996 = const()[name = tensor<string, []>("op_11996"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11997_cast = reshape(shape = var_11996, x = q_245_cast)[name = tensor<string, []>("op_11997_cast")];
            tensor<int32, [4]> var_11998 = const()[name = tensor<string, []>("op_11998"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_11999_cast = reshape(shape = var_11998, x = k_245_cast)[name = tensor<string, []>("op_11999_cast")];
            tensor<int32, [4]> var_12000 = const()[name = tensor<string, []>("op_12000"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12001_cast = reshape(shape = var_12000, x = v_245_cast)[name = tensor<string, []>("op_12001_cast")];
            tensor<bool, []> attn_weights_489_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_489_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_489_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_489_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_489_cast = matmul(transpose_x = attn_weights_489_transpose_x_0, transpose_y = attn_weights_489_transpose_y_0, x = var_11997_cast, y = var_11999_cast)[name = tensor<string, []>("attn_weights_489_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_491_cast = mul(x = attn_weights_489_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_491_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_12005_cast = softmax(axis = var_6849, x = attn_weights_491_cast)[name = tensor<string, []>("op_12005_cast")];
            tensor<bool, []> attn_245_transpose_x_0 = const()[name = tensor<string, []>("attn_245_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_245_transpose_y_0 = const()[name = tensor<string, []>("attn_245_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_245_cast = matmul(transpose_x = attn_245_transpose_x_0, transpose_y = attn_245_transpose_y_0, x = var_12001_cast, y = var_12005_cast)[name = tensor<string, []>("attn_245_cast")];
            tensor<int32, [4]> var_12009 = const()[name = tensor<string, []>("op_12009"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_691_cast = reshape(shape = var_12009, x = attn_245_cast)[name = tensor<string, []>("input_691_cast")];
            tensor<int32, [2]> var_12014 = const()[name = tensor<string, []>("op_12014"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12016 = const()[name = tensor<string, []>("op_12016"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12018_pad_type_0 = const()[name = tensor<string, []>("op_12018_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12018_pad_0 = const()[name = tensor<string, []>("op_12018_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1250953088))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1252181952))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1252182144)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12018_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_12016, groups = var_6865, pad = var_12018_pad_0, pad_type = var_12018_pad_type_0, strides = var_12014, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_691_cast)[name = tensor<string, []>("op_12018_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_369_cast = add(x = var_12018_cast, y = inputs_367_cast)[name = tensor<string, []>("inputs_369_cast")];
            tensor<int32, [1]> var_12022 = const()[name = tensor<string, []>("op_12022"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_369_cast = reduce_mean(axes = var_12022, keep_dims = var_6860, x = inputs_369_cast)[name = tensor<string, []>("channels_mean_369_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_369_cast = sub(x = inputs_369_cast, y = channels_mean_369_cast)[name = tensor<string, []>("zero_mean_369_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_369_cast = mul(x = zero_mean_369_cast, y = zero_mean_369_cast)[name = tensor<string, []>("zero_mean_sq_369_cast")];
            tensor<int32, [1]> var_12026 = const()[name = tensor<string, []>("op_12026"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12027_cast = reduce_mean(axes = var_12026, keep_dims = var_6860, x = zero_mean_sq_369_cast)[name = tensor<string, []>("op_12027_cast")];
            tensor<fp16, []> var_12028_to_fp16 = const()[name = tensor<string, []>("op_12028_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12029_cast = add(x = var_12027_cast, y = var_12028_to_fp16)[name = tensor<string, []>("op_12029_cast")];
            tensor<fp16, []> denom_369_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_369_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_369_cast = rsqrt(epsilon = denom_369_epsilon_0_to_fp16, x = var_12029_cast)[name = tensor<string, []>("denom_369_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_369_cast = mul(x = zero_mean_369_cast, y = denom_369_cast)[name = tensor<string, []>("out_369_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12033_to_fp16 = const()[name = tensor<string, []>("op_12033_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1252184768)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12034_cast = add(x = out_369_cast, y = var_12033_to_fp16)[name = tensor<string, []>("op_12034_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12036_to_fp16 = const()[name = tensor<string, []>("op_12036_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1252187392)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_479_cast = mul(x = var_12034_cast, y = var_12036_to_fp16)[name = tensor<string, []>("hidden_states_479_cast")];
            tensor<int32, [2]> var_12043 = const()[name = tensor<string, []>("op_12043"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12045 = const()[name = tensor<string, []>("op_12045"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_247_pad_type_0 = const()[name = tensor<string, []>("q_247_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_247_pad_0 = const()[name = tensor<string, []>("q_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1252190016))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1253418880))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_247_cast = conv(dilations = var_12045, groups = var_6865, pad = q_247_pad_0, pad_type = q_247_pad_type_0, strides = var_12043, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_479_cast)[name = tensor<string, []>("q_247_cast")];
            tensor<int32, [2]> var_12049 = const()[name = tensor<string, []>("op_12049"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12051 = const()[name = tensor<string, []>("op_12051"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_247_pad_type_0 = const()[name = tensor<string, []>("k_247_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_247_pad_0 = const()[name = tensor<string, []>("k_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1253419072))), lut = tensor<fp16, [4]>([-0x1.128p-6, -0x1.4dp-8, 0x1.4e8p-8, 0x1.12cp-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_247_cast = conv(dilations = var_12051, groups = var_6865, pad = k_247_pad_0, pad_type = k_247_pad_type_0, strides = var_12049, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_247_cast")];
            tensor<int32, [2]> var_12055 = const()[name = tensor<string, []>("op_12055"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12057 = const()[name = tensor<string, []>("op_12057"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_247_pad_type_0 = const()[name = tensor<string, []>("v_247_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_247_pad_0 = const()[name = tensor<string, []>("v_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1254074496))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1255385280))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_247_cast = conv(dilations = var_12057, groups = var_6865, pad = v_247_pad_0, pad_type = v_247_pad_type_0, strides = var_12055, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_247_cast")];
            tensor<int32, [4]> var_12061 = const()[name = tensor<string, []>("op_12061"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12062_cast = reshape(shape = var_12061, x = q_247_cast)[name = tensor<string, []>("op_12062_cast")];
            tensor<int32, [4]> var_12063 = const()[name = tensor<string, []>("op_12063"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_12064_cast = reshape(shape = var_12063, x = k_247_cast)[name = tensor<string, []>("op_12064_cast")];
            tensor<int32, [4]> var_12065 = const()[name = tensor<string, []>("op_12065"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_12066_cast = reshape(shape = var_12065, x = v_247_cast)[name = tensor<string, []>("op_12066_cast")];
            tensor<bool, []> attn_weights_493_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_493_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_493_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_493_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_493_cast = matmul(transpose_x = attn_weights_493_transpose_x_0, transpose_y = attn_weights_493_transpose_y_0, x = var_12062_cast, y = var_12064_cast)[name = tensor<string, []>("attn_weights_493_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_495_cast = mul(x = attn_weights_493_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_495_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_12070_cast = softmax(axis = var_6849, x = attn_weights_495_cast)[name = tensor<string, []>("op_12070_cast")];
            tensor<bool, []> attn_247_transpose_x_0 = const()[name = tensor<string, []>("attn_247_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_247_transpose_y_0 = const()[name = tensor<string, []>("attn_247_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_247_cast = matmul(transpose_x = attn_247_transpose_x_0, transpose_y = attn_247_transpose_y_0, x = var_12066_cast, y = var_12070_cast)[name = tensor<string, []>("attn_247_cast")];
            tensor<int32, [4]> var_12074 = const()[name = tensor<string, []>("op_12074"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_693_cast = reshape(shape = var_12074, x = attn_247_cast)[name = tensor<string, []>("input_693_cast")];
            tensor<int32, [2]> var_12079 = const()[name = tensor<string, []>("op_12079"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12081 = const()[name = tensor<string, []>("op_12081"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12083_pad_type_0 = const()[name = tensor<string, []>("op_12083_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12083_pad_0 = const()[name = tensor<string, []>("op_12083_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1255385408))), lut = tensor<fp16, [2]>([-0x1.614p-8, 0x1.6p-8]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1255590272)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12083_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_12081, groups = var_6865, pad = var_12083_pad_0, pad_type = var_12083_pad_type_0, strides = var_12079, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_693_cast)[name = tensor<string, []>("op_12083_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_371_cast = add(x = var_12083_cast, y = inputs_369_cast)[name = tensor<string, []>("inputs_371_cast")];
            tensor<int32, [1]> var_12087 = const()[name = tensor<string, []>("op_12087"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_371_cast = reduce_mean(axes = var_12087, keep_dims = var_6860, x = inputs_371_cast)[name = tensor<string, []>("channels_mean_371_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_371_cast = sub(x = inputs_371_cast, y = channels_mean_371_cast)[name = tensor<string, []>("zero_mean_371_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_371_cast = mul(x = zero_mean_371_cast, y = zero_mean_371_cast)[name = tensor<string, []>("zero_mean_sq_371_cast")];
            tensor<int32, [1]> var_12091 = const()[name = tensor<string, []>("op_12091"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12092_cast = reduce_mean(axes = var_12091, keep_dims = var_6860, x = zero_mean_sq_371_cast)[name = tensor<string, []>("op_12092_cast")];
            tensor<fp16, []> var_12093_to_fp16 = const()[name = tensor<string, []>("op_12093_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12094_cast = add(x = var_12092_cast, y = var_12093_to_fp16)[name = tensor<string, []>("op_12094_cast")];
            tensor<fp16, []> denom_371_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_371_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_371_cast = rsqrt(epsilon = denom_371_epsilon_0_to_fp16, x = var_12094_cast)[name = tensor<string, []>("denom_371_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_371_cast = mul(x = zero_mean_371_cast, y = denom_371_cast)[name = tensor<string, []>("out_371_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12098_to_fp16 = const()[name = tensor<string, []>("op_12098_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1255592896)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12099_cast = add(x = out_371_cast, y = var_12098_to_fp16)[name = tensor<string, []>("op_12099_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12101_to_fp16 = const()[name = tensor<string, []>("op_12101_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1255595520)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_695_cast = mul(x = var_12099_cast, y = var_12101_to_fp16)[name = tensor<string, []>("input_695_cast")];
            tensor<int32, [2]> var_12109 = const()[name = tensor<string, []>("op_12109"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12111 = const()[name = tensor<string, []>("op_12111"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12113_pad_type_0 = const()[name = tensor<string, []>("op_12113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12113_pad_0 = const()[name = tensor<string, []>("op_12113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1255598144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1265428608))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1265428800)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_12113_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_12111, groups = var_6865, pad = var_12113_pad_0, pad_type = var_12113_pad_type_0, strides = var_12109, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_695_cast)[name = tensor<string, []>("op_12113_cast")];
            tensor<int32, [2]> var_12114_split_sizes_0 = const()[name = tensor<string, []>("op_12114_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_12114_axis_0 = const()[name = tensor<string, []>("op_12114_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_12114_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_12114_cast_1 = split(axis = var_12114_axis_0, split_sizes = var_12114_split_sizes_0, x = var_12113_cast)[name = tensor<string, []>("op_12114_cast")];
            tensor<string, []> var_12116_mode_0 = const()[name = tensor<string, []>("op_12116_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_12116_cast = gelu(mode = var_12116_mode_0, x = var_12114_cast_1)[name = tensor<string, []>("op_12116_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_697_cast = mul(x = var_12114_cast_0, y = var_12116_cast)[name = tensor<string, []>("input_697_cast")];
            tensor<int32, [2]> var_12120 = const()[name = tensor<string, []>("op_12120"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12122 = const()[name = tensor<string, []>("op_12122"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12124_pad_type_0 = const()[name = tensor<string, []>("op_12124_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12124_pad_0 = const()[name = tensor<string, []>("op_12124_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1265449344))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1270364608))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1270364800)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12124_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_12122, groups = var_6865, pad = var_12124_pad_0, pad_type = var_12124_pad_type_0, strides = var_12120, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_697_cast)[name = tensor<string, []>("op_12124_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_373_cast = add(x = var_12124_cast, y = inputs_371_cast)[name = tensor<string, []>("inputs_373_cast")];
            tensor<int32, [1]> var_12134 = const()[name = tensor<string, []>("op_12134"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_373_cast = reduce_mean(axes = var_12134, keep_dims = var_6860, x = inputs_373_cast)[name = tensor<string, []>("channels_mean_373_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_373_cast = sub(x = inputs_373_cast, y = channels_mean_373_cast)[name = tensor<string, []>("zero_mean_373_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_373_cast = mul(x = zero_mean_373_cast, y = zero_mean_373_cast)[name = tensor<string, []>("zero_mean_sq_373_cast")];
            tensor<int32, [1]> var_12138 = const()[name = tensor<string, []>("op_12138"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12139_cast = reduce_mean(axes = var_12138, keep_dims = var_6860, x = zero_mean_sq_373_cast)[name = tensor<string, []>("op_12139_cast")];
            tensor<fp16, []> var_12140_to_fp16 = const()[name = tensor<string, []>("op_12140_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12141_cast = add(x = var_12139_cast, y = var_12140_to_fp16)[name = tensor<string, []>("op_12141_cast")];
            tensor<fp16, []> denom_373_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_373_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_373_cast = rsqrt(epsilon = denom_373_epsilon_0_to_fp16, x = var_12141_cast)[name = tensor<string, []>("denom_373_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_373_cast = mul(x = zero_mean_373_cast, y = denom_373_cast)[name = tensor<string, []>("out_373_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12145_to_fp16 = const()[name = tensor<string, []>("op_12145_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1270367424)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12146_cast = add(x = out_373_cast, y = var_12145_to_fp16)[name = tensor<string, []>("op_12146_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12148_to_fp16 = const()[name = tensor<string, []>("op_12148_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1270370048)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_483_cast = mul(x = var_12146_cast, y = var_12148_to_fp16)[name = tensor<string, []>("hidden_states_483_cast")];
            tensor<int32, [2]> var_12155 = const()[name = tensor<string, []>("op_12155"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12157 = const()[name = tensor<string, []>("op_12157"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_249_pad_type_0 = const()[name = tensor<string, []>("q_249_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_249_pad_0 = const()[name = tensor<string, []>("q_249_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1270372672))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1271191936))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_249_cast = conv(dilations = var_12157, groups = var_6865, pad = q_249_pad_0, pad_type = q_249_pad_type_0, strides = var_12155, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_483_cast)[name = tensor<string, []>("q_249_cast")];
            tensor<int32, [2]> var_12161 = const()[name = tensor<string, []>("op_12161"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12163 = const()[name = tensor<string, []>("op_12163"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_249_pad_type_0 = const()[name = tensor<string, []>("k_249_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_249_pad_0 = const()[name = tensor<string, []>("k_249_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1271192064))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1272011328))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_249_cast = conv(dilations = var_12163, groups = var_6865, pad = k_249_pad_0, pad_type = k_249_pad_type_0, strides = var_12161, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_483_cast)[name = tensor<string, []>("k_249_cast")];
            tensor<int32, [2]> var_12167 = const()[name = tensor<string, []>("op_12167"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12169 = const()[name = tensor<string, []>("op_12169"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_249_pad_type_0 = const()[name = tensor<string, []>("v_249_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_249_pad_0 = const()[name = tensor<string, []>("v_249_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1272011456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1273240320))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_249_cast = conv(dilations = var_12169, groups = var_6865, pad = v_249_pad_0, pad_type = v_249_pad_type_0, strides = var_12167, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_483_cast)[name = tensor<string, []>("v_249_cast")];
            tensor<int32, [4]> var_12173 = const()[name = tensor<string, []>("op_12173"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12174_cast = reshape(shape = var_12173, x = q_249_cast)[name = tensor<string, []>("op_12174_cast")];
            tensor<int32, [4]> var_12175 = const()[name = tensor<string, []>("op_12175"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12176_cast = reshape(shape = var_12175, x = k_249_cast)[name = tensor<string, []>("op_12176_cast")];
            tensor<int32, [4]> var_12177 = const()[name = tensor<string, []>("op_12177"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12178_cast = reshape(shape = var_12177, x = v_249_cast)[name = tensor<string, []>("op_12178_cast")];
            tensor<bool, []> attn_weights_497_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_497_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_497_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_497_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_497_cast = matmul(transpose_x = attn_weights_497_transpose_x_0, transpose_y = attn_weights_497_transpose_y_0, x = var_12174_cast, y = var_12176_cast)[name = tensor<string, []>("attn_weights_497_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_499_cast = mul(x = attn_weights_497_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_499_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_12182_cast = softmax(axis = var_6849, x = attn_weights_499_cast)[name = tensor<string, []>("op_12182_cast")];
            tensor<bool, []> attn_249_transpose_x_0 = const()[name = tensor<string, []>("attn_249_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_249_transpose_y_0 = const()[name = tensor<string, []>("attn_249_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_249_cast = matmul(transpose_x = attn_249_transpose_x_0, transpose_y = attn_249_transpose_y_0, x = var_12178_cast, y = var_12182_cast)[name = tensor<string, []>("attn_249_cast")];
            tensor<int32, [4]> var_12186 = const()[name = tensor<string, []>("op_12186"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_699_cast = reshape(shape = var_12186, x = attn_249_cast)[name = tensor<string, []>("input_699_cast")];
            tensor<int32, [2]> var_12191 = const()[name = tensor<string, []>("op_12191"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12193 = const()[name = tensor<string, []>("op_12193"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12195_pad_type_0 = const()[name = tensor<string, []>("op_12195_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12195_pad_0 = const()[name = tensor<string, []>("op_12195_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1273240512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1274469376))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1274469568)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12195_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_12193, groups = var_6865, pad = var_12195_pad_0, pad_type = var_12195_pad_type_0, strides = var_12191, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_699_cast)[name = tensor<string, []>("op_12195_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_375_cast = add(x = var_12195_cast, y = inputs_373_cast)[name = tensor<string, []>("inputs_375_cast")];
            tensor<int32, [1]> var_12199 = const()[name = tensor<string, []>("op_12199"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_375_cast = reduce_mean(axes = var_12199, keep_dims = var_6860, x = inputs_375_cast)[name = tensor<string, []>("channels_mean_375_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_375_cast = sub(x = inputs_375_cast, y = channels_mean_375_cast)[name = tensor<string, []>("zero_mean_375_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_375_cast = mul(x = zero_mean_375_cast, y = zero_mean_375_cast)[name = tensor<string, []>("zero_mean_sq_375_cast")];
            tensor<int32, [1]> var_12203 = const()[name = tensor<string, []>("op_12203"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12204_cast = reduce_mean(axes = var_12203, keep_dims = var_6860, x = zero_mean_sq_375_cast)[name = tensor<string, []>("op_12204_cast")];
            tensor<fp16, []> var_12205_to_fp16 = const()[name = tensor<string, []>("op_12205_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12206_cast = add(x = var_12204_cast, y = var_12205_to_fp16)[name = tensor<string, []>("op_12206_cast")];
            tensor<fp16, []> denom_375_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_375_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_375_cast = rsqrt(epsilon = denom_375_epsilon_0_to_fp16, x = var_12206_cast)[name = tensor<string, []>("denom_375_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_375_cast = mul(x = zero_mean_375_cast, y = denom_375_cast)[name = tensor<string, []>("out_375_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12210_to_fp16 = const()[name = tensor<string, []>("op_12210_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1274472192)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12211_cast = add(x = out_375_cast, y = var_12210_to_fp16)[name = tensor<string, []>("op_12211_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12213_to_fp16 = const()[name = tensor<string, []>("op_12213_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1274474816)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_485_cast = mul(x = var_12211_cast, y = var_12213_to_fp16)[name = tensor<string, []>("hidden_states_485_cast")];
            tensor<int32, [2]> var_12220 = const()[name = tensor<string, []>("op_12220"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12222 = const()[name = tensor<string, []>("op_12222"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_251_pad_type_0 = const()[name = tensor<string, []>("q_251_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_251_pad_0 = const()[name = tensor<string, []>("q_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1274477440))), lut = tensor<fp16, [4]>([-0x1.648p-6, -0x1.c48p-8, 0x1.c1p-8, 0x1.644p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_251_cast = conv(dilations = var_12222, groups = var_6865, pad = q_251_pad_0, pad_type = q_251_pad_type_0, strides = var_12220, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_485_cast)[name = tensor<string, []>("q_251_cast")];
            tensor<int32, [2]> var_12226 = const()[name = tensor<string, []>("op_12226"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12228 = const()[name = tensor<string, []>("op_12228"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_251_pad_type_0 = const()[name = tensor<string, []>("k_251_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_251_pad_0 = const()[name = tensor<string, []>("k_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1274887104))), lut = tensor<fp16, [4]>([-0x1.efp-7, -0x1.328p-8, 0x1.318p-8, 0x1.ee8p-7]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_251_cast = conv(dilations = var_12228, groups = var_6865, pad = k_251_pad_0, pad_type = k_251_pad_type_0, strides = var_12226, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_251_cast")];
            tensor<int32, [2]> var_12232 = const()[name = tensor<string, []>("op_12232"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12234 = const()[name = tensor<string, []>("op_12234"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_251_pad_type_0 = const()[name = tensor<string, []>("v_251_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_251_pad_0 = const()[name = tensor<string, []>("v_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1275542528))), lut = tensor<fp16, [4]>([-0x1.1b8p-6, -0x1.4bcp-8, 0x1.478p-8, 0x1.1a8p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_251_cast = conv(dilations = var_12234, groups = var_6865, pad = v_251_pad_0, pad_type = v_251_pad_type_0, strides = var_12232, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_251_cast")];
            tensor<int32, [4]> var_12238 = const()[name = tensor<string, []>("op_12238"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12239_cast = reshape(shape = var_12238, x = q_251_cast)[name = tensor<string, []>("op_12239_cast")];
            tensor<int32, [4]> var_12240 = const()[name = tensor<string, []>("op_12240"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_12241_cast = reshape(shape = var_12240, x = k_251_cast)[name = tensor<string, []>("op_12241_cast")];
            tensor<int32, [4]> var_12242 = const()[name = tensor<string, []>("op_12242"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_12243_cast = reshape(shape = var_12242, x = v_251_cast)[name = tensor<string, []>("op_12243_cast")];
            tensor<bool, []> attn_weights_501_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_501_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_501_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_501_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_501_cast = matmul(transpose_x = attn_weights_501_transpose_x_0, transpose_y = attn_weights_501_transpose_y_0, x = var_12239_cast, y = var_12241_cast)[name = tensor<string, []>("attn_weights_501_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_503_cast = mul(x = attn_weights_501_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_503_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_12247_cast = softmax(axis = var_6849, x = attn_weights_503_cast)[name = tensor<string, []>("op_12247_cast")];
            tensor<bool, []> attn_251_transpose_x_0 = const()[name = tensor<string, []>("attn_251_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_251_transpose_y_0 = const()[name = tensor<string, []>("attn_251_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_251_cast = matmul(transpose_x = attn_251_transpose_x_0, transpose_y = attn_251_transpose_y_0, x = var_12243_cast, y = var_12247_cast)[name = tensor<string, []>("attn_251_cast")];
            tensor<int32, [4]> var_12251 = const()[name = tensor<string, []>("op_12251"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_701_cast = reshape(shape = var_12251, x = attn_251_cast)[name = tensor<string, []>("input_701_cast")];
            tensor<int32, [2]> var_12256 = const()[name = tensor<string, []>("op_12256"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12258 = const()[name = tensor<string, []>("op_12258"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12260_pad_type_0 = const()[name = tensor<string, []>("op_12260_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12260_pad_0 = const()[name = tensor<string, []>("op_12260_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1276197952))), lut = tensor<fp16, [2]>([-0x1.478p-8, 0x1.488p-8]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1276402816)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12260_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_12258, groups = var_6865, pad = var_12260_pad_0, pad_type = var_12260_pad_type_0, strides = var_12256, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_701_cast)[name = tensor<string, []>("op_12260_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_377_cast = add(x = var_12260_cast, y = inputs_375_cast)[name = tensor<string, []>("inputs_377_cast")];
            tensor<int32, [1]> var_12264 = const()[name = tensor<string, []>("op_12264"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_377_cast = reduce_mean(axes = var_12264, keep_dims = var_6860, x = inputs_377_cast)[name = tensor<string, []>("channels_mean_377_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_377_cast = sub(x = inputs_377_cast, y = channels_mean_377_cast)[name = tensor<string, []>("zero_mean_377_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_377_cast = mul(x = zero_mean_377_cast, y = zero_mean_377_cast)[name = tensor<string, []>("zero_mean_sq_377_cast")];
            tensor<int32, [1]> var_12268 = const()[name = tensor<string, []>("op_12268"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12269_cast = reduce_mean(axes = var_12268, keep_dims = var_6860, x = zero_mean_sq_377_cast)[name = tensor<string, []>("op_12269_cast")];
            tensor<fp16, []> var_12270_to_fp16 = const()[name = tensor<string, []>("op_12270_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12271_cast = add(x = var_12269_cast, y = var_12270_to_fp16)[name = tensor<string, []>("op_12271_cast")];
            tensor<fp16, []> denom_377_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_377_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_377_cast = rsqrt(epsilon = denom_377_epsilon_0_to_fp16, x = var_12271_cast)[name = tensor<string, []>("denom_377_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_377_cast = mul(x = zero_mean_377_cast, y = denom_377_cast)[name = tensor<string, []>("out_377_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12275_to_fp16 = const()[name = tensor<string, []>("op_12275_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1276405440)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12276_cast = add(x = out_377_cast, y = var_12275_to_fp16)[name = tensor<string, []>("op_12276_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12278_to_fp16 = const()[name = tensor<string, []>("op_12278_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1276408064)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_703_cast = mul(x = var_12276_cast, y = var_12278_to_fp16)[name = tensor<string, []>("input_703_cast")];
            tensor<int32, [2]> var_12286 = const()[name = tensor<string, []>("op_12286"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12288 = const()[name = tensor<string, []>("op_12288"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12290_pad_type_0 = const()[name = tensor<string, []>("op_12290_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12290_pad_0 = const()[name = tensor<string, []>("op_12290_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1276410688))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1286241152))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1286241344)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_12290_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_12288, groups = var_6865, pad = var_12290_pad_0, pad_type = var_12290_pad_type_0, strides = var_12286, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_703_cast)[name = tensor<string, []>("op_12290_cast")];
            tensor<int32, [2]> var_12291_split_sizes_0 = const()[name = tensor<string, []>("op_12291_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_12291_axis_0 = const()[name = tensor<string, []>("op_12291_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_12291_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_12291_cast_1 = split(axis = var_12291_axis_0, split_sizes = var_12291_split_sizes_0, x = var_12290_cast)[name = tensor<string, []>("op_12291_cast")];
            tensor<string, []> var_12293_mode_0 = const()[name = tensor<string, []>("op_12293_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_12293_cast = gelu(mode = var_12293_mode_0, x = var_12291_cast_1)[name = tensor<string, []>("op_12293_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_705_cast = mul(x = var_12291_cast_0, y = var_12293_cast)[name = tensor<string, []>("input_705_cast")];
            tensor<int32, [2]> var_12297 = const()[name = tensor<string, []>("op_12297"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12299 = const()[name = tensor<string, []>("op_12299"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12301_pad_type_0 = const()[name = tensor<string, []>("op_12301_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12301_pad_0 = const()[name = tensor<string, []>("op_12301_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1286261888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291177152))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291177344)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12301_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_12299, groups = var_6865, pad = var_12301_pad_0, pad_type = var_12301_pad_type_0, strides = var_12297, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_705_cast)[name = tensor<string, []>("op_12301_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_379_cast = add(x = var_12301_cast, y = inputs_377_cast)[name = tensor<string, []>("inputs_379_cast")];
            tensor<int32, [1]> var_12311 = const()[name = tensor<string, []>("op_12311"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_379_cast = reduce_mean(axes = var_12311, keep_dims = var_6860, x = inputs_379_cast)[name = tensor<string, []>("channels_mean_379_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_379_cast = sub(x = inputs_379_cast, y = channels_mean_379_cast)[name = tensor<string, []>("zero_mean_379_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_379_cast = mul(x = zero_mean_379_cast, y = zero_mean_379_cast)[name = tensor<string, []>("zero_mean_sq_379_cast")];
            tensor<int32, [1]> var_12315 = const()[name = tensor<string, []>("op_12315"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12316_cast = reduce_mean(axes = var_12315, keep_dims = var_6860, x = zero_mean_sq_379_cast)[name = tensor<string, []>("op_12316_cast")];
            tensor<fp16, []> var_12317_to_fp16 = const()[name = tensor<string, []>("op_12317_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12318_cast = add(x = var_12316_cast, y = var_12317_to_fp16)[name = tensor<string, []>("op_12318_cast")];
            tensor<fp16, []> denom_379_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_379_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_379_cast = rsqrt(epsilon = denom_379_epsilon_0_to_fp16, x = var_12318_cast)[name = tensor<string, []>("denom_379_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_379_cast = mul(x = zero_mean_379_cast, y = denom_379_cast)[name = tensor<string, []>("out_379_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12322_to_fp16 = const()[name = tensor<string, []>("op_12322_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291179968)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12323_cast = add(x = out_379_cast, y = var_12322_to_fp16)[name = tensor<string, []>("op_12323_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12325_to_fp16 = const()[name = tensor<string, []>("op_12325_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291182592)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_489_cast = mul(x = var_12323_cast, y = var_12325_to_fp16)[name = tensor<string, []>("hidden_states_489_cast")];
            tensor<int32, [2]> var_12332 = const()[name = tensor<string, []>("op_12332"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12334 = const()[name = tensor<string, []>("op_12334"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_253_pad_type_0 = const()[name = tensor<string, []>("q_253_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_253_pad_0 = const()[name = tensor<string, []>("q_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291185216))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1292004480))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_253_cast = conv(dilations = var_12334, groups = var_6865, pad = q_253_pad_0, pad_type = q_253_pad_type_0, strides = var_12332, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_489_cast)[name = tensor<string, []>("q_253_cast")];
            tensor<int32, [2]> var_12338 = const()[name = tensor<string, []>("op_12338"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12340 = const()[name = tensor<string, []>("op_12340"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_253_pad_type_0 = const()[name = tensor<string, []>("k_253_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_253_pad_0 = const()[name = tensor<string, []>("k_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1292004608))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1292823872))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> k_253_cast = conv(dilations = var_12340, groups = var_6865, pad = k_253_pad_0, pad_type = k_253_pad_type_0, strides = var_12338, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_489_cast)[name = tensor<string, []>("k_253_cast")];
            tensor<int32, [2]> var_12344 = const()[name = tensor<string, []>("op_12344"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12346 = const()[name = tensor<string, []>("op_12346"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_253_pad_type_0 = const()[name = tensor<string, []>("v_253_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_253_pad_0 = const()[name = tensor<string, []>("v_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1292824000))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1294052864))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> v_253_cast = conv(dilations = var_12346, groups = var_6865, pad = v_253_pad_0, pad_type = v_253_pad_type_0, strides = var_12344, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_489_cast)[name = tensor<string, []>("v_253_cast")];
            tensor<int32, [4]> var_12350 = const()[name = tensor<string, []>("op_12350"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12351_cast = reshape(shape = var_12350, x = q_253_cast)[name = tensor<string, []>("op_12351_cast")];
            tensor<int32, [4]> var_12352 = const()[name = tensor<string, []>("op_12352"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12353_cast = reshape(shape = var_12352, x = k_253_cast)[name = tensor<string, []>("op_12353_cast")];
            tensor<int32, [4]> var_12354 = const()[name = tensor<string, []>("op_12354"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12355_cast = reshape(shape = var_12354, x = v_253_cast)[name = tensor<string, []>("op_12355_cast")];
            tensor<bool, []> attn_weights_505_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_505_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_505_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_505_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_505_cast = matmul(transpose_x = attn_weights_505_transpose_x_0, transpose_y = attn_weights_505_transpose_y_0, x = var_12351_cast, y = var_12353_cast)[name = tensor<string, []>("attn_weights_505_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> attn_weights_507_cast = mul(x = attn_weights_505_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_507_cast")];
            tensor<fp16, [2, 20, 1024, 1024]> var_12359_cast = softmax(axis = var_6849, x = attn_weights_507_cast)[name = tensor<string, []>("op_12359_cast")];
            tensor<bool, []> attn_253_transpose_x_0 = const()[name = tensor<string, []>("attn_253_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_253_transpose_y_0 = const()[name = tensor<string, []>("attn_253_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_253_cast = matmul(transpose_x = attn_253_transpose_x_0, transpose_y = attn_253_transpose_y_0, x = var_12355_cast, y = var_12359_cast)[name = tensor<string, []>("attn_253_cast")];
            tensor<int32, [4]> var_12363 = const()[name = tensor<string, []>("op_12363"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_707_cast = reshape(shape = var_12363, x = attn_253_cast)[name = tensor<string, []>("input_707_cast")];
            tensor<int32, [2]> var_12368 = const()[name = tensor<string, []>("op_12368"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12370 = const()[name = tensor<string, []>("op_12370"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12372_pad_type_0 = const()[name = tensor<string, []>("op_12372_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12372_pad_0 = const()[name = tensor<string, []>("op_12372_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1294053056))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1295281920))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1295282112)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12372_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_12370, groups = var_6865, pad = var_12372_pad_0, pad_type = var_12372_pad_type_0, strides = var_12368, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_707_cast)[name = tensor<string, []>("op_12372_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_381_cast = add(x = var_12372_cast, y = inputs_379_cast)[name = tensor<string, []>("inputs_381_cast")];
            tensor<int32, [1]> var_12376 = const()[name = tensor<string, []>("op_12376"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_381_cast = reduce_mean(axes = var_12376, keep_dims = var_6860, x = inputs_381_cast)[name = tensor<string, []>("channels_mean_381_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_381_cast = sub(x = inputs_381_cast, y = channels_mean_381_cast)[name = tensor<string, []>("zero_mean_381_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_381_cast = mul(x = zero_mean_381_cast, y = zero_mean_381_cast)[name = tensor<string, []>("zero_mean_sq_381_cast")];
            tensor<int32, [1]> var_12380 = const()[name = tensor<string, []>("op_12380"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12381_cast = reduce_mean(axes = var_12380, keep_dims = var_6860, x = zero_mean_sq_381_cast)[name = tensor<string, []>("op_12381_cast")];
            tensor<fp16, []> var_12382_to_fp16 = const()[name = tensor<string, []>("op_12382_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12383_cast = add(x = var_12381_cast, y = var_12382_to_fp16)[name = tensor<string, []>("op_12383_cast")];
            tensor<fp16, []> denom_381_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_381_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_381_cast = rsqrt(epsilon = denom_381_epsilon_0_to_fp16, x = var_12383_cast)[name = tensor<string, []>("denom_381_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_381_cast = mul(x = zero_mean_381_cast, y = denom_381_cast)[name = tensor<string, []>("out_381_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12387_to_fp16 = const()[name = tensor<string, []>("op_12387_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1295284736)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12388_cast = add(x = out_381_cast, y = var_12387_to_fp16)[name = tensor<string, []>("op_12388_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12390_to_fp16 = const()[name = tensor<string, []>("op_12390_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1295287360)))];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_491_cast = mul(x = var_12388_cast, y = var_12390_to_fp16)[name = tensor<string, []>("hidden_states_491_cast")];
            tensor<int32, [2]> var_12397 = const()[name = tensor<string, []>("op_12397"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12399 = const()[name = tensor<string, []>("op_12399"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_255_pad_type_0 = const()[name = tensor<string, []>("q_255_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_255_pad_0 = const()[name = tensor<string, []>("q_255_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1295289984))), lut = tensor<fp16, [4]>([-0x1.698p-6, -0x1.c78p-8, 0x1.c54p-8, 0x1.688p-6]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [2, 1280, 1, 1024]> q_255_cast = conv(dilations = var_12399, groups = var_6865, pad = q_255_pad_0, pad_type = q_255_pad_type_0, strides = var_12397, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_491_cast)[name = tensor<string, []>("q_255_cast")];
            tensor<int32, [2]> var_12403 = const()[name = tensor<string, []>("op_12403"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12405 = const()[name = tensor<string, []>("op_12405"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_255_pad_type_0 = const()[name = tensor<string, []>("k_255_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_255_pad_0 = const()[name = tensor<string, []>("k_255_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [327680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1295699648))), lut = tensor<fp16, [2]>([-0x1.fdcp-8, 0x1.fep-8]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_255_cast = conv(dilations = var_12405, groups = var_6865, pad = k_255_pad_0, pad_type = k_255_pad_type_0, strides = var_12403, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_255_cast")];
            tensor<int32, [2]> var_12409 = const()[name = tensor<string, []>("op_12409"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12411 = const()[name = tensor<string, []>("op_12411"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_255_pad_type_0 = const()[name = tensor<string, []>("v_255_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_255_pad_0 = const()[name = tensor<string, []>("v_255_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2048, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1296027392))), lut = tensor<fp16, [4]>([-0x1.e5cp-7, -0x1.21cp-8, 0x1.20cp-8, 0x1.e54p-7]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2048, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_255_cast = conv(dilations = var_12411, groups = var_6865, pad = v_255_pad_0, pad_type = v_255_pad_type_0, strides = var_12409, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_255_cast")];
            tensor<int32, [4]> var_12415 = const()[name = tensor<string, []>("op_12415"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 1024]> var_12416_cast = reshape(shape = var_12415, x = q_255_cast)[name = tensor<string, []>("op_12416_cast")];
            tensor<int32, [4]> var_12417 = const()[name = tensor<string, []>("op_12417"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_12418_cast = reshape(shape = var_12417, x = k_255_cast)[name = tensor<string, []>("op_12418_cast")];
            tensor<int32, [4]> var_12419 = const()[name = tensor<string, []>("op_12419"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_12420_cast = reshape(shape = var_12419, x = v_255_cast)[name = tensor<string, []>("op_12420_cast")];
            tensor<bool, []> attn_weights_509_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_509_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_509_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_509_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_509_cast = matmul(transpose_x = attn_weights_509_transpose_x_0, transpose_y = attn_weights_509_transpose_y_0, x = var_12416_cast, y = var_12418_cast)[name = tensor<string, []>("attn_weights_509_cast")];
            tensor<fp16, [2, 20, 1024, 77]> attn_weights_511_cast = mul(x = attn_weights_509_cast, y = var_6856_to_fp16)[name = tensor<string, []>("attn_weights_511_cast")];
            tensor<fp16, [2, 20, 1024, 77]> var_12424_cast = softmax(axis = var_6849, x = attn_weights_511_cast)[name = tensor<string, []>("op_12424_cast")];
            tensor<bool, []> attn_255_transpose_x_0 = const()[name = tensor<string, []>("attn_255_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_255_transpose_y_0 = const()[name = tensor<string, []>("attn_255_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 20, 64, 1024]> attn_255_cast = matmul(transpose_x = attn_255_transpose_x_0, transpose_y = attn_255_transpose_y_0, x = var_12420_cast, y = var_12424_cast)[name = tensor<string, []>("attn_255_cast")];
            tensor<int32, [4]> var_12428 = const()[name = tensor<string, []>("op_12428"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 1024]> input_709_cast = reshape(shape = var_12428, x = attn_255_cast)[name = tensor<string, []>("input_709_cast")];
            tensor<int32, [2]> var_12433 = const()[name = tensor<string, []>("op_12433"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12435 = const()[name = tensor<string, []>("op_12435"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12437_pad_type_0 = const()[name = tensor<string, []>("op_12437_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12437_pad_0 = const()[name = tensor<string, []>("op_12437_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1296682816))), lut = tensor<fp16, [2]>([-0x1.414p-8, 0x1.42p-8]), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1296887680)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12437_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_12435, groups = var_6865, pad = var_12437_pad_0, pad_type = var_12437_pad_type_0, strides = var_12433, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_709_cast)[name = tensor<string, []>("op_12437_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> inputs_383_cast = add(x = var_12437_cast, y = inputs_381_cast)[name = tensor<string, []>("inputs_383_cast")];
            tensor<int32, [1]> var_12441 = const()[name = tensor<string, []>("op_12441"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_383_cast = reduce_mean(axes = var_12441, keep_dims = var_6860, x = inputs_383_cast)[name = tensor<string, []>("channels_mean_383_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_383_cast = sub(x = inputs_383_cast, y = channels_mean_383_cast)[name = tensor<string, []>("zero_mean_383_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> zero_mean_sq_383_cast = mul(x = zero_mean_383_cast, y = zero_mean_383_cast)[name = tensor<string, []>("zero_mean_sq_383_cast")];
            tensor<int32, [1]> var_12445 = const()[name = tensor<string, []>("op_12445"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_12446_cast = reduce_mean(axes = var_12445, keep_dims = var_6860, x = zero_mean_sq_383_cast)[name = tensor<string, []>("op_12446_cast")];
            tensor<fp16, []> var_12447_to_fp16 = const()[name = tensor<string, []>("op_12447_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_12448_cast = add(x = var_12446_cast, y = var_12447_to_fp16)[name = tensor<string, []>("op_12448_cast")];
            tensor<fp16, []> denom_383_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_383_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 1024]> denom_383_cast = rsqrt(epsilon = denom_383_epsilon_0_to_fp16, x = var_12448_cast)[name = tensor<string, []>("denom_383_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> out_383_cast = mul(x = zero_mean_383_cast, y = denom_383_cast)[name = tensor<string, []>("out_383_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12452_to_fp16 = const()[name = tensor<string, []>("op_12452_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1296890304)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12453_cast = add(x = out_383_cast, y = var_12452_to_fp16)[name = tensor<string, []>("op_12453_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_12455_to_fp16 = const()[name = tensor<string, []>("op_12455_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1296892928)))];
            tensor<fp16, [2, 1280, 1, 1024]> input_711_cast = mul(x = var_12453_cast, y = var_12455_to_fp16)[name = tensor<string, []>("input_711_cast")];
            tensor<int32, [2]> var_12463 = const()[name = tensor<string, []>("op_12463"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12465 = const()[name = tensor<string, []>("op_12465"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12467_pad_type_0 = const()[name = tensor<string, []>("op_12467_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12467_pad_0 = const()[name = tensor<string, []>("op_12467_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1296895552))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1306726016))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([10240, 1280, 1, 1])];
            tensor<fp16, [10240]> up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1306726208)))];
            tensor<fp16, [2, 10240, 1, 1024]> var_12467_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_12465, groups = var_6865, pad = var_12467_pad_0, pad_type = var_12467_pad_type_0, strides = var_12463, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_711_cast)[name = tensor<string, []>("op_12467_cast")];
            tensor<int32, [2]> var_12468_split_sizes_0 = const()[name = tensor<string, []>("op_12468_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_12468_axis_0 = const()[name = tensor<string, []>("op_12468_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 1024]> var_12468_cast_0, tensor<fp16, [2, 5120, 1, 1024]> var_12468_cast_1 = split(axis = var_12468_axis_0, split_sizes = var_12468_split_sizes_0, x = var_12467_cast)[name = tensor<string, []>("op_12468_cast")];
            tensor<string, []> var_12470_mode_0 = const()[name = tensor<string, []>("op_12470_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 1024]> var_12470_cast = gelu(mode = var_12470_mode_0, x = var_12468_cast_1)[name = tensor<string, []>("op_12470_cast")];
            tensor<fp16, [2, 5120, 1, 1024]> input_713_cast = mul(x = var_12468_cast_0, y = var_12470_cast)[name = tensor<string, []>("input_713_cast")];
            tensor<int32, [2]> var_12474 = const()[name = tensor<string, []>("op_12474"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12476 = const()[name = tensor<string, []>("op_12476"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12478_pad_type_0 = const()[name = tensor<string, []>("op_12478_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12478_pad_0 = const()[name = tensor<string, []>("op_12478_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1306746752))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1311662016))), name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 5120, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1311662208)))];
            tensor<fp16, [2, 1280, 1, 1024]> var_12478_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_12476, groups = var_6865, pad = var_12478_pad_0, pad_type = var_12478_pad_type_0, strides = var_12474, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_713_cast)[name = tensor<string, []>("op_12478_cast")];
            tensor<fp16, [2, 1280, 1, 1024]> hidden_states_495_cast = add(x = var_12478_cast, y = inputs_383_cast)[name = tensor<string, []>("hidden_states_495_cast")];
            tensor<int32, [4]> var_12480 = const()[name = tensor<string, []>("op_12480"), val = tensor<int32, [4]>([2, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> input_715_cast = reshape(shape = var_12480, x = hidden_states_495_cast)[name = tensor<string, []>("input_715_cast")];
            tensor<int32, [2]> var_12484 = const()[name = tensor<string, []>("op_12484"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12486 = const()[name = tensor<string, []>("op_12486"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_497_pad_type_0 = const()[name = tensor<string, []>("hidden_states_497_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_497_pad_0 = const()[name = tensor<string, []>("hidden_states_497_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1311664832))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312893696))), name = tensor<string, []>("up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> up_blocks_0_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_attentions_2_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312893888)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_497_cast = conv(bias = up_blocks_0_attentions_2_proj_out_bias_to_fp16, dilations = var_12486, groups = var_6865, pad = hidden_states_497_pad_0, pad_type = hidden_states_497_pad_type_0, strides = var_12484, weight = up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized, x = input_715_cast)[name = tensor<string, []>("hidden_states_497_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_717_cast = add(x = hidden_states_497_cast, y = hidden_states_431_cast)[name = tensor<string, []>("input_717_cast")];
            tensor<fp32, []> input_719_scale_factor_height_0 = const()[name = tensor<string, []>("input_719_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, []> input_719_scale_factor_width_0 = const()[name = tensor<string, []>("input_719_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp16, [2, 1280, 64, 64]> input_719_cast = upsample_nearest_neighbor(scale_factor_height = input_719_scale_factor_height_0, scale_factor_width = input_719_scale_factor_width_0, x = input_717_cast)[name = tensor<string, []>("input_719_cast")];
            tensor<int32, [2]> var_12495 = const()[name = tensor<string, []>("op_12495"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12497 = const()[name = tensor<string, []>("op_12497"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_499_pad_type_0 = const()[name = tensor<string, []>("hidden_states_499_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_499_pad_0 = const()[name = tensor<string, []>("hidden_states_499_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [14745600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312896512))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1327642176))), name = tensor<string, []>("up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1327642752)))];
            tensor<fp16, [2, 1280, 64, 64]> hidden_states_499_cast = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_12497, groups = var_6865, pad = hidden_states_499_pad_0, pad_type = hidden_states_499_pad_type_0, strides = var_12495, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized, x = input_719_cast)[name = tensor<string, []>("hidden_states_499_cast")];
            tensor<int32, []> var_12502 = const()[name = tensor<string, []>("op_12502"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_12513 = const()[name = tensor<string, []>("op_12513"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_12518 = const()[name = tensor<string, []>("op_12518"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_721_interleave_0 = const()[name = tensor<string, []>("input_721_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 1920, 64, 64]> input_721_cast = concat(axis = var_12518, interleave = input_721_interleave_0, values = (hidden_states_499_cast, input_113_cast))[name = tensor<string, []>("input_721_cast")];
            tensor<int32, [5]> reshape_120_shape_0 = const()[name = tensor<string, []>("reshape_120_shape_0"), val = tensor<int32, [5]>([2, 32, 60, 64, 64])];
            tensor<fp16, [2, 32, 60, 64, 64]> reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_721_cast)[name = tensor<string, []>("reshape_120_cast")];
            tensor<int32, [3]> reduce_mean_90_axes_0 = const()[name = tensor<string, []>("reduce_mean_90_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_90_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_90_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_90_cast = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast)[name = tensor<string, []>("reduce_mean_90_cast")];
            tensor<fp16, [2, 32, 60, 64, 64]> sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast)[name = tensor<string, []>("sub_60_cast")];
            tensor<fp16, [2, 32, 60, 64, 64]> square_30_cast = square(x = sub_60_cast)[name = tensor<string, []>("square_30_cast")];
            tensor<int32, [3]> reduce_mean_92_axes_0 = const()[name = tensor<string, []>("reduce_mean_92_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_92_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_92_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_92_cast = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast)[name = tensor<string, []>("reduce_mean_92_cast")];
            tensor<fp16, []> add_60_y_0_to_fp16 = const()[name = tensor<string, []>("add_60_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_60_cast = add(x = reduce_mean_92_cast, y = add_60_y_0_to_fp16)[name = tensor<string, []>("add_60_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_30_cast = sqrt(x = add_60_cast)[name = tensor<string, []>("sqrt_30_cast")];
            tensor<fp16, [2, 32, 60, 64, 64]> real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast)[name = tensor<string, []>("real_div_30_cast")];
            tensor<int32, [4]> reshape_121_shape_0 = const()[name = tensor<string, []>("reshape_121_shape_0"), val = tensor<int32, [4]>([2, 1920, 64, 64])];
            tensor<fp16, [2, 1920, 64, 64]> reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast)[name = tensor<string, []>("reshape_121_cast")];
            tensor<fp16, [1920]> add_61_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_61_gamma_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1327645376)))];
            tensor<fp16, [1920]> add_61_beta_0_to_fp16 = const()[name = tensor<string, []>("add_61_beta_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1327649280)))];
            tensor<fp16, []> add_61_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_61_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1920, 64, 64]> add_61_cast = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_121_cast)[name = tensor<string, []>("add_61_cast")];
            tensor<fp16, [2, 1920, 64, 64]> input_725_cast = silu(x = add_61_cast)[name = tensor<string, []>("input_725_cast")];
            tensor<int32, [2]> var_12547 = const()[name = tensor<string, []>("op_12547"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12549 = const()[name = tensor<string, []>("op_12549"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_501_pad_type_0 = const()[name = tensor<string, []>("hidden_states_501_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_501_pad_0 = const()[name = tensor<string, []>("hidden_states_501_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 1920, 3, 3]> up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [8294400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1327653184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1335947648))), name = tensor<string, []>("up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1920, 3, 3])];
            tensor<fp16, [640]> up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1335947840)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_501_cast = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_12549, groups = var_12518, pad = hidden_states_501_pad_0, pad_type = hidden_states_501_pad_type_0, strides = var_12547, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_725_cast)[name = tensor<string, []>("hidden_states_501_cast")];
            tensor<int32, [2]> var_12555 = const()[name = tensor<string, []>("op_12555"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12557 = const()[name = tensor<string, []>("op_12557"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_23_pad_type_0 = const()[name = tensor<string, []>("temb_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_23_pad_0 = const()[name = tensor<string, []>("temb_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [614400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1335949184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1336563648))), name = tensor<string, []>("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1336563840)))];
            tensor<fp16, [2, 640, 1, 1]> temb_23_cast = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_12557, groups = var_12518, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_12555, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_23_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_729_cast = add(x = hidden_states_501_cast, y = temb_23_cast)[name = tensor<string, []>("input_729_cast")];
            tensor<int32, [5]> reshape_124_shape_0 = const()[name = tensor<string, []>("reshape_124_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_124_cast = reshape(shape = reshape_124_shape_0, x = input_729_cast)[name = tensor<string, []>("reshape_124_cast")];
            tensor<int32, [3]> reduce_mean_93_axes_0 = const()[name = tensor<string, []>("reduce_mean_93_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_93_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_93_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_93_cast = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast)[name = tensor<string, []>("reduce_mean_93_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_62_cast = sub(x = reshape_124_cast, y = reduce_mean_93_cast)[name = tensor<string, []>("sub_62_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_31_cast = square(x = sub_62_cast)[name = tensor<string, []>("square_31_cast")];
            tensor<int32, [3]> reduce_mean_95_axes_0 = const()[name = tensor<string, []>("reduce_mean_95_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_95_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_95_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_95_cast = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast)[name = tensor<string, []>("reduce_mean_95_cast")];
            tensor<fp16, []> add_62_y_0_to_fp16 = const()[name = tensor<string, []>("add_62_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_62_cast = add(x = reduce_mean_95_cast, y = add_62_y_0_to_fp16)[name = tensor<string, []>("add_62_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_31_cast = sqrt(x = add_62_cast)[name = tensor<string, []>("sqrt_31_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_31_cast = real_div(x = sub_62_cast, y = sqrt_31_cast)[name = tensor<string, []>("real_div_31_cast")];
            tensor<int32, [4]> reshape_125_shape_0 = const()[name = tensor<string, []>("reshape_125_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_125_cast = reshape(shape = reshape_125_shape_0, x = real_div_31_cast)[name = tensor<string, []>("reshape_125_cast")];
            tensor<fp16, [640]> add_63_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_63_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1336565184)))];
            tensor<fp16, [640]> add_63_beta_0_to_fp16 = const()[name = tensor<string, []>("add_63_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1336566528)))];
            tensor<fp16, []> add_63_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_63_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_63_cast = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_125_cast)[name = tensor<string, []>("add_63_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_733_cast = silu(x = add_63_cast)[name = tensor<string, []>("input_733_cast")];
            tensor<int32, [2]> var_12567 = const()[name = tensor<string, []>("op_12567"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12569 = const()[name = tensor<string, []>("op_12569"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_503_pad_type_0 = const()[name = tensor<string, []>("hidden_states_503_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_503_pad_0 = const()[name = tensor<string, []>("hidden_states_503_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3686400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1336567872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1340254336))), name = tensor<string, []>("up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1340254912)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_503_cast = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_12569, groups = var_12518, pad = hidden_states_503_pad_0, pad_type = hidden_states_503_pad_type_0, strides = var_12567, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_733_cast)[name = tensor<string, []>("hidden_states_503_cast")];
            tensor<int32, [2]> var_12574 = const()[name = tensor<string, []>("op_12574"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12576 = const()[name = tensor<string, []>("op_12576"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_11_pad_type_0 = const()[name = tensor<string, []>("x_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_11_pad_0 = const()[name = tensor<string, []>("x_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1920, 1, 1]> up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1340256256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341485120))), name = tensor<string, []>("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1920, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341485696)))];
            tensor<fp16, [2, 640, 64, 64]> x_11_cast = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_12576, groups = var_12518, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_12574, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_721_cast)[name = tensor<string, []>("x_11_cast")];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_505_cast = add(x = x_11_cast, y = hidden_states_503_cast)[name = tensor<string, []>("hidden_states_505_cast")];
            tensor<int32, [5]> reshape_128_shape_0 = const()[name = tensor<string, []>("reshape_128_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_128_cast = reshape(shape = reshape_128_shape_0, x = hidden_states_505_cast)[name = tensor<string, []>("reshape_128_cast")];
            tensor<int32, [3]> reduce_mean_96_axes_0 = const()[name = tensor<string, []>("reduce_mean_96_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_96_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_96_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_96_cast = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast)[name = tensor<string, []>("reduce_mean_96_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_64_cast = sub(x = reshape_128_cast, y = reduce_mean_96_cast)[name = tensor<string, []>("sub_64_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_32_cast = square(x = sub_64_cast)[name = tensor<string, []>("square_32_cast")];
            tensor<int32, [3]> reduce_mean_98_axes_0 = const()[name = tensor<string, []>("reduce_mean_98_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_98_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_98_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_98_cast = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast)[name = tensor<string, []>("reduce_mean_98_cast")];
            tensor<fp16, []> add_64_y_0_to_fp16 = const()[name = tensor<string, []>("add_64_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_64_cast = add(x = reduce_mean_98_cast, y = add_64_y_0_to_fp16)[name = tensor<string, []>("add_64_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_32_cast = sqrt(x = add_64_cast)[name = tensor<string, []>("sqrt_32_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_32_cast = real_div(x = sub_64_cast, y = sqrt_32_cast)[name = tensor<string, []>("real_div_32_cast")];
            tensor<int32, [4]> reshape_129_shape_0 = const()[name = tensor<string, []>("reshape_129_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_129_cast = reshape(shape = reshape_129_shape_0, x = real_div_32_cast)[name = tensor<string, []>("reshape_129_cast")];
            tensor<fp16, [640]> add_65_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_65_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341487040)))];
            tensor<fp16, [640]> add_65_beta_0_to_fp16 = const()[name = tensor<string, []>("add_65_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341488384)))];
            tensor<fp16, []> add_65_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_65_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_65_cast = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_129_cast)[name = tensor<string, []>("add_65_cast")];
            tensor<int32, [2]> var_12598 = const()[name = tensor<string, []>("op_12598"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12600 = const()[name = tensor<string, []>("op_12600"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_507_pad_type_0 = const()[name = tensor<string, []>("hidden_states_507_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_507_pad_0 = const()[name = tensor<string, []>("hidden_states_507_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341489728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341899392))), name = tensor<string, []>("up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341899968)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_507_cast = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_12600, groups = var_12518, pad = hidden_states_507_pad_0, pad_type = hidden_states_507_pad_type_0, strides = var_12598, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_65_cast)[name = tensor<string, []>("hidden_states_507_cast")];
            tensor<int32, [4]> var_12605 = const()[name = tensor<string, []>("op_12605"), val = tensor<int32, [4]>([2, 640, 1, 4096])];
            tensor<fp16, [2, 640, 1, 4096]> inputs_385_cast = reshape(shape = var_12605, x = hidden_states_507_cast)[name = tensor<string, []>("inputs_385_cast")];
            tensor<int32, [1]> var_12615 = const()[name = tensor<string, []>("op_12615"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_385_cast = reduce_mean(axes = var_12615, keep_dims = var_12513, x = inputs_385_cast)[name = tensor<string, []>("channels_mean_385_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_385_cast = sub(x = inputs_385_cast, y = channels_mean_385_cast)[name = tensor<string, []>("zero_mean_385_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_385_cast = mul(x = zero_mean_385_cast, y = zero_mean_385_cast)[name = tensor<string, []>("zero_mean_sq_385_cast")];
            tensor<int32, [1]> var_12619 = const()[name = tensor<string, []>("op_12619"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_12620_cast = reduce_mean(axes = var_12619, keep_dims = var_12513, x = zero_mean_sq_385_cast)[name = tensor<string, []>("op_12620_cast")];
            tensor<fp16, []> var_12621_to_fp16 = const()[name = tensor<string, []>("op_12621_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_12622_cast = add(x = var_12620_cast, y = var_12621_to_fp16)[name = tensor<string, []>("op_12622_cast")];
            tensor<fp16, []> denom_385_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_385_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_385_cast = rsqrt(epsilon = denom_385_epsilon_0_to_fp16, x = var_12622_cast)[name = tensor<string, []>("denom_385_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_385_cast = mul(x = zero_mean_385_cast, y = denom_385_cast)[name = tensor<string, []>("out_385_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12626_to_fp16 = const()[name = tensor<string, []>("op_12626_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341901312)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12627_cast = add(x = out_385_cast, y = var_12626_to_fp16)[name = tensor<string, []>("op_12627_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12629_to_fp16 = const()[name = tensor<string, []>("op_12629_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341902656)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_509_cast = mul(x = var_12627_cast, y = var_12629_to_fp16)[name = tensor<string, []>("hidden_states_509_cast")];
            tensor<int32, [2]> var_12636 = const()[name = tensor<string, []>("op_12636"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12638 = const()[name = tensor<string, []>("op_12638"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_257_pad_type_0 = const()[name = tensor<string, []>("q_257_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_257_pad_0 = const()[name = tensor<string, []>("q_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1341904000))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1342211264))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_257_cast = conv(dilations = var_12638, groups = var_12518, pad = q_257_pad_0, pad_type = q_257_pad_type_0, strides = var_12636, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_509_cast)[name = tensor<string, []>("q_257_cast")];
            tensor<int32, [2]> var_12642 = const()[name = tensor<string, []>("op_12642"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12644 = const()[name = tensor<string, []>("op_12644"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_257_pad_type_0 = const()[name = tensor<string, []>("k_257_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_257_pad_0 = const()[name = tensor<string, []>("k_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1342211456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1342518720))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_257_cast = conv(dilations = var_12644, groups = var_12518, pad = k_257_pad_0, pad_type = k_257_pad_type_0, strides = var_12642, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_509_cast)[name = tensor<string, []>("k_257_cast")];
            tensor<int32, [2]> var_12648 = const()[name = tensor<string, []>("op_12648"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12650 = const()[name = tensor<string, []>("op_12650"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_257_pad_type_0 = const()[name = tensor<string, []>("v_257_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_257_pad_0 = const()[name = tensor<string, []>("v_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1342518912))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1342928576))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_257_cast = conv(dilations = var_12650, groups = var_12518, pad = v_257_pad_0, pad_type = v_257_pad_type_0, strides = var_12648, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_509_cast)[name = tensor<string, []>("v_257_cast")];
            tensor<int32, [4]> var_12654 = const()[name = tensor<string, []>("op_12654"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12655_cast = reshape(shape = var_12654, x = q_257_cast)[name = tensor<string, []>("op_12655_cast")];
            tensor<int32, [4]> var_12656 = const()[name = tensor<string, []>("op_12656"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12657_cast = reshape(shape = var_12656, x = k_257_cast)[name = tensor<string, []>("op_12657_cast")];
            tensor<int32, [4]> var_12658 = const()[name = tensor<string, []>("op_12658"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12659_cast = reshape(shape = var_12658, x = v_257_cast)[name = tensor<string, []>("op_12659_cast")];
            tensor<bool, []> attn_weights_513_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_513_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_513_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_513_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_513_cast = matmul(transpose_x = attn_weights_513_transpose_x_0, transpose_y = attn_weights_513_transpose_y_0, x = var_12655_cast, y = var_12657_cast)[name = tensor<string, []>("attn_weights_513_cast")];
            tensor<fp16, []> var_12509_to_fp16 = const()[name = tensor<string, []>("op_12509_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_515_cast = mul(x = attn_weights_513_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_515_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_12663_cast = softmax(axis = var_12502, x = attn_weights_515_cast)[name = tensor<string, []>("op_12663_cast")];
            tensor<bool, []> attn_257_transpose_x_0 = const()[name = tensor<string, []>("attn_257_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_257_transpose_y_0 = const()[name = tensor<string, []>("attn_257_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_257_cast = matmul(transpose_x = attn_257_transpose_x_0, transpose_y = attn_257_transpose_y_0, x = var_12659_cast, y = var_12663_cast)[name = tensor<string, []>("attn_257_cast")];
            tensor<int32, [4]> var_12667 = const()[name = tensor<string, []>("op_12667"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_737_cast = reshape(shape = var_12667, x = attn_257_cast)[name = tensor<string, []>("input_737_cast")];
            tensor<int32, [2]> var_12672 = const()[name = tensor<string, []>("op_12672"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12674 = const()[name = tensor<string, []>("op_12674"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12676_pad_type_0 = const()[name = tensor<string, []>("op_12676_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12676_pad_0 = const()[name = tensor<string, []>("op_12676_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1342929152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1343338816))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1343339392)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12676_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_12674, groups = var_12518, pad = var_12676_pad_0, pad_type = var_12676_pad_type_0, strides = var_12672, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_737_cast)[name = tensor<string, []>("op_12676_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_387_cast = add(x = var_12676_cast, y = inputs_385_cast)[name = tensor<string, []>("inputs_387_cast")];
            tensor<int32, [1]> var_12680 = const()[name = tensor<string, []>("op_12680"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_387_cast = reduce_mean(axes = var_12680, keep_dims = var_12513, x = inputs_387_cast)[name = tensor<string, []>("channels_mean_387_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_387_cast = sub(x = inputs_387_cast, y = channels_mean_387_cast)[name = tensor<string, []>("zero_mean_387_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_387_cast = mul(x = zero_mean_387_cast, y = zero_mean_387_cast)[name = tensor<string, []>("zero_mean_sq_387_cast")];
            tensor<int32, [1]> var_12684 = const()[name = tensor<string, []>("op_12684"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_12685_cast = reduce_mean(axes = var_12684, keep_dims = var_12513, x = zero_mean_sq_387_cast)[name = tensor<string, []>("op_12685_cast")];
            tensor<fp16, []> var_12686_to_fp16 = const()[name = tensor<string, []>("op_12686_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_12687_cast = add(x = var_12685_cast, y = var_12686_to_fp16)[name = tensor<string, []>("op_12687_cast")];
            tensor<fp16, []> denom_387_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_387_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_387_cast = rsqrt(epsilon = denom_387_epsilon_0_to_fp16, x = var_12687_cast)[name = tensor<string, []>("denom_387_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_387_cast = mul(x = zero_mean_387_cast, y = denom_387_cast)[name = tensor<string, []>("out_387_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12691_to_fp16 = const()[name = tensor<string, []>("op_12691_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1343340736)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12692_cast = add(x = out_387_cast, y = var_12691_to_fp16)[name = tensor<string, []>("op_12692_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12694_to_fp16 = const()[name = tensor<string, []>("op_12694_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1343342080)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_511_cast = mul(x = var_12692_cast, y = var_12694_to_fp16)[name = tensor<string, []>("hidden_states_511_cast")];
            tensor<int32, [2]> var_12701 = const()[name = tensor<string, []>("op_12701"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12703 = const()[name = tensor<string, []>("op_12703"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_259_pad_type_0 = const()[name = tensor<string, []>("q_259_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_259_pad_0 = const()[name = tensor<string, []>("q_259_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1343343424))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1343753088))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_259_cast = conv(dilations = var_12703, groups = var_12518, pad = q_259_pad_0, pad_type = q_259_pad_type_0, strides = var_12701, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_511_cast)[name = tensor<string, []>("q_259_cast")];
            tensor<int32, [2]> var_12707 = const()[name = tensor<string, []>("op_12707"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12709 = const()[name = tensor<string, []>("op_12709"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_259_pad_type_0 = const()[name = tensor<string, []>("k_259_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_259_pad_0 = const()[name = tensor<string, []>("k_259_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1343753664))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1344736768))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_259_cast = conv(dilations = var_12709, groups = var_12518, pad = k_259_pad_0, pad_type = k_259_pad_type_0, strides = var_12707, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_259_cast")];
            tensor<int32, [2]> var_12713 = const()[name = tensor<string, []>("op_12713"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12715 = const()[name = tensor<string, []>("op_12715"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_259_pad_type_0 = const()[name = tensor<string, []>("v_259_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_259_pad_0 = const()[name = tensor<string, []>("v_259_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1344736960))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1345392384))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_259_cast = conv(dilations = var_12715, groups = var_12518, pad = v_259_pad_0, pad_type = v_259_pad_type_0, strides = var_12713, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_259_cast")];
            tensor<int32, [4]> var_12719 = const()[name = tensor<string, []>("op_12719"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12720_cast = reshape(shape = var_12719, x = q_259_cast)[name = tensor<string, []>("op_12720_cast")];
            tensor<int32, [4]> var_12721 = const()[name = tensor<string, []>("op_12721"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_12722_cast = reshape(shape = var_12721, x = k_259_cast)[name = tensor<string, []>("op_12722_cast")];
            tensor<int32, [4]> var_12723 = const()[name = tensor<string, []>("op_12723"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_12724_cast = reshape(shape = var_12723, x = v_259_cast)[name = tensor<string, []>("op_12724_cast")];
            tensor<bool, []> attn_weights_517_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_517_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_517_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_517_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_517_cast = matmul(transpose_x = attn_weights_517_transpose_x_0, transpose_y = attn_weights_517_transpose_y_0, x = var_12720_cast, y = var_12722_cast)[name = tensor<string, []>("attn_weights_517_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_519_cast = mul(x = attn_weights_517_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_519_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_12728_cast = softmax(axis = var_12502, x = attn_weights_519_cast)[name = tensor<string, []>("op_12728_cast")];
            tensor<bool, []> attn_259_transpose_x_0 = const()[name = tensor<string, []>("attn_259_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_259_transpose_y_0 = const()[name = tensor<string, []>("attn_259_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_259_cast = matmul(transpose_x = attn_259_transpose_x_0, transpose_y = attn_259_transpose_y_0, x = var_12724_cast, y = var_12728_cast)[name = tensor<string, []>("attn_259_cast")];
            tensor<int32, [4]> var_12732 = const()[name = tensor<string, []>("op_12732"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_739_cast = reshape(shape = var_12732, x = attn_259_cast)[name = tensor<string, []>("input_739_cast")];
            tensor<int32, [2]> var_12737 = const()[name = tensor<string, []>("op_12737"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12739 = const()[name = tensor<string, []>("op_12739"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12741_pad_type_0 = const()[name = tensor<string, []>("op_12741_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12741_pad_0 = const()[name = tensor<string, []>("op_12741_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1345392512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1345699776))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1345699968)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12741_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_12739, groups = var_12518, pad = var_12741_pad_0, pad_type = var_12741_pad_type_0, strides = var_12737, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_739_cast)[name = tensor<string, []>("op_12741_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_389_cast = add(x = var_12741_cast, y = inputs_387_cast)[name = tensor<string, []>("inputs_389_cast")];
            tensor<int32, [1]> var_12745 = const()[name = tensor<string, []>("op_12745"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_389_cast = reduce_mean(axes = var_12745, keep_dims = var_12513, x = inputs_389_cast)[name = tensor<string, []>("channels_mean_389_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_389_cast = sub(x = inputs_389_cast, y = channels_mean_389_cast)[name = tensor<string, []>("zero_mean_389_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_389_cast = mul(x = zero_mean_389_cast, y = zero_mean_389_cast)[name = tensor<string, []>("zero_mean_sq_389_cast")];
            tensor<int32, [1]> var_12749 = const()[name = tensor<string, []>("op_12749"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_12750_cast = reduce_mean(axes = var_12749, keep_dims = var_12513, x = zero_mean_sq_389_cast)[name = tensor<string, []>("op_12750_cast")];
            tensor<fp16, []> var_12751_to_fp16 = const()[name = tensor<string, []>("op_12751_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_12752_cast = add(x = var_12750_cast, y = var_12751_to_fp16)[name = tensor<string, []>("op_12752_cast")];
            tensor<fp16, []> denom_389_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_389_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_389_cast = rsqrt(epsilon = denom_389_epsilon_0_to_fp16, x = var_12752_cast)[name = tensor<string, []>("denom_389_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_389_cast = mul(x = zero_mean_389_cast, y = denom_389_cast)[name = tensor<string, []>("out_389_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12756_to_fp16 = const()[name = tensor<string, []>("op_12756_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1345701312)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12757_cast = add(x = out_389_cast, y = var_12756_to_fp16)[name = tensor<string, []>("op_12757_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12759_to_fp16 = const()[name = tensor<string, []>("op_12759_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1345702656)))];
            tensor<fp16, [2, 640, 1, 4096]> input_741_cast = mul(x = var_12757_cast, y = var_12759_to_fp16)[name = tensor<string, []>("input_741_cast")];
            tensor<int32, [2]> var_12767 = const()[name = tensor<string, []>("op_12767"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12769 = const()[name = tensor<string, []>("op_12769"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12771_pad_type_0 = const()[name = tensor<string, []>("op_12771_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12771_pad_0 = const()[name = tensor<string, []>("op_12771_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1345704000))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1348980864))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1348981440)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_12771_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_12769, groups = var_12518, pad = var_12771_pad_0, pad_type = var_12771_pad_type_0, strides = var_12767, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_741_cast)[name = tensor<string, []>("op_12771_cast")];
            tensor<int32, [2]> var_12772_split_sizes_0 = const()[name = tensor<string, []>("op_12772_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_12772_axis_0 = const()[name = tensor<string, []>("op_12772_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_12772_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_12772_cast_1 = split(axis = var_12772_axis_0, split_sizes = var_12772_split_sizes_0, x = var_12771_cast)[name = tensor<string, []>("op_12772_cast")];
            tensor<string, []> var_12774_mode_0 = const()[name = tensor<string, []>("op_12774_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_12774_cast = gelu(mode = var_12774_mode_0, x = var_12772_cast_1)[name = tensor<string, []>("op_12774_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_743_cast = mul(x = var_12772_cast_0, y = var_12774_cast)[name = tensor<string, []>("input_743_cast")];
            tensor<int32, [2]> var_12778 = const()[name = tensor<string, []>("op_12778"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12780 = const()[name = tensor<string, []>("op_12780"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12782_pad_type_0 = const()[name = tensor<string, []>("op_12782_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12782_pad_0 = const()[name = tensor<string, []>("op_12782_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1348991744))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350630208))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350630784)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12782_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_12780, groups = var_12518, pad = var_12782_pad_0, pad_type = var_12782_pad_type_0, strides = var_12778, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_743_cast)[name = tensor<string, []>("op_12782_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_391_cast = add(x = var_12782_cast, y = inputs_389_cast)[name = tensor<string, []>("inputs_391_cast")];
            tensor<int32, [1]> var_12792 = const()[name = tensor<string, []>("op_12792"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_391_cast = reduce_mean(axes = var_12792, keep_dims = var_12513, x = inputs_391_cast)[name = tensor<string, []>("channels_mean_391_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_391_cast = sub(x = inputs_391_cast, y = channels_mean_391_cast)[name = tensor<string, []>("zero_mean_391_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_391_cast = mul(x = zero_mean_391_cast, y = zero_mean_391_cast)[name = tensor<string, []>("zero_mean_sq_391_cast")];
            tensor<int32, [1]> var_12796 = const()[name = tensor<string, []>("op_12796"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_12797_cast = reduce_mean(axes = var_12796, keep_dims = var_12513, x = zero_mean_sq_391_cast)[name = tensor<string, []>("op_12797_cast")];
            tensor<fp16, []> var_12798_to_fp16 = const()[name = tensor<string, []>("op_12798_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_12799_cast = add(x = var_12797_cast, y = var_12798_to_fp16)[name = tensor<string, []>("op_12799_cast")];
            tensor<fp16, []> denom_391_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_391_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_391_cast = rsqrt(epsilon = denom_391_epsilon_0_to_fp16, x = var_12799_cast)[name = tensor<string, []>("denom_391_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_391_cast = mul(x = zero_mean_391_cast, y = denom_391_cast)[name = tensor<string, []>("out_391_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12803_to_fp16 = const()[name = tensor<string, []>("op_12803_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350632128)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12804_cast = add(x = out_391_cast, y = var_12803_to_fp16)[name = tensor<string, []>("op_12804_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12806_to_fp16 = const()[name = tensor<string, []>("op_12806_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350633472)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_515_cast = mul(x = var_12804_cast, y = var_12806_to_fp16)[name = tensor<string, []>("hidden_states_515_cast")];
            tensor<int32, [2]> var_12813 = const()[name = tensor<string, []>("op_12813"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12815 = const()[name = tensor<string, []>("op_12815"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_261_pad_type_0 = const()[name = tensor<string, []>("q_261_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_261_pad_0 = const()[name = tensor<string, []>("q_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350634816))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350942080))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_261_cast = conv(dilations = var_12815, groups = var_12518, pad = q_261_pad_0, pad_type = q_261_pad_type_0, strides = var_12813, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_515_cast)[name = tensor<string, []>("q_261_cast")];
            tensor<int32, [2]> var_12819 = const()[name = tensor<string, []>("op_12819"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12821 = const()[name = tensor<string, []>("op_12821"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_261_pad_type_0 = const()[name = tensor<string, []>("k_261_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_261_pad_0 = const()[name = tensor<string, []>("k_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350942272))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1351249536))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_261_cast = conv(dilations = var_12821, groups = var_12518, pad = k_261_pad_0, pad_type = k_261_pad_type_0, strides = var_12819, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_515_cast)[name = tensor<string, []>("k_261_cast")];
            tensor<int32, [2]> var_12825 = const()[name = tensor<string, []>("op_12825"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12827 = const()[name = tensor<string, []>("op_12827"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_261_pad_type_0 = const()[name = tensor<string, []>("v_261_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_261_pad_0 = const()[name = tensor<string, []>("v_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1351249728))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1351659392))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_261_cast = conv(dilations = var_12827, groups = var_12518, pad = v_261_pad_0, pad_type = v_261_pad_type_0, strides = var_12825, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_515_cast)[name = tensor<string, []>("v_261_cast")];
            tensor<int32, [4]> var_12831 = const()[name = tensor<string, []>("op_12831"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12832_cast = reshape(shape = var_12831, x = q_261_cast)[name = tensor<string, []>("op_12832_cast")];
            tensor<int32, [4]> var_12833 = const()[name = tensor<string, []>("op_12833"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12834_cast = reshape(shape = var_12833, x = k_261_cast)[name = tensor<string, []>("op_12834_cast")];
            tensor<int32, [4]> var_12835 = const()[name = tensor<string, []>("op_12835"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12836_cast = reshape(shape = var_12835, x = v_261_cast)[name = tensor<string, []>("op_12836_cast")];
            tensor<bool, []> attn_weights_521_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_521_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_521_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_521_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_521_cast = matmul(transpose_x = attn_weights_521_transpose_x_0, transpose_y = attn_weights_521_transpose_y_0, x = var_12832_cast, y = var_12834_cast)[name = tensor<string, []>("attn_weights_521_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_523_cast = mul(x = attn_weights_521_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_523_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_12840_cast = softmax(axis = var_12502, x = attn_weights_523_cast)[name = tensor<string, []>("op_12840_cast")];
            tensor<bool, []> attn_261_transpose_x_0 = const()[name = tensor<string, []>("attn_261_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_261_transpose_y_0 = const()[name = tensor<string, []>("attn_261_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_261_cast = matmul(transpose_x = attn_261_transpose_x_0, transpose_y = attn_261_transpose_y_0, x = var_12836_cast, y = var_12840_cast)[name = tensor<string, []>("attn_261_cast")];
            tensor<int32, [4]> var_12844 = const()[name = tensor<string, []>("op_12844"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_745_cast = reshape(shape = var_12844, x = attn_261_cast)[name = tensor<string, []>("input_745_cast")];
            tensor<int32, [2]> var_12849 = const()[name = tensor<string, []>("op_12849"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12851 = const()[name = tensor<string, []>("op_12851"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12853_pad_type_0 = const()[name = tensor<string, []>("op_12853_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12853_pad_0 = const()[name = tensor<string, []>("op_12853_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1351659968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352069632))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352070208)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12853_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_12851, groups = var_12518, pad = var_12853_pad_0, pad_type = var_12853_pad_type_0, strides = var_12849, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_745_cast)[name = tensor<string, []>("op_12853_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_393_cast = add(x = var_12853_cast, y = inputs_391_cast)[name = tensor<string, []>("inputs_393_cast")];
            tensor<int32, [1]> var_12857 = const()[name = tensor<string, []>("op_12857"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_393_cast = reduce_mean(axes = var_12857, keep_dims = var_12513, x = inputs_393_cast)[name = tensor<string, []>("channels_mean_393_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_393_cast = sub(x = inputs_393_cast, y = channels_mean_393_cast)[name = tensor<string, []>("zero_mean_393_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_393_cast = mul(x = zero_mean_393_cast, y = zero_mean_393_cast)[name = tensor<string, []>("zero_mean_sq_393_cast")];
            tensor<int32, [1]> var_12861 = const()[name = tensor<string, []>("op_12861"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_12862_cast = reduce_mean(axes = var_12861, keep_dims = var_12513, x = zero_mean_sq_393_cast)[name = tensor<string, []>("op_12862_cast")];
            tensor<fp16, []> var_12863_to_fp16 = const()[name = tensor<string, []>("op_12863_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_12864_cast = add(x = var_12862_cast, y = var_12863_to_fp16)[name = tensor<string, []>("op_12864_cast")];
            tensor<fp16, []> denom_393_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_393_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_393_cast = rsqrt(epsilon = denom_393_epsilon_0_to_fp16, x = var_12864_cast)[name = tensor<string, []>("denom_393_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_393_cast = mul(x = zero_mean_393_cast, y = denom_393_cast)[name = tensor<string, []>("out_393_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12868_to_fp16 = const()[name = tensor<string, []>("op_12868_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352071552)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12869_cast = add(x = out_393_cast, y = var_12868_to_fp16)[name = tensor<string, []>("op_12869_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12871_to_fp16 = const()[name = tensor<string, []>("op_12871_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352072896)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_517_cast = mul(x = var_12869_cast, y = var_12871_to_fp16)[name = tensor<string, []>("hidden_states_517_cast")];
            tensor<int32, [2]> var_12878 = const()[name = tensor<string, []>("op_12878"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12880 = const()[name = tensor<string, []>("op_12880"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_263_pad_type_0 = const()[name = tensor<string, []>("q_263_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_263_pad_0 = const()[name = tensor<string, []>("q_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352074240))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352279104))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_263_cast = conv(dilations = var_12880, groups = var_12518, pad = q_263_pad_0, pad_type = q_263_pad_type_0, strides = var_12878, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_517_cast)[name = tensor<string, []>("q_263_cast")];
            tensor<int32, [2]> var_12884 = const()[name = tensor<string, []>("op_12884"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12886 = const()[name = tensor<string, []>("op_12886"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_263_pad_type_0 = const()[name = tensor<string, []>("k_263_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_263_pad_0 = const()[name = tensor<string, []>("k_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352279232))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352934656))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_263_cast = conv(dilations = var_12886, groups = var_12518, pad = k_263_pad_0, pad_type = k_263_pad_type_0, strides = var_12884, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_263_cast")];
            tensor<int32, [2]> var_12890 = const()[name = tensor<string, []>("op_12890"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12892 = const()[name = tensor<string, []>("op_12892"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_263_pad_type_0 = const()[name = tensor<string, []>("v_263_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_263_pad_0 = const()[name = tensor<string, []>("v_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1352934784))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353590208))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_263_cast = conv(dilations = var_12892, groups = var_12518, pad = v_263_pad_0, pad_type = v_263_pad_type_0, strides = var_12890, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_263_cast")];
            tensor<int32, [4]> var_12896 = const()[name = tensor<string, []>("op_12896"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_12897_cast = reshape(shape = var_12896, x = q_263_cast)[name = tensor<string, []>("op_12897_cast")];
            tensor<int32, [4]> var_12898 = const()[name = tensor<string, []>("op_12898"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_12899_cast = reshape(shape = var_12898, x = k_263_cast)[name = tensor<string, []>("op_12899_cast")];
            tensor<int32, [4]> var_12900 = const()[name = tensor<string, []>("op_12900"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_12901_cast = reshape(shape = var_12900, x = v_263_cast)[name = tensor<string, []>("op_12901_cast")];
            tensor<bool, []> attn_weights_525_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_525_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_525_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_525_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_525_cast = matmul(transpose_x = attn_weights_525_transpose_x_0, transpose_y = attn_weights_525_transpose_y_0, x = var_12897_cast, y = var_12899_cast)[name = tensor<string, []>("attn_weights_525_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_527_cast = mul(x = attn_weights_525_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_527_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_12905_cast = softmax(axis = var_12502, x = attn_weights_527_cast)[name = tensor<string, []>("op_12905_cast")];
            tensor<bool, []> attn_263_transpose_x_0 = const()[name = tensor<string, []>("attn_263_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_263_transpose_y_0 = const()[name = tensor<string, []>("attn_263_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_263_cast = matmul(transpose_x = attn_263_transpose_x_0, transpose_y = attn_263_transpose_y_0, x = var_12901_cast, y = var_12905_cast)[name = tensor<string, []>("attn_263_cast")];
            tensor<int32, [4]> var_12909 = const()[name = tensor<string, []>("op_12909"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_747_cast = reshape(shape = var_12909, x = attn_263_cast)[name = tensor<string, []>("input_747_cast")];
            tensor<int32, [2]> var_12914 = const()[name = tensor<string, []>("op_12914"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12916 = const()[name = tensor<string, []>("op_12916"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12918_pad_type_0 = const()[name = tensor<string, []>("op_12918_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12918_pad_0 = const()[name = tensor<string, []>("op_12918_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353590336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353897600))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353897792)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12918_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_12916, groups = var_12518, pad = var_12918_pad_0, pad_type = var_12918_pad_type_0, strides = var_12914, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_747_cast)[name = tensor<string, []>("op_12918_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_395_cast = add(x = var_12918_cast, y = inputs_393_cast)[name = tensor<string, []>("inputs_395_cast")];
            tensor<int32, [1]> var_12922 = const()[name = tensor<string, []>("op_12922"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_395_cast = reduce_mean(axes = var_12922, keep_dims = var_12513, x = inputs_395_cast)[name = tensor<string, []>("channels_mean_395_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_395_cast = sub(x = inputs_395_cast, y = channels_mean_395_cast)[name = tensor<string, []>("zero_mean_395_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_395_cast = mul(x = zero_mean_395_cast, y = zero_mean_395_cast)[name = tensor<string, []>("zero_mean_sq_395_cast")];
            tensor<int32, [1]> var_12926 = const()[name = tensor<string, []>("op_12926"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_12927_cast = reduce_mean(axes = var_12926, keep_dims = var_12513, x = zero_mean_sq_395_cast)[name = tensor<string, []>("op_12927_cast")];
            tensor<fp16, []> var_12928_to_fp16 = const()[name = tensor<string, []>("op_12928_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_12929_cast = add(x = var_12927_cast, y = var_12928_to_fp16)[name = tensor<string, []>("op_12929_cast")];
            tensor<fp16, []> denom_395_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_395_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_395_cast = rsqrt(epsilon = denom_395_epsilon_0_to_fp16, x = var_12929_cast)[name = tensor<string, []>("denom_395_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_395_cast = mul(x = zero_mean_395_cast, y = denom_395_cast)[name = tensor<string, []>("out_395_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12933_to_fp16 = const()[name = tensor<string, []>("op_12933_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353899136)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12934_cast = add(x = out_395_cast, y = var_12933_to_fp16)[name = tensor<string, []>("op_12934_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_12936_to_fp16 = const()[name = tensor<string, []>("op_12936_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353900480)))];
            tensor<fp16, [2, 640, 1, 4096]> input_749_cast = mul(x = var_12934_cast, y = var_12936_to_fp16)[name = tensor<string, []>("input_749_cast")];
            tensor<int32, [2]> var_12944 = const()[name = tensor<string, []>("op_12944"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12946 = const()[name = tensor<string, []>("op_12946"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12948_pad_type_0 = const()[name = tensor<string, []>("op_12948_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12948_pad_0 = const()[name = tensor<string, []>("op_12948_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353901824))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357178688))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357179264)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_12948_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_12946, groups = var_12518, pad = var_12948_pad_0, pad_type = var_12948_pad_type_0, strides = var_12944, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_749_cast)[name = tensor<string, []>("op_12948_cast")];
            tensor<int32, [2]> var_12949_split_sizes_0 = const()[name = tensor<string, []>("op_12949_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_12949_axis_0 = const()[name = tensor<string, []>("op_12949_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_12949_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_12949_cast_1 = split(axis = var_12949_axis_0, split_sizes = var_12949_split_sizes_0, x = var_12948_cast)[name = tensor<string, []>("op_12949_cast")];
            tensor<string, []> var_12951_mode_0 = const()[name = tensor<string, []>("op_12951_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_12951_cast = gelu(mode = var_12951_mode_0, x = var_12949_cast_1)[name = tensor<string, []>("op_12951_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_751_cast = mul(x = var_12949_cast_0, y = var_12951_cast)[name = tensor<string, []>("input_751_cast")];
            tensor<int32, [2]> var_12955 = const()[name = tensor<string, []>("op_12955"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12957 = const()[name = tensor<string, []>("op_12957"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_12959_pad_type_0 = const()[name = tensor<string, []>("op_12959_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_12959_pad_0 = const()[name = tensor<string, []>("op_12959_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357189568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1358828032))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1358828608)))];
            tensor<fp16, [2, 640, 1, 4096]> var_12959_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_12957, groups = var_12518, pad = var_12959_pad_0, pad_type = var_12959_pad_type_0, strides = var_12955, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_751_cast)[name = tensor<string, []>("op_12959_cast")];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_521_cast = add(x = var_12959_cast, y = inputs_395_cast)[name = tensor<string, []>("hidden_states_521_cast")];
            tensor<int32, [4]> var_12961 = const()[name = tensor<string, []>("op_12961"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> input_753_cast = reshape(shape = var_12961, x = hidden_states_521_cast)[name = tensor<string, []>("input_753_cast")];
            tensor<int32, [2]> var_12965 = const()[name = tensor<string, []>("op_12965"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12967 = const()[name = tensor<string, []>("op_12967"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_523_pad_type_0 = const()[name = tensor<string, []>("hidden_states_523_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_523_pad_0 = const()[name = tensor<string, []>("hidden_states_523_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1358829952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359239616))), name = tensor<string, []>("up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359240192)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_523_cast = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_12967, groups = var_12518, pad = hidden_states_523_pad_0, pad_type = hidden_states_523_pad_type_0, strides = var_12965, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_753_cast)[name = tensor<string, []>("hidden_states_523_cast")];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_525_cast = add(x = hidden_states_523_cast, y = hidden_states_505_cast)[name = tensor<string, []>("hidden_states_525_cast")];
            tensor<bool, []> input_755_interleave_0 = const()[name = tensor<string, []>("input_755_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 1280, 64, 64]> input_755_cast = concat(axis = var_12518, interleave = input_755_interleave_0, values = (hidden_states_525_cast, input_79_cast))[name = tensor<string, []>("input_755_cast")];
            tensor<int32, [5]> reshape_132_shape_0 = const()[name = tensor<string, []>("reshape_132_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 64, 64])];
            tensor<fp16, [2, 32, 40, 64, 64]> reshape_132_cast = reshape(shape = reshape_132_shape_0, x = input_755_cast)[name = tensor<string, []>("reshape_132_cast")];
            tensor<int32, [3]> reduce_mean_99_axes_0 = const()[name = tensor<string, []>("reduce_mean_99_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_99_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_99_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_99_cast = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast)[name = tensor<string, []>("reduce_mean_99_cast")];
            tensor<fp16, [2, 32, 40, 64, 64]> sub_66_cast = sub(x = reshape_132_cast, y = reduce_mean_99_cast)[name = tensor<string, []>("sub_66_cast")];
            tensor<fp16, [2, 32, 40, 64, 64]> square_33_cast = square(x = sub_66_cast)[name = tensor<string, []>("square_33_cast")];
            tensor<int32, [3]> reduce_mean_101_axes_0 = const()[name = tensor<string, []>("reduce_mean_101_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_101_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_101_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_101_cast = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast)[name = tensor<string, []>("reduce_mean_101_cast")];
            tensor<fp16, []> add_66_y_0_to_fp16 = const()[name = tensor<string, []>("add_66_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_66_cast = add(x = reduce_mean_101_cast, y = add_66_y_0_to_fp16)[name = tensor<string, []>("add_66_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_33_cast = sqrt(x = add_66_cast)[name = tensor<string, []>("sqrt_33_cast")];
            tensor<fp16, [2, 32, 40, 64, 64]> real_div_33_cast = real_div(x = sub_66_cast, y = sqrt_33_cast)[name = tensor<string, []>("real_div_33_cast")];
            tensor<int32, [4]> reshape_133_shape_0 = const()[name = tensor<string, []>("reshape_133_shape_0"), val = tensor<int32, [4]>([2, 1280, 64, 64])];
            tensor<fp16, [2, 1280, 64, 64]> reshape_133_cast = reshape(shape = reshape_133_shape_0, x = real_div_33_cast)[name = tensor<string, []>("reshape_133_cast")];
            tensor<fp16, [1280]> add_67_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_67_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359241536)))];
            tensor<fp16, [1280]> add_67_beta_0_to_fp16 = const()[name = tensor<string, []>("add_67_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359244160)))];
            tensor<fp16, []> add_67_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_67_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 64, 64]> add_67_cast = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_133_cast)[name = tensor<string, []>("add_67_cast")];
            tensor<fp16, [2, 1280, 64, 64]> input_759_cast = silu(x = add_67_cast)[name = tensor<string, []>("input_759_cast")];
            tensor<int32, [2]> var_12985 = const()[name = tensor<string, []>("op_12985"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12987 = const()[name = tensor<string, []>("op_12987"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_527_pad_type_0 = const()[name = tensor<string, []>("hidden_states_527_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_527_pad_0 = const()[name = tensor<string, []>("hidden_states_527_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 1280, 3, 3]> up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7372800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359246784))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1366619648))), name = tensor<string, []>("up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 3, 3])];
            tensor<fp16, [640]> up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1366620224)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_527_cast = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_12987, groups = var_12518, pad = hidden_states_527_pad_0, pad_type = hidden_states_527_pad_type_0, strides = var_12985, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_759_cast)[name = tensor<string, []>("hidden_states_527_cast")];
            tensor<int32, [2]> var_12993 = const()[name = tensor<string, []>("op_12993"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_12995 = const()[name = tensor<string, []>("op_12995"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_25_pad_type_0 = const()[name = tensor<string, []>("temb_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_25_pad_0 = const()[name = tensor<string, []>("temb_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1366621568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367440832))), name = tensor<string, []>("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367441408)))];
            tensor<fp16, [2, 640, 1, 1]> temb_25_cast = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_12995, groups = var_12518, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_12993, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_25_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_763_cast = add(x = hidden_states_527_cast, y = temb_25_cast)[name = tensor<string, []>("input_763_cast")];
            tensor<int32, [5]> reshape_136_shape_0 = const()[name = tensor<string, []>("reshape_136_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_136_cast = reshape(shape = reshape_136_shape_0, x = input_763_cast)[name = tensor<string, []>("reshape_136_cast")];
            tensor<int32, [3]> reduce_mean_102_axes_0 = const()[name = tensor<string, []>("reduce_mean_102_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_102_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_102_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_102_cast = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast)[name = tensor<string, []>("reduce_mean_102_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_68_cast = sub(x = reshape_136_cast, y = reduce_mean_102_cast)[name = tensor<string, []>("sub_68_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_34_cast = square(x = sub_68_cast)[name = tensor<string, []>("square_34_cast")];
            tensor<int32, [3]> reduce_mean_104_axes_0 = const()[name = tensor<string, []>("reduce_mean_104_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_104_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_104_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_104_cast = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast)[name = tensor<string, []>("reduce_mean_104_cast")];
            tensor<fp16, []> add_68_y_0_to_fp16 = const()[name = tensor<string, []>("add_68_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_68_cast = add(x = reduce_mean_104_cast, y = add_68_y_0_to_fp16)[name = tensor<string, []>("add_68_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_34_cast = sqrt(x = add_68_cast)[name = tensor<string, []>("sqrt_34_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_34_cast = real_div(x = sub_68_cast, y = sqrt_34_cast)[name = tensor<string, []>("real_div_34_cast")];
            tensor<int32, [4]> reshape_137_shape_0 = const()[name = tensor<string, []>("reshape_137_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_137_cast = reshape(shape = reshape_137_shape_0, x = real_div_34_cast)[name = tensor<string, []>("reshape_137_cast")];
            tensor<fp16, [640]> add_69_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_69_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367442752)))];
            tensor<fp16, [640]> add_69_beta_0_to_fp16 = const()[name = tensor<string, []>("add_69_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367444096)))];
            tensor<fp16, []> add_69_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_69_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_69_cast = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_137_cast)[name = tensor<string, []>("add_69_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_767_cast = silu(x = add_69_cast)[name = tensor<string, []>("input_767_cast")];
            tensor<int32, [2]> var_13005 = const()[name = tensor<string, []>("op_13005"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13007 = const()[name = tensor<string, []>("op_13007"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_529_pad_type_0 = const()[name = tensor<string, []>("hidden_states_529_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_529_pad_0 = const()[name = tensor<string, []>("hidden_states_529_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3686400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367445440))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1371131904))), name = tensor<string, []>("up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1371132480)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_529_cast = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_13007, groups = var_12518, pad = hidden_states_529_pad_0, pad_type = hidden_states_529_pad_type_0, strides = var_13005, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_767_cast)[name = tensor<string, []>("hidden_states_529_cast")];
            tensor<int32, [2]> var_13012 = const()[name = tensor<string, []>("op_13012"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13014 = const()[name = tensor<string, []>("op_13014"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_13_pad_type_0 = const()[name = tensor<string, []>("x_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_13_pad_0 = const()[name = tensor<string, []>("x_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [640, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1371133824)))];
            tensor<fp16, [640]> up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1372772288)))];
            tensor<fp16, [2, 640, 64, 64]> x_13_cast = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13014, groups = var_12518, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_13012, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16, x = input_755_cast)[name = tensor<string, []>("x_13_cast")];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_531_cast = add(x = x_13_cast, y = hidden_states_529_cast)[name = tensor<string, []>("hidden_states_531_cast")];
            tensor<int32, [5]> reshape_140_shape_0 = const()[name = tensor<string, []>("reshape_140_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_140_cast = reshape(shape = reshape_140_shape_0, x = hidden_states_531_cast)[name = tensor<string, []>("reshape_140_cast")];
            tensor<int32, [3]> reduce_mean_105_axes_0 = const()[name = tensor<string, []>("reduce_mean_105_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_105_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_105_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_105_cast = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast)[name = tensor<string, []>("reduce_mean_105_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_70_cast = sub(x = reshape_140_cast, y = reduce_mean_105_cast)[name = tensor<string, []>("sub_70_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_35_cast = square(x = sub_70_cast)[name = tensor<string, []>("square_35_cast")];
            tensor<int32, [3]> reduce_mean_107_axes_0 = const()[name = tensor<string, []>("reduce_mean_107_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_107_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_107_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_107_cast = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast)[name = tensor<string, []>("reduce_mean_107_cast")];
            tensor<fp16, []> add_70_y_0_to_fp16 = const()[name = tensor<string, []>("add_70_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_70_cast = add(x = reduce_mean_107_cast, y = add_70_y_0_to_fp16)[name = tensor<string, []>("add_70_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_35_cast = sqrt(x = add_70_cast)[name = tensor<string, []>("sqrt_35_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_35_cast = real_div(x = sub_70_cast, y = sqrt_35_cast)[name = tensor<string, []>("real_div_35_cast")];
            tensor<int32, [4]> reshape_141_shape_0 = const()[name = tensor<string, []>("reshape_141_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_141_cast = reshape(shape = reshape_141_shape_0, x = real_div_35_cast)[name = tensor<string, []>("reshape_141_cast")];
            tensor<fp16, [640]> add_71_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_71_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1372773632)))];
            tensor<fp16, [640]> add_71_beta_0_to_fp16 = const()[name = tensor<string, []>("add_71_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1372774976)))];
            tensor<fp16, []> add_71_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_71_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_71_cast = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_141_cast)[name = tensor<string, []>("add_71_cast")];
            tensor<int32, [2]> var_13036 = const()[name = tensor<string, []>("op_13036"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13038 = const()[name = tensor<string, []>("op_13038"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_533_pad_type_0 = const()[name = tensor<string, []>("hidden_states_533_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_533_pad_0 = const()[name = tensor<string, []>("hidden_states_533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1372776320))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373185984))), name = tensor<string, []>("up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373186560)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_533_cast = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_13038, groups = var_12518, pad = hidden_states_533_pad_0, pad_type = hidden_states_533_pad_type_0, strides = var_13036, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_71_cast)[name = tensor<string, []>("hidden_states_533_cast")];
            tensor<int32, [4]> var_13043 = const()[name = tensor<string, []>("op_13043"), val = tensor<int32, [4]>([2, 640, 1, 4096])];
            tensor<fp16, [2, 640, 1, 4096]> inputs_397_cast = reshape(shape = var_13043, x = hidden_states_533_cast)[name = tensor<string, []>("inputs_397_cast")];
            tensor<int32, [1]> var_13053 = const()[name = tensor<string, []>("op_13053"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_397_cast = reduce_mean(axes = var_13053, keep_dims = var_12513, x = inputs_397_cast)[name = tensor<string, []>("channels_mean_397_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_397_cast = sub(x = inputs_397_cast, y = channels_mean_397_cast)[name = tensor<string, []>("zero_mean_397_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_397_cast = mul(x = zero_mean_397_cast, y = zero_mean_397_cast)[name = tensor<string, []>("zero_mean_sq_397_cast")];
            tensor<int32, [1]> var_13057 = const()[name = tensor<string, []>("op_13057"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13058_cast = reduce_mean(axes = var_13057, keep_dims = var_12513, x = zero_mean_sq_397_cast)[name = tensor<string, []>("op_13058_cast")];
            tensor<fp16, []> var_13059_to_fp16 = const()[name = tensor<string, []>("op_13059_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13060_cast = add(x = var_13058_cast, y = var_13059_to_fp16)[name = tensor<string, []>("op_13060_cast")];
            tensor<fp16, []> denom_397_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_397_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_397_cast = rsqrt(epsilon = denom_397_epsilon_0_to_fp16, x = var_13060_cast)[name = tensor<string, []>("denom_397_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_397_cast = mul(x = zero_mean_397_cast, y = denom_397_cast)[name = tensor<string, []>("out_397_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13064_to_fp16 = const()[name = tensor<string, []>("op_13064_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373187904)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13065_cast = add(x = out_397_cast, y = var_13064_to_fp16)[name = tensor<string, []>("op_13065_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13067_to_fp16 = const()[name = tensor<string, []>("op_13067_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373189248)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_535_cast = mul(x = var_13065_cast, y = var_13067_to_fp16)[name = tensor<string, []>("hidden_states_535_cast")];
            tensor<int32, [2]> var_13074 = const()[name = tensor<string, []>("op_13074"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13076 = const()[name = tensor<string, []>("op_13076"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_265_pad_type_0 = const()[name = tensor<string, []>("q_265_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_265_pad_0 = const()[name = tensor<string, []>("q_265_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373190592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373497856))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_265_cast = conv(dilations = var_13076, groups = var_12518, pad = q_265_pad_0, pad_type = q_265_pad_type_0, strides = var_13074, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_535_cast)[name = tensor<string, []>("q_265_cast")];
            tensor<int32, [2]> var_13080 = const()[name = tensor<string, []>("op_13080"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13082 = const()[name = tensor<string, []>("op_13082"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_265_pad_type_0 = const()[name = tensor<string, []>("k_265_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_265_pad_0 = const()[name = tensor<string, []>("k_265_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373498048))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373805312))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_265_cast = conv(dilations = var_13082, groups = var_12518, pad = k_265_pad_0, pad_type = k_265_pad_type_0, strides = var_13080, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_535_cast)[name = tensor<string, []>("k_265_cast")];
            tensor<int32, [2]> var_13086 = const()[name = tensor<string, []>("op_13086"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13088 = const()[name = tensor<string, []>("op_13088"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_265_pad_type_0 = const()[name = tensor<string, []>("v_265_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_265_pad_0 = const()[name = tensor<string, []>("v_265_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373805504))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374215168))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_265_cast = conv(dilations = var_13088, groups = var_12518, pad = v_265_pad_0, pad_type = v_265_pad_type_0, strides = var_13086, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_535_cast)[name = tensor<string, []>("v_265_cast")];
            tensor<int32, [4]> var_13092 = const()[name = tensor<string, []>("op_13092"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13093_cast = reshape(shape = var_13092, x = q_265_cast)[name = tensor<string, []>("op_13093_cast")];
            tensor<int32, [4]> var_13094 = const()[name = tensor<string, []>("op_13094"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13095_cast = reshape(shape = var_13094, x = k_265_cast)[name = tensor<string, []>("op_13095_cast")];
            tensor<int32, [4]> var_13096 = const()[name = tensor<string, []>("op_13096"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13097_cast = reshape(shape = var_13096, x = v_265_cast)[name = tensor<string, []>("op_13097_cast")];
            tensor<bool, []> attn_weights_529_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_529_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_529_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_529_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_529_cast = matmul(transpose_x = attn_weights_529_transpose_x_0, transpose_y = attn_weights_529_transpose_y_0, x = var_13093_cast, y = var_13095_cast)[name = tensor<string, []>("attn_weights_529_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_531_cast = mul(x = attn_weights_529_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_531_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_13101_cast = softmax(axis = var_12502, x = attn_weights_531_cast)[name = tensor<string, []>("op_13101_cast")];
            tensor<bool, []> attn_265_transpose_x_0 = const()[name = tensor<string, []>("attn_265_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_265_transpose_y_0 = const()[name = tensor<string, []>("attn_265_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_265_cast = matmul(transpose_x = attn_265_transpose_x_0, transpose_y = attn_265_transpose_y_0, x = var_13097_cast, y = var_13101_cast)[name = tensor<string, []>("attn_265_cast")];
            tensor<int32, [4]> var_13105 = const()[name = tensor<string, []>("op_13105"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_771_cast = reshape(shape = var_13105, x = attn_265_cast)[name = tensor<string, []>("input_771_cast")];
            tensor<int32, [2]> var_13110 = const()[name = tensor<string, []>("op_13110"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13112 = const()[name = tensor<string, []>("op_13112"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13114_pad_type_0 = const()[name = tensor<string, []>("op_13114_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13114_pad_0 = const()[name = tensor<string, []>("op_13114_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374215744))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374625408))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374625984)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13114_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13112, groups = var_12518, pad = var_13114_pad_0, pad_type = var_13114_pad_type_0, strides = var_13110, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_771_cast)[name = tensor<string, []>("op_13114_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_399_cast = add(x = var_13114_cast, y = inputs_397_cast)[name = tensor<string, []>("inputs_399_cast")];
            tensor<int32, [1]> var_13118 = const()[name = tensor<string, []>("op_13118"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_399_cast = reduce_mean(axes = var_13118, keep_dims = var_12513, x = inputs_399_cast)[name = tensor<string, []>("channels_mean_399_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_399_cast = sub(x = inputs_399_cast, y = channels_mean_399_cast)[name = tensor<string, []>("zero_mean_399_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_399_cast = mul(x = zero_mean_399_cast, y = zero_mean_399_cast)[name = tensor<string, []>("zero_mean_sq_399_cast")];
            tensor<int32, [1]> var_13122 = const()[name = tensor<string, []>("op_13122"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13123_cast = reduce_mean(axes = var_13122, keep_dims = var_12513, x = zero_mean_sq_399_cast)[name = tensor<string, []>("op_13123_cast")];
            tensor<fp16, []> var_13124_to_fp16 = const()[name = tensor<string, []>("op_13124_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13125_cast = add(x = var_13123_cast, y = var_13124_to_fp16)[name = tensor<string, []>("op_13125_cast")];
            tensor<fp16, []> denom_399_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_399_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_399_cast = rsqrt(epsilon = denom_399_epsilon_0_to_fp16, x = var_13125_cast)[name = tensor<string, []>("denom_399_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_399_cast = mul(x = zero_mean_399_cast, y = denom_399_cast)[name = tensor<string, []>("out_399_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13129_to_fp16 = const()[name = tensor<string, []>("op_13129_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374627328)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13130_cast = add(x = out_399_cast, y = var_13129_to_fp16)[name = tensor<string, []>("op_13130_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13132_to_fp16 = const()[name = tensor<string, []>("op_13132_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374628672)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_537_cast = mul(x = var_13130_cast, y = var_13132_to_fp16)[name = tensor<string, []>("hidden_states_537_cast")];
            tensor<int32, [2]> var_13139 = const()[name = tensor<string, []>("op_13139"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13141 = const()[name = tensor<string, []>("op_13141"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_267_pad_type_0 = const()[name = tensor<string, []>("q_267_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_267_pad_0 = const()[name = tensor<string, []>("q_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374630016))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374937280))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_267_cast = conv(dilations = var_13141, groups = var_12518, pad = q_267_pad_0, pad_type = q_267_pad_type_0, strides = var_13139, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_537_cast)[name = tensor<string, []>("q_267_cast")];
            tensor<int32, [2]> var_13145 = const()[name = tensor<string, []>("op_13145"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13147 = const()[name = tensor<string, []>("op_13147"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_267_pad_type_0 = const()[name = tensor<string, []>("k_267_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_267_pad_0 = const()[name = tensor<string, []>("k_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1374937472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1375920576))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_267_cast = conv(dilations = var_13147, groups = var_12518, pad = k_267_pad_0, pad_type = k_267_pad_type_0, strides = var_13145, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_267_cast")];
            tensor<int32, [2]> var_13151 = const()[name = tensor<string, []>("op_13151"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13153 = const()[name = tensor<string, []>("op_13153"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_267_pad_type_0 = const()[name = tensor<string, []>("v_267_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_267_pad_0 = const()[name = tensor<string, []>("v_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1375920768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1376903872))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_267_cast = conv(dilations = var_13153, groups = var_12518, pad = v_267_pad_0, pad_type = v_267_pad_type_0, strides = var_13151, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_267_cast")];
            tensor<int32, [4]> var_13157 = const()[name = tensor<string, []>("op_13157"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13158_cast = reshape(shape = var_13157, x = q_267_cast)[name = tensor<string, []>("op_13158_cast")];
            tensor<int32, [4]> var_13159 = const()[name = tensor<string, []>("op_13159"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13160_cast = reshape(shape = var_13159, x = k_267_cast)[name = tensor<string, []>("op_13160_cast")];
            tensor<int32, [4]> var_13161 = const()[name = tensor<string, []>("op_13161"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13162_cast = reshape(shape = var_13161, x = v_267_cast)[name = tensor<string, []>("op_13162_cast")];
            tensor<bool, []> attn_weights_533_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_533_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_533_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_533_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_533_cast = matmul(transpose_x = attn_weights_533_transpose_x_0, transpose_y = attn_weights_533_transpose_y_0, x = var_13158_cast, y = var_13160_cast)[name = tensor<string, []>("attn_weights_533_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_535_cast = mul(x = attn_weights_533_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_535_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_13166_cast = softmax(axis = var_12502, x = attn_weights_535_cast)[name = tensor<string, []>("op_13166_cast")];
            tensor<bool, []> attn_267_transpose_x_0 = const()[name = tensor<string, []>("attn_267_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_267_transpose_y_0 = const()[name = tensor<string, []>("attn_267_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_267_cast = matmul(transpose_x = attn_267_transpose_x_0, transpose_y = attn_267_transpose_y_0, x = var_13162_cast, y = var_13166_cast)[name = tensor<string, []>("attn_267_cast")];
            tensor<int32, [4]> var_13170 = const()[name = tensor<string, []>("op_13170"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_773_cast = reshape(shape = var_13170, x = attn_267_cast)[name = tensor<string, []>("input_773_cast")];
            tensor<int32, [2]> var_13175 = const()[name = tensor<string, []>("op_13175"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13177 = const()[name = tensor<string, []>("op_13177"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13179_pad_type_0 = const()[name = tensor<string, []>("op_13179_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13179_pad_0 = const()[name = tensor<string, []>("op_13179_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1376904064))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1377211328))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1377211520)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13179_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13177, groups = var_12518, pad = var_13179_pad_0, pad_type = var_13179_pad_type_0, strides = var_13175, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_773_cast)[name = tensor<string, []>("op_13179_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_401_cast = add(x = var_13179_cast, y = inputs_399_cast)[name = tensor<string, []>("inputs_401_cast")];
            tensor<int32, [1]> var_13183 = const()[name = tensor<string, []>("op_13183"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_401_cast = reduce_mean(axes = var_13183, keep_dims = var_12513, x = inputs_401_cast)[name = tensor<string, []>("channels_mean_401_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_401_cast = sub(x = inputs_401_cast, y = channels_mean_401_cast)[name = tensor<string, []>("zero_mean_401_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_401_cast = mul(x = zero_mean_401_cast, y = zero_mean_401_cast)[name = tensor<string, []>("zero_mean_sq_401_cast")];
            tensor<int32, [1]> var_13187 = const()[name = tensor<string, []>("op_13187"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13188_cast = reduce_mean(axes = var_13187, keep_dims = var_12513, x = zero_mean_sq_401_cast)[name = tensor<string, []>("op_13188_cast")];
            tensor<fp16, []> var_13189_to_fp16 = const()[name = tensor<string, []>("op_13189_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13190_cast = add(x = var_13188_cast, y = var_13189_to_fp16)[name = tensor<string, []>("op_13190_cast")];
            tensor<fp16, []> denom_401_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_401_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_401_cast = rsqrt(epsilon = denom_401_epsilon_0_to_fp16, x = var_13190_cast)[name = tensor<string, []>("denom_401_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_401_cast = mul(x = zero_mean_401_cast, y = denom_401_cast)[name = tensor<string, []>("out_401_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13194_to_fp16 = const()[name = tensor<string, []>("op_13194_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1377212864)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13195_cast = add(x = out_401_cast, y = var_13194_to_fp16)[name = tensor<string, []>("op_13195_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13197_to_fp16 = const()[name = tensor<string, []>("op_13197_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1377214208)))];
            tensor<fp16, [2, 640, 1, 4096]> input_775_cast = mul(x = var_13195_cast, y = var_13197_to_fp16)[name = tensor<string, []>("input_775_cast")];
            tensor<int32, [2]> var_13205 = const()[name = tensor<string, []>("op_13205"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13207 = const()[name = tensor<string, []>("op_13207"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13209_pad_type_0 = const()[name = tensor<string, []>("op_13209_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13209_pad_0 = const()[name = tensor<string, []>("op_13209_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1377215552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1380492416))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1380492992)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_13209_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_13207, groups = var_12518, pad = var_13209_pad_0, pad_type = var_13209_pad_type_0, strides = var_13205, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_775_cast)[name = tensor<string, []>("op_13209_cast")];
            tensor<int32, [2]> var_13210_split_sizes_0 = const()[name = tensor<string, []>("op_13210_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_13210_axis_0 = const()[name = tensor<string, []>("op_13210_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_13210_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_13210_cast_1 = split(axis = var_13210_axis_0, split_sizes = var_13210_split_sizes_0, x = var_13209_cast)[name = tensor<string, []>("op_13210_cast")];
            tensor<string, []> var_13212_mode_0 = const()[name = tensor<string, []>("op_13212_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_13212_cast = gelu(mode = var_13212_mode_0, x = var_13210_cast_1)[name = tensor<string, []>("op_13212_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_777_cast = mul(x = var_13210_cast_0, y = var_13212_cast)[name = tensor<string, []>("input_777_cast")];
            tensor<int32, [2]> var_13216 = const()[name = tensor<string, []>("op_13216"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13218 = const()[name = tensor<string, []>("op_13218"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13220_pad_type_0 = const()[name = tensor<string, []>("op_13220_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13220_pad_0 = const()[name = tensor<string, []>("op_13220_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1380503296))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382141760))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382142336)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13220_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13218, groups = var_12518, pad = var_13220_pad_0, pad_type = var_13220_pad_type_0, strides = var_13216, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_777_cast)[name = tensor<string, []>("op_13220_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_403_cast = add(x = var_13220_cast, y = inputs_401_cast)[name = tensor<string, []>("inputs_403_cast")];
            tensor<int32, [1]> var_13230 = const()[name = tensor<string, []>("op_13230"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_403_cast = reduce_mean(axes = var_13230, keep_dims = var_12513, x = inputs_403_cast)[name = tensor<string, []>("channels_mean_403_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_403_cast = sub(x = inputs_403_cast, y = channels_mean_403_cast)[name = tensor<string, []>("zero_mean_403_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_403_cast = mul(x = zero_mean_403_cast, y = zero_mean_403_cast)[name = tensor<string, []>("zero_mean_sq_403_cast")];
            tensor<int32, [1]> var_13234 = const()[name = tensor<string, []>("op_13234"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13235_cast = reduce_mean(axes = var_13234, keep_dims = var_12513, x = zero_mean_sq_403_cast)[name = tensor<string, []>("op_13235_cast")];
            tensor<fp16, []> var_13236_to_fp16 = const()[name = tensor<string, []>("op_13236_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13237_cast = add(x = var_13235_cast, y = var_13236_to_fp16)[name = tensor<string, []>("op_13237_cast")];
            tensor<fp16, []> denom_403_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_403_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_403_cast = rsqrt(epsilon = denom_403_epsilon_0_to_fp16, x = var_13237_cast)[name = tensor<string, []>("denom_403_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_403_cast = mul(x = zero_mean_403_cast, y = denom_403_cast)[name = tensor<string, []>("out_403_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13241_to_fp16 = const()[name = tensor<string, []>("op_13241_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382143680)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13242_cast = add(x = out_403_cast, y = var_13241_to_fp16)[name = tensor<string, []>("op_13242_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13244_to_fp16 = const()[name = tensor<string, []>("op_13244_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382145024)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_541_cast = mul(x = var_13242_cast, y = var_13244_to_fp16)[name = tensor<string, []>("hidden_states_541_cast")];
            tensor<int32, [2]> var_13251 = const()[name = tensor<string, []>("op_13251"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13253 = const()[name = tensor<string, []>("op_13253"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_269_pad_type_0 = const()[name = tensor<string, []>("q_269_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_269_pad_0 = const()[name = tensor<string, []>("q_269_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382146368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382453632))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_269_cast = conv(dilations = var_13253, groups = var_12518, pad = q_269_pad_0, pad_type = q_269_pad_type_0, strides = var_13251, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_541_cast)[name = tensor<string, []>("q_269_cast")];
            tensor<int32, [2]> var_13257 = const()[name = tensor<string, []>("op_13257"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13259 = const()[name = tensor<string, []>("op_13259"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_269_pad_type_0 = const()[name = tensor<string, []>("k_269_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_269_pad_0 = const()[name = tensor<string, []>("k_269_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382453824))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382863488))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_269_cast = conv(dilations = var_13259, groups = var_12518, pad = k_269_pad_0, pad_type = k_269_pad_type_0, strides = var_13257, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_541_cast)[name = tensor<string, []>("k_269_cast")];
            tensor<int32, [2]> var_13263 = const()[name = tensor<string, []>("op_13263"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13265 = const()[name = tensor<string, []>("op_13265"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_269_pad_type_0 = const()[name = tensor<string, []>("v_269_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_269_pad_0 = const()[name = tensor<string, []>("v_269_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1382864064))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383273728))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_269_cast = conv(dilations = var_13265, groups = var_12518, pad = v_269_pad_0, pad_type = v_269_pad_type_0, strides = var_13263, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_541_cast)[name = tensor<string, []>("v_269_cast")];
            tensor<int32, [4]> var_13269 = const()[name = tensor<string, []>("op_13269"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13270_cast = reshape(shape = var_13269, x = q_269_cast)[name = tensor<string, []>("op_13270_cast")];
            tensor<int32, [4]> var_13271 = const()[name = tensor<string, []>("op_13271"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13272_cast = reshape(shape = var_13271, x = k_269_cast)[name = tensor<string, []>("op_13272_cast")];
            tensor<int32, [4]> var_13273 = const()[name = tensor<string, []>("op_13273"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13274_cast = reshape(shape = var_13273, x = v_269_cast)[name = tensor<string, []>("op_13274_cast")];
            tensor<bool, []> attn_weights_537_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_537_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_537_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_537_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_537_cast = matmul(transpose_x = attn_weights_537_transpose_x_0, transpose_y = attn_weights_537_transpose_y_0, x = var_13270_cast, y = var_13272_cast)[name = tensor<string, []>("attn_weights_537_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_539_cast = mul(x = attn_weights_537_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_539_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_13278_cast = softmax(axis = var_12502, x = attn_weights_539_cast)[name = tensor<string, []>("op_13278_cast")];
            tensor<bool, []> attn_269_transpose_x_0 = const()[name = tensor<string, []>("attn_269_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_269_transpose_y_0 = const()[name = tensor<string, []>("attn_269_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_269_cast = matmul(transpose_x = attn_269_transpose_x_0, transpose_y = attn_269_transpose_y_0, x = var_13274_cast, y = var_13278_cast)[name = tensor<string, []>("attn_269_cast")];
            tensor<int32, [4]> var_13282 = const()[name = tensor<string, []>("op_13282"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_779_cast = reshape(shape = var_13282, x = attn_269_cast)[name = tensor<string, []>("input_779_cast")];
            tensor<int32, [2]> var_13287 = const()[name = tensor<string, []>("op_13287"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13289 = const()[name = tensor<string, []>("op_13289"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13291_pad_type_0 = const()[name = tensor<string, []>("op_13291_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13291_pad_0 = const()[name = tensor<string, []>("op_13291_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383274304))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383683968))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383684544)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13291_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13289, groups = var_12518, pad = var_13291_pad_0, pad_type = var_13291_pad_type_0, strides = var_13287, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_779_cast)[name = tensor<string, []>("op_13291_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_405_cast = add(x = var_13291_cast, y = inputs_403_cast)[name = tensor<string, []>("inputs_405_cast")];
            tensor<int32, [1]> var_13295 = const()[name = tensor<string, []>("op_13295"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_405_cast = reduce_mean(axes = var_13295, keep_dims = var_12513, x = inputs_405_cast)[name = tensor<string, []>("channels_mean_405_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_405_cast = sub(x = inputs_405_cast, y = channels_mean_405_cast)[name = tensor<string, []>("zero_mean_405_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_405_cast = mul(x = zero_mean_405_cast, y = zero_mean_405_cast)[name = tensor<string, []>("zero_mean_sq_405_cast")];
            tensor<int32, [1]> var_13299 = const()[name = tensor<string, []>("op_13299"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13300_cast = reduce_mean(axes = var_13299, keep_dims = var_12513, x = zero_mean_sq_405_cast)[name = tensor<string, []>("op_13300_cast")];
            tensor<fp16, []> var_13301_to_fp16 = const()[name = tensor<string, []>("op_13301_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13302_cast = add(x = var_13300_cast, y = var_13301_to_fp16)[name = tensor<string, []>("op_13302_cast")];
            tensor<fp16, []> denom_405_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_405_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_405_cast = rsqrt(epsilon = denom_405_epsilon_0_to_fp16, x = var_13302_cast)[name = tensor<string, []>("denom_405_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_405_cast = mul(x = zero_mean_405_cast, y = denom_405_cast)[name = tensor<string, []>("out_405_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13306_to_fp16 = const()[name = tensor<string, []>("op_13306_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383685888)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13307_cast = add(x = out_405_cast, y = var_13306_to_fp16)[name = tensor<string, []>("op_13307_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13309_to_fp16 = const()[name = tensor<string, []>("op_13309_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383687232)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_543_cast = mul(x = var_13307_cast, y = var_13309_to_fp16)[name = tensor<string, []>("hidden_states_543_cast")];
            tensor<int32, [2]> var_13316 = const()[name = tensor<string, []>("op_13316"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13318 = const()[name = tensor<string, []>("op_13318"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_271_pad_type_0 = const()[name = tensor<string, []>("q_271_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_271_pad_0 = const()[name = tensor<string, []>("q_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383688576))), lut = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383893440))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_271_cast = conv(dilations = var_13318, groups = var_12518, pad = q_271_pad_0, pad_type = q_271_pad_type_0, strides = var_13316, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_543_cast)[name = tensor<string, []>("q_271_cast")];
            tensor<int32, [2]> var_13322 = const()[name = tensor<string, []>("op_13322"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13324 = const()[name = tensor<string, []>("op_13324"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_271_pad_type_0 = const()[name = tensor<string, []>("k_271_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_271_pad_0 = const()[name = tensor<string, []>("k_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1383893568))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1384876672))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_271_cast = conv(dilations = var_13324, groups = var_12518, pad = k_271_pad_0, pad_type = k_271_pad_type_0, strides = var_13322, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_271_cast")];
            tensor<int32, [2]> var_13328 = const()[name = tensor<string, []>("op_13328"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13330 = const()[name = tensor<string, []>("op_13330"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_271_pad_type_0 = const()[name = tensor<string, []>("v_271_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_271_pad_0 = const()[name = tensor<string, []>("v_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1384876864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1385859968))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_271_cast = conv(dilations = var_13330, groups = var_12518, pad = v_271_pad_0, pad_type = v_271_pad_type_0, strides = var_13328, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_271_cast")];
            tensor<int32, [4]> var_13334 = const()[name = tensor<string, []>("op_13334"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13335_cast = reshape(shape = var_13334, x = q_271_cast)[name = tensor<string, []>("op_13335_cast")];
            tensor<int32, [4]> var_13336 = const()[name = tensor<string, []>("op_13336"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13337_cast = reshape(shape = var_13336, x = k_271_cast)[name = tensor<string, []>("op_13337_cast")];
            tensor<int32, [4]> var_13338 = const()[name = tensor<string, []>("op_13338"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13339_cast = reshape(shape = var_13338, x = v_271_cast)[name = tensor<string, []>("op_13339_cast")];
            tensor<bool, []> attn_weights_541_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_541_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_541_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_541_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_541_cast = matmul(transpose_x = attn_weights_541_transpose_x_0, transpose_y = attn_weights_541_transpose_y_0, x = var_13335_cast, y = var_13337_cast)[name = tensor<string, []>("attn_weights_541_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_543_cast = mul(x = attn_weights_541_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_543_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_13343_cast = softmax(axis = var_12502, x = attn_weights_543_cast)[name = tensor<string, []>("op_13343_cast")];
            tensor<bool, []> attn_271_transpose_x_0 = const()[name = tensor<string, []>("attn_271_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_271_transpose_y_0 = const()[name = tensor<string, []>("attn_271_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_271_cast = matmul(transpose_x = attn_271_transpose_x_0, transpose_y = attn_271_transpose_y_0, x = var_13339_cast, y = var_13343_cast)[name = tensor<string, []>("attn_271_cast")];
            tensor<int32, [4]> var_13347 = const()[name = tensor<string, []>("op_13347"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_781_cast = reshape(shape = var_13347, x = attn_271_cast)[name = tensor<string, []>("input_781_cast")];
            tensor<int32, [2]> var_13352 = const()[name = tensor<string, []>("op_13352"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13354 = const()[name = tensor<string, []>("op_13354"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13356_pad_type_0 = const()[name = tensor<string, []>("op_13356_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13356_pad_0 = const()[name = tensor<string, []>("op_13356_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1385860160))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1386167424))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1386167616)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13356_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13354, groups = var_12518, pad = var_13356_pad_0, pad_type = var_13356_pad_type_0, strides = var_13352, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_781_cast)[name = tensor<string, []>("op_13356_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_407_cast = add(x = var_13356_cast, y = inputs_405_cast)[name = tensor<string, []>("inputs_407_cast")];
            tensor<int32, [1]> var_13360 = const()[name = tensor<string, []>("op_13360"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_407_cast = reduce_mean(axes = var_13360, keep_dims = var_12513, x = inputs_407_cast)[name = tensor<string, []>("channels_mean_407_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_407_cast = sub(x = inputs_407_cast, y = channels_mean_407_cast)[name = tensor<string, []>("zero_mean_407_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_407_cast = mul(x = zero_mean_407_cast, y = zero_mean_407_cast)[name = tensor<string, []>("zero_mean_sq_407_cast")];
            tensor<int32, [1]> var_13364 = const()[name = tensor<string, []>("op_13364"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13365_cast = reduce_mean(axes = var_13364, keep_dims = var_12513, x = zero_mean_sq_407_cast)[name = tensor<string, []>("op_13365_cast")];
            tensor<fp16, []> var_13366_to_fp16 = const()[name = tensor<string, []>("op_13366_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13367_cast = add(x = var_13365_cast, y = var_13366_to_fp16)[name = tensor<string, []>("op_13367_cast")];
            tensor<fp16, []> denom_407_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_407_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_407_cast = rsqrt(epsilon = denom_407_epsilon_0_to_fp16, x = var_13367_cast)[name = tensor<string, []>("denom_407_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_407_cast = mul(x = zero_mean_407_cast, y = denom_407_cast)[name = tensor<string, []>("out_407_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13371_to_fp16 = const()[name = tensor<string, []>("op_13371_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1386168960)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13372_cast = add(x = out_407_cast, y = var_13371_to_fp16)[name = tensor<string, []>("op_13372_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13374_to_fp16 = const()[name = tensor<string, []>("op_13374_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1386170304)))];
            tensor<fp16, [2, 640, 1, 4096]> input_783_cast = mul(x = var_13372_cast, y = var_13374_to_fp16)[name = tensor<string, []>("input_783_cast")];
            tensor<int32, [2]> var_13382 = const()[name = tensor<string, []>("op_13382"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13384 = const()[name = tensor<string, []>("op_13384"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13386_pad_type_0 = const()[name = tensor<string, []>("op_13386_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13386_pad_0 = const()[name = tensor<string, []>("op_13386_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1386171648))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1389448512))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1389449088)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_13386_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_13384, groups = var_12518, pad = var_13386_pad_0, pad_type = var_13386_pad_type_0, strides = var_13382, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_783_cast)[name = tensor<string, []>("op_13386_cast")];
            tensor<int32, [2]> var_13387_split_sizes_0 = const()[name = tensor<string, []>("op_13387_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_13387_axis_0 = const()[name = tensor<string, []>("op_13387_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_13387_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_13387_cast_1 = split(axis = var_13387_axis_0, split_sizes = var_13387_split_sizes_0, x = var_13386_cast)[name = tensor<string, []>("op_13387_cast")];
            tensor<string, []> var_13389_mode_0 = const()[name = tensor<string, []>("op_13389_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_13389_cast = gelu(mode = var_13389_mode_0, x = var_13387_cast_1)[name = tensor<string, []>("op_13389_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_785_cast = mul(x = var_13387_cast_0, y = var_13389_cast)[name = tensor<string, []>("input_785_cast")];
            tensor<int32, [2]> var_13393 = const()[name = tensor<string, []>("op_13393"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13395 = const()[name = tensor<string, []>("op_13395"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13397_pad_type_0 = const()[name = tensor<string, []>("op_13397_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13397_pad_0 = const()[name = tensor<string, []>("op_13397_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1389459392))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391097856))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391098432)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13397_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13395, groups = var_12518, pad = var_13397_pad_0, pad_type = var_13397_pad_type_0, strides = var_13393, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_785_cast)[name = tensor<string, []>("op_13397_cast")];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_547_cast = add(x = var_13397_cast, y = inputs_407_cast)[name = tensor<string, []>("hidden_states_547_cast")];
            tensor<int32, [4]> var_13399 = const()[name = tensor<string, []>("op_13399"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> input_787_cast = reshape(shape = var_13399, x = hidden_states_547_cast)[name = tensor<string, []>("input_787_cast")];
            tensor<int32, [2]> var_13403 = const()[name = tensor<string, []>("op_13403"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13405 = const()[name = tensor<string, []>("op_13405"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_549_pad_type_0 = const()[name = tensor<string, []>("hidden_states_549_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_549_pad_0 = const()[name = tensor<string, []>("hidden_states_549_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391099776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391509440))), name = tensor<string, []>("up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391510016)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_549_cast = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_13405, groups = var_12518, pad = hidden_states_549_pad_0, pad_type = hidden_states_549_pad_type_0, strides = var_13403, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_787_cast)[name = tensor<string, []>("hidden_states_549_cast")];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_551_cast = add(x = hidden_states_549_cast, y = hidden_states_531_cast)[name = tensor<string, []>("hidden_states_551_cast")];
            tensor<bool, []> input_789_interleave_0 = const()[name = tensor<string, []>("input_789_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 960, 64, 64]> input_789_cast = concat(axis = var_12518, interleave = input_789_interleave_0, values = (hidden_states_551_cast, input_45_cast))[name = tensor<string, []>("input_789_cast")];
            tensor<int32, [5]> reshape_144_shape_0 = const()[name = tensor<string, []>("reshape_144_shape_0"), val = tensor<int32, [5]>([2, 32, 30, 64, 64])];
            tensor<fp16, [2, 32, 30, 64, 64]> reshape_144_cast = reshape(shape = reshape_144_shape_0, x = input_789_cast)[name = tensor<string, []>("reshape_144_cast")];
            tensor<int32, [3]> reduce_mean_108_axes_0 = const()[name = tensor<string, []>("reduce_mean_108_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_108_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_108_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_108_cast = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast)[name = tensor<string, []>("reduce_mean_108_cast")];
            tensor<fp16, [2, 32, 30, 64, 64]> sub_72_cast = sub(x = reshape_144_cast, y = reduce_mean_108_cast)[name = tensor<string, []>("sub_72_cast")];
            tensor<fp16, [2, 32, 30, 64, 64]> square_36_cast = square(x = sub_72_cast)[name = tensor<string, []>("square_36_cast")];
            tensor<int32, [3]> reduce_mean_110_axes_0 = const()[name = tensor<string, []>("reduce_mean_110_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_110_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_110_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_110_cast = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast)[name = tensor<string, []>("reduce_mean_110_cast")];
            tensor<fp16, []> add_72_y_0_to_fp16 = const()[name = tensor<string, []>("add_72_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_72_cast = add(x = reduce_mean_110_cast, y = add_72_y_0_to_fp16)[name = tensor<string, []>("add_72_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_36_cast = sqrt(x = add_72_cast)[name = tensor<string, []>("sqrt_36_cast")];
            tensor<fp16, [2, 32, 30, 64, 64]> real_div_36_cast = real_div(x = sub_72_cast, y = sqrt_36_cast)[name = tensor<string, []>("real_div_36_cast")];
            tensor<int32, [4]> reshape_145_shape_0 = const()[name = tensor<string, []>("reshape_145_shape_0"), val = tensor<int32, [4]>([2, 960, 64, 64])];
            tensor<fp16, [2, 960, 64, 64]> reshape_145_cast = reshape(shape = reshape_145_shape_0, x = real_div_36_cast)[name = tensor<string, []>("reshape_145_cast")];
            tensor<fp16, [960]> add_73_mean_0_to_fp16 = const()[name = tensor<string, []>("add_73_mean_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391511360)))];
            tensor<fp16, [960]> add_73_variance_0_to_fp16 = const()[name = tensor<string, []>("add_73_variance_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391513344)))];
            tensor<fp16, [960]> add_73_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_73_gamma_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391515328)))];
            tensor<fp16, [960]> add_73_beta_0_to_fp16 = const()[name = tensor<string, []>("add_73_beta_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391517312)))];
            tensor<fp16, []> add_73_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_73_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 960, 64, 64]> add_73_cast = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_145_cast)[name = tensor<string, []>("add_73_cast")];
            tensor<fp16, [2, 960, 64, 64]> input_793_cast = silu(x = add_73_cast)[name = tensor<string, []>("input_793_cast")];
            tensor<int32, [2]> var_13423 = const()[name = tensor<string, []>("op_13423"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13425 = const()[name = tensor<string, []>("op_13425"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_553_pad_type_0 = const()[name = tensor<string, []>("hidden_states_553_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_553_pad_0 = const()[name = tensor<string, []>("hidden_states_553_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 960, 3, 3]> up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [5529600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1391519296))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397048960))), name = tensor<string, []>("up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 960, 3, 3])];
            tensor<fp16, [640]> up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397049536)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_553_cast = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_13425, groups = var_12518, pad = hidden_states_553_pad_0, pad_type = hidden_states_553_pad_type_0, strides = var_13423, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized, x = input_793_cast)[name = tensor<string, []>("hidden_states_553_cast")];
            tensor<int32, [2]> var_13431 = const()[name = tensor<string, []>("op_13431"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13433 = const()[name = tensor<string, []>("op_13433"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_27_pad_type_0 = const()[name = tensor<string, []>("temb_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_27_pad_0 = const()[name = tensor<string, []>("temb_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [819200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397050880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397870144))), name = tensor<string, []>("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397870720)))];
            tensor<fp16, [2, 640, 1, 1]> temb_27_cast = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13433, groups = var_12518, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_13431, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_27_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_797_cast = add(x = hidden_states_553_cast, y = temb_27_cast)[name = tensor<string, []>("input_797_cast")];
            tensor<int32, [5]> reshape_148_shape_0 = const()[name = tensor<string, []>("reshape_148_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_148_cast = reshape(shape = reshape_148_shape_0, x = input_797_cast)[name = tensor<string, []>("reshape_148_cast")];
            tensor<int32, [3]> reduce_mean_111_axes_0 = const()[name = tensor<string, []>("reduce_mean_111_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_111_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_111_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_111_cast = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast)[name = tensor<string, []>("reduce_mean_111_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_74_cast = sub(x = reshape_148_cast, y = reduce_mean_111_cast)[name = tensor<string, []>("sub_74_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_37_cast = square(x = sub_74_cast)[name = tensor<string, []>("square_37_cast")];
            tensor<int32, [3]> reduce_mean_113_axes_0 = const()[name = tensor<string, []>("reduce_mean_113_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_113_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_113_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_113_cast = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast)[name = tensor<string, []>("reduce_mean_113_cast")];
            tensor<fp16, []> add_74_y_0_to_fp16 = const()[name = tensor<string, []>("add_74_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_74_cast = add(x = reduce_mean_113_cast, y = add_74_y_0_to_fp16)[name = tensor<string, []>("add_74_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_37_cast = sqrt(x = add_74_cast)[name = tensor<string, []>("sqrt_37_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_37_cast = real_div(x = sub_74_cast, y = sqrt_37_cast)[name = tensor<string, []>("real_div_37_cast")];
            tensor<int32, [4]> reshape_149_shape_0 = const()[name = tensor<string, []>("reshape_149_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_149_cast = reshape(shape = reshape_149_shape_0, x = real_div_37_cast)[name = tensor<string, []>("reshape_149_cast")];
            tensor<fp16, [640]> add_75_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_75_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397872064)))];
            tensor<fp16, [640]> add_75_beta_0_to_fp16 = const()[name = tensor<string, []>("add_75_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397873408)))];
            tensor<fp16, []> add_75_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_75_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_75_cast = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_149_cast)[name = tensor<string, []>("add_75_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_801_cast = silu(x = add_75_cast)[name = tensor<string, []>("input_801_cast")];
            tensor<int32, [2]> var_13443 = const()[name = tensor<string, []>("op_13443"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13445 = const()[name = tensor<string, []>("op_13445"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_555_pad_type_0 = const()[name = tensor<string, []>("hidden_states_555_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_555_pad_0 = const()[name = tensor<string, []>("hidden_states_555_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3686400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1397874752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1401561216))), name = tensor<string, []>("up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1401561792)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_555_cast = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_13445, groups = var_12518, pad = hidden_states_555_pad_0, pad_type = hidden_states_555_pad_type_0, strides = var_13443, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized, x = input_801_cast)[name = tensor<string, []>("hidden_states_555_cast")];
            tensor<int32, [2]> var_13450 = const()[name = tensor<string, []>("op_13450"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13452 = const()[name = tensor<string, []>("op_13452"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_15_pad_type_0 = const()[name = tensor<string, []>("x_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_15_pad_0 = const()[name = tensor<string, []>("x_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 960, 1, 1]> up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [640, 960, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1401563136)))];
            tensor<fp16, [640]> up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1402792000)))];
            tensor<fp16, [2, 640, 64, 64]> x_15_cast = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_13452, groups = var_12518, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_13450, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16, x = input_789_cast)[name = tensor<string, []>("x_15_cast")];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_557_cast = add(x = x_15_cast, y = hidden_states_555_cast)[name = tensor<string, []>("hidden_states_557_cast")];
            tensor<int32, [5]> reshape_152_shape_0 = const()[name = tensor<string, []>("reshape_152_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_152_cast = reshape(shape = reshape_152_shape_0, x = hidden_states_557_cast)[name = tensor<string, []>("reshape_152_cast")];
            tensor<int32, [3]> reduce_mean_114_axes_0 = const()[name = tensor<string, []>("reduce_mean_114_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_114_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_114_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_114_cast = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast)[name = tensor<string, []>("reduce_mean_114_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_76_cast = sub(x = reshape_152_cast, y = reduce_mean_114_cast)[name = tensor<string, []>("sub_76_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_38_cast = square(x = sub_76_cast)[name = tensor<string, []>("square_38_cast")];
            tensor<int32, [3]> reduce_mean_116_axes_0 = const()[name = tensor<string, []>("reduce_mean_116_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_116_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_116_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_116_cast = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast)[name = tensor<string, []>("reduce_mean_116_cast")];
            tensor<fp16, []> add_76_y_0_to_fp16 = const()[name = tensor<string, []>("add_76_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_76_cast = add(x = reduce_mean_116_cast, y = add_76_y_0_to_fp16)[name = tensor<string, []>("add_76_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_38_cast = sqrt(x = add_76_cast)[name = tensor<string, []>("sqrt_38_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_38_cast = real_div(x = sub_76_cast, y = sqrt_38_cast)[name = tensor<string, []>("real_div_38_cast")];
            tensor<int32, [4]> reshape_153_shape_0 = const()[name = tensor<string, []>("reshape_153_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_153_cast = reshape(shape = reshape_153_shape_0, x = real_div_38_cast)[name = tensor<string, []>("reshape_153_cast")];
            tensor<fp16, [640]> add_77_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_77_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1402793344)))];
            tensor<fp16, [640]> add_77_beta_0_to_fp16 = const()[name = tensor<string, []>("add_77_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1402794688)))];
            tensor<fp16, []> add_77_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_77_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_77_cast = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_153_cast)[name = tensor<string, []>("add_77_cast")];
            tensor<int32, [2]> var_13474 = const()[name = tensor<string, []>("op_13474"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13476 = const()[name = tensor<string, []>("op_13476"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_559_pad_type_0 = const()[name = tensor<string, []>("hidden_states_559_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_559_pad_0 = const()[name = tensor<string, []>("hidden_states_559_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1402796032))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403205696))), name = tensor<string, []>("up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403206272)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_559_cast = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_13476, groups = var_12518, pad = hidden_states_559_pad_0, pad_type = hidden_states_559_pad_type_0, strides = var_13474, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized, x = add_77_cast)[name = tensor<string, []>("hidden_states_559_cast")];
            tensor<int32, [4]> var_13481 = const()[name = tensor<string, []>("op_13481"), val = tensor<int32, [4]>([2, 640, 1, 4096])];
            tensor<fp16, [2, 640, 1, 4096]> inputs_409_cast = reshape(shape = var_13481, x = hidden_states_559_cast)[name = tensor<string, []>("inputs_409_cast")];
            tensor<int32, [1]> var_13491 = const()[name = tensor<string, []>("op_13491"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_409_cast = reduce_mean(axes = var_13491, keep_dims = var_12513, x = inputs_409_cast)[name = tensor<string, []>("channels_mean_409_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_409_cast = sub(x = inputs_409_cast, y = channels_mean_409_cast)[name = tensor<string, []>("zero_mean_409_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_409_cast = mul(x = zero_mean_409_cast, y = zero_mean_409_cast)[name = tensor<string, []>("zero_mean_sq_409_cast")];
            tensor<int32, [1]> var_13495 = const()[name = tensor<string, []>("op_13495"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13496_cast = reduce_mean(axes = var_13495, keep_dims = var_12513, x = zero_mean_sq_409_cast)[name = tensor<string, []>("op_13496_cast")];
            tensor<fp16, []> var_13497_to_fp16 = const()[name = tensor<string, []>("op_13497_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13498_cast = add(x = var_13496_cast, y = var_13497_to_fp16)[name = tensor<string, []>("op_13498_cast")];
            tensor<fp16, []> denom_409_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_409_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_409_cast = rsqrt(epsilon = denom_409_epsilon_0_to_fp16, x = var_13498_cast)[name = tensor<string, []>("denom_409_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_409_cast = mul(x = zero_mean_409_cast, y = denom_409_cast)[name = tensor<string, []>("out_409_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13502_to_fp16 = const()[name = tensor<string, []>("op_13502_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403207616)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13503_cast = add(x = out_409_cast, y = var_13502_to_fp16)[name = tensor<string, []>("op_13503_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13505_to_fp16 = const()[name = tensor<string, []>("op_13505_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403208960)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_561_cast = mul(x = var_13503_cast, y = var_13505_to_fp16)[name = tensor<string, []>("hidden_states_561_cast")];
            tensor<int32, [2]> var_13512 = const()[name = tensor<string, []>("op_13512"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13514 = const()[name = tensor<string, []>("op_13514"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_273_pad_type_0 = const()[name = tensor<string, []>("q_273_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_273_pad_0 = const()[name = tensor<string, []>("q_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403210304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403517568))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_273_cast = conv(dilations = var_13514, groups = var_12518, pad = q_273_pad_0, pad_type = q_273_pad_type_0, strides = var_13512, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_561_cast)[name = tensor<string, []>("q_273_cast")];
            tensor<int32, [2]> var_13518 = const()[name = tensor<string, []>("op_13518"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13520 = const()[name = tensor<string, []>("op_13520"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_273_pad_type_0 = const()[name = tensor<string, []>("k_273_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_273_pad_0 = const()[name = tensor<string, []>("k_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403517760))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403825024))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_273_cast = conv(dilations = var_13520, groups = var_12518, pad = k_273_pad_0, pad_type = k_273_pad_type_0, strides = var_13518, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_561_cast)[name = tensor<string, []>("k_273_cast")];
            tensor<int32, [2]> var_13524 = const()[name = tensor<string, []>("op_13524"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13526 = const()[name = tensor<string, []>("op_13526"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_273_pad_type_0 = const()[name = tensor<string, []>("v_273_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_273_pad_0 = const()[name = tensor<string, []>("v_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403825216))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404234880))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_273_cast = conv(dilations = var_13526, groups = var_12518, pad = v_273_pad_0, pad_type = v_273_pad_type_0, strides = var_13524, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_561_cast)[name = tensor<string, []>("v_273_cast")];
            tensor<int32, [4]> var_13530 = const()[name = tensor<string, []>("op_13530"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13531_cast = reshape(shape = var_13530, x = q_273_cast)[name = tensor<string, []>("op_13531_cast")];
            tensor<int32, [4]> var_13532 = const()[name = tensor<string, []>("op_13532"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13533_cast = reshape(shape = var_13532, x = k_273_cast)[name = tensor<string, []>("op_13533_cast")];
            tensor<int32, [4]> var_13534 = const()[name = tensor<string, []>("op_13534"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13535_cast = reshape(shape = var_13534, x = v_273_cast)[name = tensor<string, []>("op_13535_cast")];
            tensor<bool, []> attn_weights_545_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_545_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_545_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_545_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_545_cast = matmul(transpose_x = attn_weights_545_transpose_x_0, transpose_y = attn_weights_545_transpose_y_0, x = var_13531_cast, y = var_13533_cast)[name = tensor<string, []>("attn_weights_545_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_547_cast = mul(x = attn_weights_545_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_547_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_13539_cast = softmax(axis = var_12502, x = attn_weights_547_cast)[name = tensor<string, []>("op_13539_cast")];
            tensor<bool, []> attn_273_transpose_x_0 = const()[name = tensor<string, []>("attn_273_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_273_transpose_y_0 = const()[name = tensor<string, []>("attn_273_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_273_cast = matmul(transpose_x = attn_273_transpose_x_0, transpose_y = attn_273_transpose_y_0, x = var_13535_cast, y = var_13539_cast)[name = tensor<string, []>("attn_273_cast")];
            tensor<int32, [4]> var_13543 = const()[name = tensor<string, []>("op_13543"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_805_cast = reshape(shape = var_13543, x = attn_273_cast)[name = tensor<string, []>("input_805_cast")];
            tensor<int32, [2]> var_13548 = const()[name = tensor<string, []>("op_13548"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13550 = const()[name = tensor<string, []>("op_13550"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13552_pad_type_0 = const()[name = tensor<string, []>("op_13552_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13552_pad_0 = const()[name = tensor<string, []>("op_13552_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404235456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404645120))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404645696)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13552_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13550, groups = var_12518, pad = var_13552_pad_0, pad_type = var_13552_pad_type_0, strides = var_13548, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_805_cast)[name = tensor<string, []>("op_13552_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_411_cast = add(x = var_13552_cast, y = inputs_409_cast)[name = tensor<string, []>("inputs_411_cast")];
            tensor<int32, [1]> var_13556 = const()[name = tensor<string, []>("op_13556"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_411_cast = reduce_mean(axes = var_13556, keep_dims = var_12513, x = inputs_411_cast)[name = tensor<string, []>("channels_mean_411_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_411_cast = sub(x = inputs_411_cast, y = channels_mean_411_cast)[name = tensor<string, []>("zero_mean_411_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_411_cast = mul(x = zero_mean_411_cast, y = zero_mean_411_cast)[name = tensor<string, []>("zero_mean_sq_411_cast")];
            tensor<int32, [1]> var_13560 = const()[name = tensor<string, []>("op_13560"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13561_cast = reduce_mean(axes = var_13560, keep_dims = var_12513, x = zero_mean_sq_411_cast)[name = tensor<string, []>("op_13561_cast")];
            tensor<fp16, []> var_13562_to_fp16 = const()[name = tensor<string, []>("op_13562_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13563_cast = add(x = var_13561_cast, y = var_13562_to_fp16)[name = tensor<string, []>("op_13563_cast")];
            tensor<fp16, []> denom_411_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_411_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_411_cast = rsqrt(epsilon = denom_411_epsilon_0_to_fp16, x = var_13563_cast)[name = tensor<string, []>("denom_411_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_411_cast = mul(x = zero_mean_411_cast, y = denom_411_cast)[name = tensor<string, []>("out_411_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13567_to_fp16 = const()[name = tensor<string, []>("op_13567_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404647040)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13568_cast = add(x = out_411_cast, y = var_13567_to_fp16)[name = tensor<string, []>("op_13568_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13570_to_fp16 = const()[name = tensor<string, []>("op_13570_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404648384)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_563_cast = mul(x = var_13568_cast, y = var_13570_to_fp16)[name = tensor<string, []>("hidden_states_563_cast")];
            tensor<int32, [2]> var_13577 = const()[name = tensor<string, []>("op_13577"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13579 = const()[name = tensor<string, []>("op_13579"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_275_pad_type_0 = const()[name = tensor<string, []>("q_275_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_275_pad_0 = const()[name = tensor<string, []>("q_275_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404649728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404956992))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_275_cast = conv(dilations = var_13579, groups = var_12518, pad = q_275_pad_0, pad_type = q_275_pad_type_0, strides = var_13577, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_563_cast)[name = tensor<string, []>("q_275_cast")];
            tensor<int32, [2]> var_13583 = const()[name = tensor<string, []>("op_13583"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13585 = const()[name = tensor<string, []>("op_13585"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_275_pad_type_0 = const()[name = tensor<string, []>("k_275_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_275_pad_0 = const()[name = tensor<string, []>("k_275_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1404957184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1405940288))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_275_cast = conv(dilations = var_13585, groups = var_12518, pad = k_275_pad_0, pad_type = k_275_pad_type_0, strides = var_13583, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_275_cast")];
            tensor<int32, [2]> var_13589 = const()[name = tensor<string, []>("op_13589"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13591 = const()[name = tensor<string, []>("op_13591"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_275_pad_type_0 = const()[name = tensor<string, []>("v_275_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_275_pad_0 = const()[name = tensor<string, []>("v_275_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1405940480))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1406923584))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_275_cast = conv(dilations = var_13591, groups = var_12518, pad = v_275_pad_0, pad_type = v_275_pad_type_0, strides = var_13589, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_275_cast")];
            tensor<int32, [4]> var_13595 = const()[name = tensor<string, []>("op_13595"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13596_cast = reshape(shape = var_13595, x = q_275_cast)[name = tensor<string, []>("op_13596_cast")];
            tensor<int32, [4]> var_13597 = const()[name = tensor<string, []>("op_13597"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13598_cast = reshape(shape = var_13597, x = k_275_cast)[name = tensor<string, []>("op_13598_cast")];
            tensor<int32, [4]> var_13599 = const()[name = tensor<string, []>("op_13599"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13600_cast = reshape(shape = var_13599, x = v_275_cast)[name = tensor<string, []>("op_13600_cast")];
            tensor<bool, []> attn_weights_549_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_549_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_549_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_549_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_549_cast = matmul(transpose_x = attn_weights_549_transpose_x_0, transpose_y = attn_weights_549_transpose_y_0, x = var_13596_cast, y = var_13598_cast)[name = tensor<string, []>("attn_weights_549_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_551_cast = mul(x = attn_weights_549_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_551_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_13604_cast = softmax(axis = var_12502, x = attn_weights_551_cast)[name = tensor<string, []>("op_13604_cast")];
            tensor<bool, []> attn_275_transpose_x_0 = const()[name = tensor<string, []>("attn_275_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_275_transpose_y_0 = const()[name = tensor<string, []>("attn_275_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_275_cast = matmul(transpose_x = attn_275_transpose_x_0, transpose_y = attn_275_transpose_y_0, x = var_13600_cast, y = var_13604_cast)[name = tensor<string, []>("attn_275_cast")];
            tensor<int32, [4]> var_13608 = const()[name = tensor<string, []>("op_13608"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_807_cast = reshape(shape = var_13608, x = attn_275_cast)[name = tensor<string, []>("input_807_cast")];
            tensor<int32, [2]> var_13613 = const()[name = tensor<string, []>("op_13613"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13615 = const()[name = tensor<string, []>("op_13615"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13617_pad_type_0 = const()[name = tensor<string, []>("op_13617_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13617_pad_0 = const()[name = tensor<string, []>("op_13617_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1406923776))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1407231040))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1407231232)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13617_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13615, groups = var_12518, pad = var_13617_pad_0, pad_type = var_13617_pad_type_0, strides = var_13613, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_807_cast)[name = tensor<string, []>("op_13617_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_413_cast = add(x = var_13617_cast, y = inputs_411_cast)[name = tensor<string, []>("inputs_413_cast")];
            tensor<int32, [1]> var_13621 = const()[name = tensor<string, []>("op_13621"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_413_cast = reduce_mean(axes = var_13621, keep_dims = var_12513, x = inputs_413_cast)[name = tensor<string, []>("channels_mean_413_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_413_cast = sub(x = inputs_413_cast, y = channels_mean_413_cast)[name = tensor<string, []>("zero_mean_413_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_413_cast = mul(x = zero_mean_413_cast, y = zero_mean_413_cast)[name = tensor<string, []>("zero_mean_sq_413_cast")];
            tensor<int32, [1]> var_13625 = const()[name = tensor<string, []>("op_13625"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13626_cast = reduce_mean(axes = var_13625, keep_dims = var_12513, x = zero_mean_sq_413_cast)[name = tensor<string, []>("op_13626_cast")];
            tensor<fp16, []> var_13627_to_fp16 = const()[name = tensor<string, []>("op_13627_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13628_cast = add(x = var_13626_cast, y = var_13627_to_fp16)[name = tensor<string, []>("op_13628_cast")];
            tensor<fp16, []> denom_413_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_413_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_413_cast = rsqrt(epsilon = denom_413_epsilon_0_to_fp16, x = var_13628_cast)[name = tensor<string, []>("denom_413_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_413_cast = mul(x = zero_mean_413_cast, y = denom_413_cast)[name = tensor<string, []>("out_413_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13632_to_fp16 = const()[name = tensor<string, []>("op_13632_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1407232576)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13633_cast = add(x = out_413_cast, y = var_13632_to_fp16)[name = tensor<string, []>("op_13633_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13635_to_fp16 = const()[name = tensor<string, []>("op_13635_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1407233920)))];
            tensor<fp16, [2, 640, 1, 4096]> input_809_cast = mul(x = var_13633_cast, y = var_13635_to_fp16)[name = tensor<string, []>("input_809_cast")];
            tensor<int32, [2]> var_13643 = const()[name = tensor<string, []>("op_13643"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13645 = const()[name = tensor<string, []>("op_13645"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13647_pad_type_0 = const()[name = tensor<string, []>("op_13647_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13647_pad_0 = const()[name = tensor<string, []>("op_13647_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1407235264))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1410512128))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1410512704)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_13647_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_13645, groups = var_12518, pad = var_13647_pad_0, pad_type = var_13647_pad_type_0, strides = var_13643, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_809_cast)[name = tensor<string, []>("op_13647_cast")];
            tensor<int32, [2]> var_13648_split_sizes_0 = const()[name = tensor<string, []>("op_13648_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_13648_axis_0 = const()[name = tensor<string, []>("op_13648_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_13648_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_13648_cast_1 = split(axis = var_13648_axis_0, split_sizes = var_13648_split_sizes_0, x = var_13647_cast)[name = tensor<string, []>("op_13648_cast")];
            tensor<string, []> var_13650_mode_0 = const()[name = tensor<string, []>("op_13650_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_13650_cast = gelu(mode = var_13650_mode_0, x = var_13648_cast_1)[name = tensor<string, []>("op_13650_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_811_cast = mul(x = var_13648_cast_0, y = var_13650_cast)[name = tensor<string, []>("input_811_cast")];
            tensor<int32, [2]> var_13654 = const()[name = tensor<string, []>("op_13654"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13656 = const()[name = tensor<string, []>("op_13656"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13658_pad_type_0 = const()[name = tensor<string, []>("op_13658_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13658_pad_0 = const()[name = tensor<string, []>("op_13658_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1410523008))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412161472))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412162048)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13658_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13656, groups = var_12518, pad = var_13658_pad_0, pad_type = var_13658_pad_type_0, strides = var_13654, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_811_cast)[name = tensor<string, []>("op_13658_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_415_cast = add(x = var_13658_cast, y = inputs_413_cast)[name = tensor<string, []>("inputs_415_cast")];
            tensor<int32, [1]> var_13668 = const()[name = tensor<string, []>("op_13668"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_415_cast = reduce_mean(axes = var_13668, keep_dims = var_12513, x = inputs_415_cast)[name = tensor<string, []>("channels_mean_415_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_415_cast = sub(x = inputs_415_cast, y = channels_mean_415_cast)[name = tensor<string, []>("zero_mean_415_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_415_cast = mul(x = zero_mean_415_cast, y = zero_mean_415_cast)[name = tensor<string, []>("zero_mean_sq_415_cast")];
            tensor<int32, [1]> var_13672 = const()[name = tensor<string, []>("op_13672"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13673_cast = reduce_mean(axes = var_13672, keep_dims = var_12513, x = zero_mean_sq_415_cast)[name = tensor<string, []>("op_13673_cast")];
            tensor<fp16, []> var_13674_to_fp16 = const()[name = tensor<string, []>("op_13674_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13675_cast = add(x = var_13673_cast, y = var_13674_to_fp16)[name = tensor<string, []>("op_13675_cast")];
            tensor<fp16, []> denom_415_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_415_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_415_cast = rsqrt(epsilon = denom_415_epsilon_0_to_fp16, x = var_13675_cast)[name = tensor<string, []>("denom_415_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_415_cast = mul(x = zero_mean_415_cast, y = denom_415_cast)[name = tensor<string, []>("out_415_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13679_to_fp16 = const()[name = tensor<string, []>("op_13679_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412163392)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13680_cast = add(x = out_415_cast, y = var_13679_to_fp16)[name = tensor<string, []>("op_13680_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13682_to_fp16 = const()[name = tensor<string, []>("op_13682_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412164736)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_567_cast = mul(x = var_13680_cast, y = var_13682_to_fp16)[name = tensor<string, []>("hidden_states_567_cast")];
            tensor<int32, [2]> var_13689 = const()[name = tensor<string, []>("op_13689"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13691 = const()[name = tensor<string, []>("op_13691"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_277_pad_type_0 = const()[name = tensor<string, []>("q_277_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_277_pad_0 = const()[name = tensor<string, []>("q_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412166080))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412575744))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_277_cast = conv(dilations = var_13691, groups = var_12518, pad = q_277_pad_0, pad_type = q_277_pad_type_0, strides = var_13689, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_567_cast)[name = tensor<string, []>("q_277_cast")];
            tensor<int32, [2]> var_13695 = const()[name = tensor<string, []>("op_13695"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13697 = const()[name = tensor<string, []>("op_13697"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_277_pad_type_0 = const()[name = tensor<string, []>("k_277_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_277_pad_0 = const()[name = tensor<string, []>("k_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412576320))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412985984))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> k_277_cast = conv(dilations = var_13697, groups = var_12518, pad = k_277_pad_0, pad_type = k_277_pad_type_0, strides = var_13695, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_567_cast)[name = tensor<string, []>("k_277_cast")];
            tensor<int32, [2]> var_13701 = const()[name = tensor<string, []>("op_13701"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13703 = const()[name = tensor<string, []>("op_13703"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_277_pad_type_0 = const()[name = tensor<string, []>("v_277_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_277_pad_0 = const()[name = tensor<string, []>("v_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412986560))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1413396224))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> v_277_cast = conv(dilations = var_13703, groups = var_12518, pad = v_277_pad_0, pad_type = v_277_pad_type_0, strides = var_13701, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_567_cast)[name = tensor<string, []>("v_277_cast")];
            tensor<int32, [4]> var_13707 = const()[name = tensor<string, []>("op_13707"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13708_cast = reshape(shape = var_13707, x = q_277_cast)[name = tensor<string, []>("op_13708_cast")];
            tensor<int32, [4]> var_13709 = const()[name = tensor<string, []>("op_13709"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13710_cast = reshape(shape = var_13709, x = k_277_cast)[name = tensor<string, []>("op_13710_cast")];
            tensor<int32, [4]> var_13711 = const()[name = tensor<string, []>("op_13711"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13712_cast = reshape(shape = var_13711, x = v_277_cast)[name = tensor<string, []>("op_13712_cast")];
            tensor<bool, []> attn_weights_553_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_553_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_553_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_553_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_553_cast = matmul(transpose_x = attn_weights_553_transpose_x_0, transpose_y = attn_weights_553_transpose_y_0, x = var_13708_cast, y = var_13710_cast)[name = tensor<string, []>("attn_weights_553_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> attn_weights_555_cast = mul(x = attn_weights_553_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_555_cast")];
            tensor<fp16, [2, 10, 4096, 4096]> var_13716_cast = softmax(axis = var_12502, x = attn_weights_555_cast)[name = tensor<string, []>("op_13716_cast")];
            tensor<bool, []> attn_277_transpose_x_0 = const()[name = tensor<string, []>("attn_277_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_277_transpose_y_0 = const()[name = tensor<string, []>("attn_277_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_277_cast = matmul(transpose_x = attn_277_transpose_x_0, transpose_y = attn_277_transpose_y_0, x = var_13712_cast, y = var_13716_cast)[name = tensor<string, []>("attn_277_cast")];
            tensor<int32, [4]> var_13720 = const()[name = tensor<string, []>("op_13720"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_813_cast = reshape(shape = var_13720, x = attn_277_cast)[name = tensor<string, []>("input_813_cast")];
            tensor<int32, [2]> var_13725 = const()[name = tensor<string, []>("op_13725"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13727 = const()[name = tensor<string, []>("op_13727"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13729_pad_type_0 = const()[name = tensor<string, []>("op_13729_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13729_pad_0 = const()[name = tensor<string, []>("op_13729_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1413396800))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1413806464))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1413807040)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13729_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13727, groups = var_12518, pad = var_13729_pad_0, pad_type = var_13729_pad_type_0, strides = var_13725, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_813_cast)[name = tensor<string, []>("op_13729_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_417_cast = add(x = var_13729_cast, y = inputs_415_cast)[name = tensor<string, []>("inputs_417_cast")];
            tensor<int32, [1]> var_13733 = const()[name = tensor<string, []>("op_13733"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_417_cast = reduce_mean(axes = var_13733, keep_dims = var_12513, x = inputs_417_cast)[name = tensor<string, []>("channels_mean_417_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_417_cast = sub(x = inputs_417_cast, y = channels_mean_417_cast)[name = tensor<string, []>("zero_mean_417_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_417_cast = mul(x = zero_mean_417_cast, y = zero_mean_417_cast)[name = tensor<string, []>("zero_mean_sq_417_cast")];
            tensor<int32, [1]> var_13737 = const()[name = tensor<string, []>("op_13737"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13738_cast = reduce_mean(axes = var_13737, keep_dims = var_12513, x = zero_mean_sq_417_cast)[name = tensor<string, []>("op_13738_cast")];
            tensor<fp16, []> var_13739_to_fp16 = const()[name = tensor<string, []>("op_13739_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13740_cast = add(x = var_13738_cast, y = var_13739_to_fp16)[name = tensor<string, []>("op_13740_cast")];
            tensor<fp16, []> denom_417_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_417_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_417_cast = rsqrt(epsilon = denom_417_epsilon_0_to_fp16, x = var_13740_cast)[name = tensor<string, []>("denom_417_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_417_cast = mul(x = zero_mean_417_cast, y = denom_417_cast)[name = tensor<string, []>("out_417_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13744_to_fp16 = const()[name = tensor<string, []>("op_13744_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1413808384)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13745_cast = add(x = out_417_cast, y = var_13744_to_fp16)[name = tensor<string, []>("op_13745_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13747_to_fp16 = const()[name = tensor<string, []>("op_13747_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1413809728)))];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_569_cast = mul(x = var_13745_cast, y = var_13747_to_fp16)[name = tensor<string, []>("hidden_states_569_cast")];
            tensor<int32, [2]> var_13754 = const()[name = tensor<string, []>("op_13754"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13756 = const()[name = tensor<string, []>("op_13756"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_pad_type_0 = const()[name = tensor<string, []>("q_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_pad_0 = const()[name = tensor<string, []>("q_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1413811072))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1414118336))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [2, 640, 1, 4096]> q_cast = conv(dilations = var_13756, groups = var_12518, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_13754, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_569_cast)[name = tensor<string, []>("q_cast")];
            tensor<int32, [2]> var_13760 = const()[name = tensor<string, []>("op_13760"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13762 = const()[name = tensor<string, []>("op_13762"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_pad_type_0 = const()[name = tensor<string, []>("k_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_pad_0 = const()[name = tensor<string, []>("k_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1414118528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1415101632))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_cast = conv(dilations = var_13762, groups = var_12518, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_13760, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_cast")];
            tensor<int32, [2]> var_13766 = const()[name = tensor<string, []>("op_13766"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13768 = const()[name = tensor<string, []>("op_13768"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_pad_type_0 = const()[name = tensor<string, []>("v_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_pad_0 = const()[name = tensor<string, []>("v_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2048, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [983040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1415101824))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416084928))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2048, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_cast = conv(dilations = var_13768, groups = var_12518, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_13766, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_cast")];
            tensor<int32, [4]> var_13772 = const()[name = tensor<string, []>("op_13772"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 4096]> var_13773_cast = reshape(shape = var_13772, x = q_cast)[name = tensor<string, []>("op_13773_cast")];
            tensor<int32, [4]> var_13774 = const()[name = tensor<string, []>("op_13774"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13775_cast = reshape(shape = var_13774, x = k_cast)[name = tensor<string, []>("op_13775_cast")];
            tensor<int32, [4]> var_13776 = const()[name = tensor<string, []>("op_13776"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_13777_cast = reshape(shape = var_13776, x = v_cast)[name = tensor<string, []>("op_13777_cast")];
            tensor<bool, []> attn_weights_557_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_557_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_557_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_557_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_557_cast = matmul(transpose_x = attn_weights_557_transpose_x_0, transpose_y = attn_weights_557_transpose_y_0, x = var_13773_cast, y = var_13775_cast)[name = tensor<string, []>("attn_weights_557_cast")];
            tensor<fp16, [2, 10, 4096, 77]> attn_weights_cast = mul(x = attn_weights_557_cast, y = var_12509_to_fp16)[name = tensor<string, []>("attn_weights_cast")];
            tensor<fp16, [2, 10, 4096, 77]> var_13781_cast = softmax(axis = var_12502, x = attn_weights_cast)[name = tensor<string, []>("op_13781_cast")];
            tensor<bool, []> attn_transpose_x_0 = const()[name = tensor<string, []>("attn_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_transpose_y_0 = const()[name = tensor<string, []>("attn_transpose_y_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 10, 64, 4096]> attn_cast = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_13777_cast, y = var_13781_cast)[name = tensor<string, []>("attn_cast")];
            tensor<int32, [4]> var_13785 = const()[name = tensor<string, []>("op_13785"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 4096]> input_815_cast = reshape(shape = var_13785, x = attn_cast)[name = tensor<string, []>("input_815_cast")];
            tensor<int32, [2]> var_13790 = const()[name = tensor<string, []>("op_13790"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13792 = const()[name = tensor<string, []>("op_13792"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13794_pad_type_0 = const()[name = tensor<string, []>("op_13794_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13794_pad_0 = const()[name = tensor<string, []>("op_13794_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416085120))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416392384))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416392576)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13794_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13792, groups = var_12518, pad = var_13794_pad_0, pad_type = var_13794_pad_type_0, strides = var_13790, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_815_cast)[name = tensor<string, []>("op_13794_cast")];
            tensor<fp16, [2, 640, 1, 4096]> inputs_cast = add(x = var_13794_cast, y = inputs_417_cast)[name = tensor<string, []>("inputs_cast")];
            tensor<int32, [1]> var_13798 = const()[name = tensor<string, []>("op_13798"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_cast = reduce_mean(axes = var_13798, keep_dims = var_12513, x = inputs_cast)[name = tensor<string, []>("channels_mean_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_cast = sub(x = inputs_cast, y = channels_mean_cast)[name = tensor<string, []>("zero_mean_cast")];
            tensor<fp16, [2, 640, 1, 4096]> zero_mean_sq_cast = mul(x = zero_mean_cast, y = zero_mean_cast)[name = tensor<string, []>("zero_mean_sq_cast")];
            tensor<int32, [1]> var_13802 = const()[name = tensor<string, []>("op_13802"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_13803_cast = reduce_mean(axes = var_13802, keep_dims = var_12513, x = zero_mean_sq_cast)[name = tensor<string, []>("op_13803_cast")];
            tensor<fp16, []> var_13804_to_fp16 = const()[name = tensor<string, []>("op_13804_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_13805_cast = add(x = var_13803_cast, y = var_13804_to_fp16)[name = tensor<string, []>("op_13805_cast")];
            tensor<fp16, []> denom_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 4096]> denom_cast = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_13805_cast)[name = tensor<string, []>("denom_cast")];
            tensor<fp16, [2, 640, 1, 4096]> out_cast = mul(x = zero_mean_cast, y = denom_cast)[name = tensor<string, []>("out_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13809_to_fp16 = const()[name = tensor<string, []>("op_13809_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416393920)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13810_cast = add(x = out_cast, y = var_13809_to_fp16)[name = tensor<string, []>("op_13810_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_13812_to_fp16 = const()[name = tensor<string, []>("op_13812_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416395264)))];
            tensor<fp16, [2, 640, 1, 4096]> input_817_cast = mul(x = var_13810_cast, y = var_13812_to_fp16)[name = tensor<string, []>("input_817_cast")];
            tensor<int32, [2]> var_13820 = const()[name = tensor<string, []>("op_13820"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13822 = const()[name = tensor<string, []>("op_13822"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13824_pad_type_0 = const()[name = tensor<string, []>("op_13824_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13824_pad_0 = const()[name = tensor<string, []>("op_13824_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3276800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416396608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1419673472))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([5120, 640, 1, 1])];
            tensor<fp16, [5120]> up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1419674048)))];
            tensor<fp16, [2, 5120, 1, 4096]> var_13824_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_13822, groups = var_12518, pad = var_13824_pad_0, pad_type = var_13824_pad_type_0, strides = var_13820, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_817_cast)[name = tensor<string, []>("op_13824_cast")];
            tensor<int32, [2]> var_13825_split_sizes_0 = const()[name = tensor<string, []>("op_13825_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_13825_axis_0 = const()[name = tensor<string, []>("op_13825_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 4096]> var_13825_cast_0, tensor<fp16, [2, 2560, 1, 4096]> var_13825_cast_1 = split(axis = var_13825_axis_0, split_sizes = var_13825_split_sizes_0, x = var_13824_cast)[name = tensor<string, []>("op_13825_cast")];
            tensor<string, []> var_13827_mode_0 = const()[name = tensor<string, []>("op_13827_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 4096]> var_13827_cast = gelu(mode = var_13827_mode_0, x = var_13825_cast_1)[name = tensor<string, []>("op_13827_cast")];
            tensor<fp16, [2, 2560, 1, 4096]> input_819_cast = mul(x = var_13825_cast_0, y = var_13827_cast)[name = tensor<string, []>("input_819_cast")];
            tensor<int32, [2]> var_13831 = const()[name = tensor<string, []>("op_13831"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13833 = const()[name = tensor<string, []>("op_13833"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_13835_pad_type_0 = const()[name = tensor<string, []>("op_13835_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_13835_pad_0 = const()[name = tensor<string, []>("op_13835_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1638400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1419684352))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1421322816))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 2560, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1421323392)))];
            tensor<fp16, [2, 640, 1, 4096]> var_13835_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13833, groups = var_12518, pad = var_13835_pad_0, pad_type = var_13835_pad_type_0, strides = var_13831, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_819_cast)[name = tensor<string, []>("op_13835_cast")];
            tensor<fp16, [2, 640, 1, 4096]> hidden_states_573_cast = add(x = var_13835_cast, y = inputs_cast)[name = tensor<string, []>("hidden_states_573_cast")];
            tensor<int32, [4]> var_13837 = const()[name = tensor<string, []>("op_13837"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> input_821_cast = reshape(shape = var_13837, x = hidden_states_573_cast)[name = tensor<string, []>("input_821_cast")];
            tensor<int32, [2]> var_13841 = const()[name = tensor<string, []>("op_13841"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13843 = const()[name = tensor<string, []>("op_13843"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_575_pad_type_0 = const()[name = tensor<string, []>("hidden_states_575_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_575_pad_0 = const()[name = tensor<string, []>("hidden_states_575_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1421324736))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1421734400))), name = tensor<string, []>("up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1421734976)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_575_cast = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_13843, groups = var_12518, pad = hidden_states_575_pad_0, pad_type = hidden_states_575_pad_type_0, strides = var_13841, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized, x = input_821_cast)[name = tensor<string, []>("hidden_states_575_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_823_cast = add(x = hidden_states_575_cast, y = hidden_states_557_cast)[name = tensor<string, []>("input_823_cast")];
            tensor<fp32, []> input_825_scale_factor_height_0 = const()[name = tensor<string, []>("input_825_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, []> input_825_scale_factor_width_0 = const()[name = tensor<string, []>("input_825_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp16, [2, 640, 128, 128]> input_825_cast = upsample_nearest_neighbor(scale_factor_height = input_825_scale_factor_height_0, scale_factor_width = input_825_scale_factor_width_0, x = input_823_cast)[name = tensor<string, []>("input_825_cast")];
            tensor<int32, [2]> var_13852 = const()[name = tensor<string, []>("op_13852"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13854 = const()[name = tensor<string, []>("op_13854"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_577_pad_type_0 = const()[name = tensor<string, []>("hidden_states_577_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_577_pad_0 = const()[name = tensor<string, []>("hidden_states_577_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [640, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1421736320)))];
            tensor<fp16, [640]> up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1429109184)))];
            tensor<fp16, [2, 640, 128, 128]> hidden_states_577_cast = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_13854, groups = var_12518, pad = hidden_states_577_pad_0, pad_type = hidden_states_577_pad_type_0, strides = var_13852, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_825_cast)[name = tensor<string, []>("hidden_states_577_cast")];
            tensor<int32, []> var_13862 = const()[name = tensor<string, []>("op_13862"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_827_interleave_0 = const()[name = tensor<string, []>("input_827_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 960, 128, 128]> input_827_cast = concat(axis = var_13862, interleave = input_827_interleave_0, values = (hidden_states_577_cast, input_43_cast))[name = tensor<string, []>("input_827_cast")];
            tensor<int32, [5]> reshape_156_shape_0 = const()[name = tensor<string, []>("reshape_156_shape_0"), val = tensor<int32, [5]>([2, 32, 30, 128, 128])];
            tensor<fp16, [2, 32, 30, 128, 128]> reshape_156_cast = reshape(shape = reshape_156_shape_0, x = input_827_cast)[name = tensor<string, []>("reshape_156_cast")];
            tensor<int32, [3]> reduce_mean_117_axes_0 = const()[name = tensor<string, []>("reduce_mean_117_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_117_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_117_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_117_cast = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast)[name = tensor<string, []>("reduce_mean_117_cast")];
            tensor<fp16, [2, 32, 30, 128, 128]> sub_78_cast = sub(x = reshape_156_cast, y = reduce_mean_117_cast)[name = tensor<string, []>("sub_78_cast")];
            tensor<fp16, [2, 32, 30, 128, 128]> square_39_cast = square(x = sub_78_cast)[name = tensor<string, []>("square_39_cast")];
            tensor<int32, [3]> reduce_mean_119_axes_0 = const()[name = tensor<string, []>("reduce_mean_119_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_119_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_119_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_119_cast = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast)[name = tensor<string, []>("reduce_mean_119_cast")];
            tensor<fp16, []> add_78_y_0_to_fp16 = const()[name = tensor<string, []>("add_78_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_78_cast = add(x = reduce_mean_119_cast, y = add_78_y_0_to_fp16)[name = tensor<string, []>("add_78_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_39_cast = sqrt(x = add_78_cast)[name = tensor<string, []>("sqrt_39_cast")];
            tensor<fp16, [2, 32, 30, 128, 128]> real_div_39_cast = real_div(x = sub_78_cast, y = sqrt_39_cast)[name = tensor<string, []>("real_div_39_cast")];
            tensor<int32, [4]> reshape_157_shape_0 = const()[name = tensor<string, []>("reshape_157_shape_0"), val = tensor<int32, [4]>([2, 960, 128, 128])];
            tensor<fp16, [2, 960, 128, 128]> reshape_157_cast = reshape(shape = reshape_157_shape_0, x = real_div_39_cast)[name = tensor<string, []>("reshape_157_cast")];
            tensor<fp16, [960]> add_79_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_79_gamma_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1429110528)))];
            tensor<fp16, [960]> add_79_beta_0_to_fp16 = const()[name = tensor<string, []>("add_79_beta_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1429112512)))];
            tensor<fp16, []> add_79_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_79_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 960, 128, 128]> add_79_cast = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_157_cast)[name = tensor<string, []>("add_79_cast")];
            tensor<fp16, [2, 960, 128, 128]> input_831_cast = silu(x = add_79_cast)[name = tensor<string, []>("input_831_cast")];
            tensor<int32, [2]> var_13883 = const()[name = tensor<string, []>("op_13883"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13885 = const()[name = tensor<string, []>("op_13885"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_579_pad_type_0 = const()[name = tensor<string, []>("hidden_states_579_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_579_pad_0 = const()[name = tensor<string, []>("hidden_states_579_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 960, 3, 3]> up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [320, 960, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1429114496)))];
            tensor<fp16, [320]> up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1434644160)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_579_cast = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_13885, groups = var_13862, pad = hidden_states_579_pad_0, pad_type = hidden_states_579_pad_type_0, strides = var_13883, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_831_cast)[name = tensor<string, []>("hidden_states_579_cast")];
            tensor<int32, [2]> var_13891 = const()[name = tensor<string, []>("op_13891"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13893 = const()[name = tensor<string, []>("op_13893"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_29_pad_type_0 = const()[name = tensor<string, []>("temb_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_29_pad_0 = const()[name = tensor<string, []>("temb_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1434644864))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1435054528))), name = tensor<string, []>("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1435055104)))];
            tensor<fp16, [2, 320, 1, 1]> temb_29_cast = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_13893, groups = var_13862, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_13891, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_29_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_835_cast = add(x = hidden_states_579_cast, y = temb_29_cast)[name = tensor<string, []>("input_835_cast")];
            tensor<int32, [5]> reshape_160_shape_0 = const()[name = tensor<string, []>("reshape_160_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_160_cast = reshape(shape = reshape_160_shape_0, x = input_835_cast)[name = tensor<string, []>("reshape_160_cast")];
            tensor<int32, [3]> reduce_mean_120_axes_0 = const()[name = tensor<string, []>("reduce_mean_120_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_120_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_120_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_120_cast = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast)[name = tensor<string, []>("reduce_mean_120_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_80_cast = sub(x = reshape_160_cast, y = reduce_mean_120_cast)[name = tensor<string, []>("sub_80_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_40_cast = square(x = sub_80_cast)[name = tensor<string, []>("square_40_cast")];
            tensor<int32, [3]> reduce_mean_122_axes_0 = const()[name = tensor<string, []>("reduce_mean_122_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_122_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_122_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_122_cast = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast)[name = tensor<string, []>("reduce_mean_122_cast")];
            tensor<fp16, []> add_80_y_0_to_fp16 = const()[name = tensor<string, []>("add_80_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_80_cast = add(x = reduce_mean_122_cast, y = add_80_y_0_to_fp16)[name = tensor<string, []>("add_80_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_40_cast = sqrt(x = add_80_cast)[name = tensor<string, []>("sqrt_40_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_40_cast = real_div(x = sub_80_cast, y = sqrt_40_cast)[name = tensor<string, []>("real_div_40_cast")];
            tensor<int32, [4]> reshape_161_shape_0 = const()[name = tensor<string, []>("reshape_161_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_161_cast = reshape(shape = reshape_161_shape_0, x = real_div_40_cast)[name = tensor<string, []>("reshape_161_cast")];
            tensor<fp16, [320]> add_81_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_81_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1435055808)))];
            tensor<fp16, [320]> add_81_beta_0_to_fp16 = const()[name = tensor<string, []>("add_81_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1435056512)))];
            tensor<fp16, []> add_81_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_81_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_81_cast = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_161_cast)[name = tensor<string, []>("add_81_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_839_cast = silu(x = add_81_cast)[name = tensor<string, []>("input_839_cast")];
            tensor<int32, [2]> var_13903 = const()[name = tensor<string, []>("op_13903"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13905 = const()[name = tensor<string, []>("op_13905"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_581_pad_type_0 = const()[name = tensor<string, []>("hidden_states_581_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_581_pad_0 = const()[name = tensor<string, []>("hidden_states_581_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1435057216)))];
            tensor<fp16, [320]> up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1436900480)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_581_cast = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_13905, groups = var_13862, pad = hidden_states_581_pad_0, pad_type = hidden_states_581_pad_type_0, strides = var_13903, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_839_cast)[name = tensor<string, []>("hidden_states_581_cast")];
            tensor<int32, [2]> var_13910 = const()[name = tensor<string, []>("op_13910"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13912 = const()[name = tensor<string, []>("op_13912"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_17_pad_type_0 = const()[name = tensor<string, []>("x_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_17_pad_0 = const()[name = tensor<string, []>("x_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 960, 1, 1]> up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [320, 960, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1436901184)))];
            tensor<fp16, [320]> up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1437515648)))];
            tensor<fp16, [2, 320, 128, 128]> x_17_cast = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_13912, groups = var_13862, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_13910, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_827_cast)[name = tensor<string, []>("x_17_cast")];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_583_cast = add(x = x_17_cast, y = hidden_states_581_cast)[name = tensor<string, []>("hidden_states_583_cast")];
            tensor<bool, []> input_841_interleave_0 = const()[name = tensor<string, []>("input_841_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 640, 128, 128]> input_841_cast = concat(axis = var_13862, interleave = input_841_interleave_0, values = (hidden_states_583_cast, input_29_cast))[name = tensor<string, []>("input_841_cast")];
            tensor<int32, [5]> reshape_164_shape_0 = const()[name = tensor<string, []>("reshape_164_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 128, 128])];
            tensor<fp16, [2, 32, 20, 128, 128]> reshape_164_cast = reshape(shape = reshape_164_shape_0, x = input_841_cast)[name = tensor<string, []>("reshape_164_cast")];
            tensor<int32, [3]> reduce_mean_123_axes_0 = const()[name = tensor<string, []>("reduce_mean_123_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_123_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_123_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_123_cast = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast)[name = tensor<string, []>("reduce_mean_123_cast")];
            tensor<fp16, [2, 32, 20, 128, 128]> sub_82_cast = sub(x = reshape_164_cast, y = reduce_mean_123_cast)[name = tensor<string, []>("sub_82_cast")];
            tensor<fp16, [2, 32, 20, 128, 128]> square_41_cast = square(x = sub_82_cast)[name = tensor<string, []>("square_41_cast")];
            tensor<int32, [3]> reduce_mean_125_axes_0 = const()[name = tensor<string, []>("reduce_mean_125_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_125_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_125_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_125_cast = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast)[name = tensor<string, []>("reduce_mean_125_cast")];
            tensor<fp16, []> add_82_y_0_to_fp16 = const()[name = tensor<string, []>("add_82_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_82_cast = add(x = reduce_mean_125_cast, y = add_82_y_0_to_fp16)[name = tensor<string, []>("add_82_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_41_cast = sqrt(x = add_82_cast)[name = tensor<string, []>("sqrt_41_cast")];
            tensor<fp16, [2, 32, 20, 128, 128]> real_div_41_cast = real_div(x = sub_82_cast, y = sqrt_41_cast)[name = tensor<string, []>("real_div_41_cast")];
            tensor<int32, [4]> reshape_165_shape_0 = const()[name = tensor<string, []>("reshape_165_shape_0"), val = tensor<int32, [4]>([2, 640, 128, 128])];
            tensor<fp16, [2, 640, 128, 128]> reshape_165_cast = reshape(shape = reshape_165_shape_0, x = real_div_41_cast)[name = tensor<string, []>("reshape_165_cast")];
            tensor<fp16, [640]> add_83_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_83_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1437516352)))];
            tensor<fp16, [640]> add_83_beta_0_to_fp16 = const()[name = tensor<string, []>("add_83_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1437517696)))];
            tensor<fp16, []> add_83_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_83_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 128, 128]> add_83_cast = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_165_cast)[name = tensor<string, []>("add_83_cast")];
            tensor<fp16, [2, 640, 128, 128]> input_845_cast = silu(x = add_83_cast)[name = tensor<string, []>("input_845_cast")];
            tensor<int32, [2]> var_13930 = const()[name = tensor<string, []>("op_13930"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13932 = const()[name = tensor<string, []>("op_13932"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_585_pad_type_0 = const()[name = tensor<string, []>("hidden_states_585_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_585_pad_0 = const()[name = tensor<string, []>("hidden_states_585_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 640, 3, 3]> up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [320, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1437519040)))];
            tensor<fp16, [320]> up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1441205504)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_585_cast = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_13932, groups = var_13862, pad = hidden_states_585_pad_0, pad_type = hidden_states_585_pad_type_0, strides = var_13930, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_845_cast)[name = tensor<string, []>("hidden_states_585_cast")];
            tensor<int32, [2]> var_13938 = const()[name = tensor<string, []>("op_13938"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13940 = const()[name = tensor<string, []>("op_13940"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_31_pad_type_0 = const()[name = tensor<string, []>("temb_31_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_31_pad_0 = const()[name = tensor<string, []>("temb_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1441206208))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1441615872))), name = tensor<string, []>("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1441616448)))];
            tensor<fp16, [2, 320, 1, 1]> temb_31_cast = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_13940, groups = var_13862, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_13938, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_31_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_849_cast = add(x = hidden_states_585_cast, y = temb_31_cast)[name = tensor<string, []>("input_849_cast")];
            tensor<int32, [5]> reshape_168_shape_0 = const()[name = tensor<string, []>("reshape_168_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_168_cast = reshape(shape = reshape_168_shape_0, x = input_849_cast)[name = tensor<string, []>("reshape_168_cast")];
            tensor<int32, [3]> reduce_mean_126_axes_0 = const()[name = tensor<string, []>("reduce_mean_126_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_126_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_126_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_126_cast = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast)[name = tensor<string, []>("reduce_mean_126_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_84_cast = sub(x = reshape_168_cast, y = reduce_mean_126_cast)[name = tensor<string, []>("sub_84_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_42_cast = square(x = sub_84_cast)[name = tensor<string, []>("square_42_cast")];
            tensor<int32, [3]> reduce_mean_128_axes_0 = const()[name = tensor<string, []>("reduce_mean_128_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_128_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_128_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_128_cast = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast)[name = tensor<string, []>("reduce_mean_128_cast")];
            tensor<fp16, []> add_84_y_0_to_fp16 = const()[name = tensor<string, []>("add_84_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_84_cast = add(x = reduce_mean_128_cast, y = add_84_y_0_to_fp16)[name = tensor<string, []>("add_84_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_42_cast = sqrt(x = add_84_cast)[name = tensor<string, []>("sqrt_42_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_42_cast = real_div(x = sub_84_cast, y = sqrt_42_cast)[name = tensor<string, []>("real_div_42_cast")];
            tensor<int32, [4]> reshape_169_shape_0 = const()[name = tensor<string, []>("reshape_169_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_169_cast = reshape(shape = reshape_169_shape_0, x = real_div_42_cast)[name = tensor<string, []>("reshape_169_cast")];
            tensor<fp16, [320]> add_85_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_85_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1441617152)))];
            tensor<fp16, [320]> add_85_beta_0_to_fp16 = const()[name = tensor<string, []>("add_85_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1441617856)))];
            tensor<fp16, []> add_85_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_85_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_85_cast = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_169_cast)[name = tensor<string, []>("add_85_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_853_cast = silu(x = add_85_cast)[name = tensor<string, []>("input_853_cast")];
            tensor<int32, [2]> var_13950 = const()[name = tensor<string, []>("op_13950"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13952 = const()[name = tensor<string, []>("op_13952"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_587_pad_type_0 = const()[name = tensor<string, []>("hidden_states_587_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_587_pad_0 = const()[name = tensor<string, []>("hidden_states_587_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1441618560)))];
            tensor<fp16, [320]> up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1443461824)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_587_cast = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_13952, groups = var_13862, pad = hidden_states_587_pad_0, pad_type = hidden_states_587_pad_type_0, strides = var_13950, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_853_cast)[name = tensor<string, []>("hidden_states_587_cast")];
            tensor<int32, [2]> var_13957 = const()[name = tensor<string, []>("op_13957"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13959 = const()[name = tensor<string, []>("op_13959"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_19_pad_type_0 = const()[name = tensor<string, []>("x_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_19_pad_0 = const()[name = tensor<string, []>("x_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 640, 1, 1]> up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [320, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1443462528)))];
            tensor<fp16, [320]> up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1443872192)))];
            tensor<fp16, [2, 320, 128, 128]> x_19_cast = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13959, groups = var_13862, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_13957, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16, x = input_841_cast)[name = tensor<string, []>("x_19_cast")];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_589_cast = add(x = x_19_cast, y = hidden_states_587_cast)[name = tensor<string, []>("hidden_states_589_cast")];
            tensor<bool, []> input_855_interleave_0 = const()[name = tensor<string, []>("input_855_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 640, 128, 128]> input_855_cast = concat(axis = var_13862, interleave = input_855_interleave_0, values = (hidden_states_589_cast, input_13_cast))[name = tensor<string, []>("input_855_cast")];
            tensor<int32, [5]> reshape_172_shape_0 = const()[name = tensor<string, []>("reshape_172_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 128, 128])];
            tensor<fp16, [2, 32, 20, 128, 128]> reshape_172_cast = reshape(shape = reshape_172_shape_0, x = input_855_cast)[name = tensor<string, []>("reshape_172_cast")];
            tensor<int32, [3]> reduce_mean_129_axes_0 = const()[name = tensor<string, []>("reduce_mean_129_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_129_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_129_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_129_cast = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast)[name = tensor<string, []>("reduce_mean_129_cast")];
            tensor<fp16, [2, 32, 20, 128, 128]> sub_86_cast = sub(x = reshape_172_cast, y = reduce_mean_129_cast)[name = tensor<string, []>("sub_86_cast")];
            tensor<fp16, [2, 32, 20, 128, 128]> square_43_cast = square(x = sub_86_cast)[name = tensor<string, []>("square_43_cast")];
            tensor<int32, [3]> reduce_mean_131_axes_0 = const()[name = tensor<string, []>("reduce_mean_131_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_131_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_131_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_131_cast = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast)[name = tensor<string, []>("reduce_mean_131_cast")];
            tensor<fp16, []> add_86_y_0_to_fp16 = const()[name = tensor<string, []>("add_86_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_86_cast = add(x = reduce_mean_131_cast, y = add_86_y_0_to_fp16)[name = tensor<string, []>("add_86_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_43_cast = sqrt(x = add_86_cast)[name = tensor<string, []>("sqrt_43_cast")];
            tensor<fp16, [2, 32, 20, 128, 128]> real_div_43_cast = real_div(x = sub_86_cast, y = sqrt_43_cast)[name = tensor<string, []>("real_div_43_cast")];
            tensor<int32, [4]> reshape_173_shape_0 = const()[name = tensor<string, []>("reshape_173_shape_0"), val = tensor<int32, [4]>([2, 640, 128, 128])];
            tensor<fp16, [2, 640, 128, 128]> reshape_173_cast = reshape(shape = reshape_173_shape_0, x = real_div_43_cast)[name = tensor<string, []>("reshape_173_cast")];
            tensor<fp16, [640]> add_87_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_87_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1443872896)))];
            tensor<fp16, [640]> add_87_beta_0_to_fp16 = const()[name = tensor<string, []>("add_87_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1443874240)))];
            tensor<fp16, []> add_87_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_87_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 128, 128]> add_87_cast = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_173_cast)[name = tensor<string, []>("add_87_cast")];
            tensor<fp16, [2, 640, 128, 128]> input_859_cast = silu(x = add_87_cast)[name = tensor<string, []>("input_859_cast")];
            tensor<int32, [2]> var_13977 = const()[name = tensor<string, []>("op_13977"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13979 = const()[name = tensor<string, []>("op_13979"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_591_pad_type_0 = const()[name = tensor<string, []>("hidden_states_591_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_591_pad_0 = const()[name = tensor<string, []>("hidden_states_591_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 640, 3, 3]> up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [320, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1443875584)))];
            tensor<fp16, [320]> up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1447562048)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_591_cast = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_13979, groups = var_13862, pad = hidden_states_591_pad_0, pad_type = hidden_states_591_pad_type_0, strides = var_13977, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_859_cast)[name = tensor<string, []>("hidden_states_591_cast")];
            tensor<int32, [2]> var_13985 = const()[name = tensor<string, []>("op_13985"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13987 = const()[name = tensor<string, []>("op_13987"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_pad_type_0 = const()[name = tensor<string, []>("temb_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_pad_0 = const()[name = tensor<string, []>("temb_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [409600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1447562752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1447972416))), name = tensor<string, []>("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1447972992)))];
            tensor<fp16, [2, 320, 1, 1]> temb_cast = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13987, groups = var_13862, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_13985, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("temb_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_863_cast = add(x = hidden_states_591_cast, y = temb_cast)[name = tensor<string, []>("input_863_cast")];
            tensor<int32, [5]> reshape_176_shape_0 = const()[name = tensor<string, []>("reshape_176_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_176_cast = reshape(shape = reshape_176_shape_0, x = input_863_cast)[name = tensor<string, []>("reshape_176_cast")];
            tensor<int32, [3]> reduce_mean_132_axes_0 = const()[name = tensor<string, []>("reduce_mean_132_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_132_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_132_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_132_cast = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast)[name = tensor<string, []>("reduce_mean_132_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_88_cast = sub(x = reshape_176_cast, y = reduce_mean_132_cast)[name = tensor<string, []>("sub_88_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_44_cast = square(x = sub_88_cast)[name = tensor<string, []>("square_44_cast")];
            tensor<int32, [3]> reduce_mean_134_axes_0 = const()[name = tensor<string, []>("reduce_mean_134_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_134_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_134_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_134_cast = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast)[name = tensor<string, []>("reduce_mean_134_cast")];
            tensor<fp16, []> add_88_y_0_to_fp16 = const()[name = tensor<string, []>("add_88_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_88_cast = add(x = reduce_mean_134_cast, y = add_88_y_0_to_fp16)[name = tensor<string, []>("add_88_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_44_cast = sqrt(x = add_88_cast)[name = tensor<string, []>("sqrt_44_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_44_cast = real_div(x = sub_88_cast, y = sqrt_44_cast)[name = tensor<string, []>("real_div_44_cast")];
            tensor<int32, [4]> reshape_177_shape_0 = const()[name = tensor<string, []>("reshape_177_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_177_cast = reshape(shape = reshape_177_shape_0, x = real_div_44_cast)[name = tensor<string, []>("reshape_177_cast")];
            tensor<fp16, [320]> add_89_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_89_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1447973696)))];
            tensor<fp16, [320]> add_89_beta_0_to_fp16 = const()[name = tensor<string, []>("add_89_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1447974400)))];
            tensor<fp16, []> add_89_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_89_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_89_cast = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_177_cast)[name = tensor<string, []>("add_89_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_867_cast = silu(x = add_89_cast)[name = tensor<string, []>("input_867_cast")];
            tensor<int32, [2]> var_13997 = const()[name = tensor<string, []>("op_13997"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_13999 = const()[name = tensor<string, []>("op_13999"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_pad_type_0 = const()[name = tensor<string, []>("hidden_states_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_pad_0 = const()[name = tensor<string, []>("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1447975104)))];
            tensor<fp16, [320]> up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1449818368)))];
            tensor<fp16, [2, 320, 128, 128]> hidden_states_cast = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_13999, groups = var_13862, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_13997, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_867_cast)[name = tensor<string, []>("hidden_states_cast")];
            tensor<int32, [2]> var_14004 = const()[name = tensor<string, []>("op_14004"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_14006 = const()[name = tensor<string, []>("op_14006"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_pad_type_0 = const()[name = tensor<string, []>("x_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 640, 1, 1]> up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [204800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1449819072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1450023936))), name = tensor<string, []>("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 640, 1, 1])];
            tensor<fp16, [320]> up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1450024512)))];
            tensor<fp16, [2, 320, 128, 128]> x_cast = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_14006, groups = var_13862, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_14004, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_855_cast)[name = tensor<string, []>("x_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_869_cast = add(x = x_cast, y = hidden_states_cast)[name = tensor<string, []>("input_869_cast")];
            tensor<int32, [5]> reshape_180_shape_0 = const()[name = tensor<string, []>("reshape_180_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 128, 128])];
            tensor<fp16, [2, 32, 10, 128, 128]> reshape_180_cast = reshape(shape = reshape_180_shape_0, x = input_869_cast)[name = tensor<string, []>("reshape_180_cast")];
            tensor<int32, [3]> reduce_mean_135_axes_0 = const()[name = tensor<string, []>("reduce_mean_135_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_135_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_135_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_135_cast = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast)[name = tensor<string, []>("reduce_mean_135_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> sub_90_cast = sub(x = reshape_180_cast, y = reduce_mean_135_cast)[name = tensor<string, []>("sub_90_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> square_45_cast = square(x = sub_90_cast)[name = tensor<string, []>("square_45_cast")];
            tensor<int32, [3]> reduce_mean_137_axes_0 = const()[name = tensor<string, []>("reduce_mean_137_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_137_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_137_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_137_cast = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast)[name = tensor<string, []>("reduce_mean_137_cast")];
            tensor<fp16, []> add_90_y_0_to_fp16 = const()[name = tensor<string, []>("add_90_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_90_cast = add(x = reduce_mean_137_cast, y = add_90_y_0_to_fp16)[name = tensor<string, []>("add_90_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_45_cast = sqrt(x = add_90_cast)[name = tensor<string, []>("sqrt_45_cast")];
            tensor<fp16, [2, 32, 10, 128, 128]> real_div_45_cast = real_div(x = sub_90_cast, y = sqrt_45_cast)[name = tensor<string, []>("real_div_45_cast")];
            tensor<int32, [4]> reshape_181_shape_0 = const()[name = tensor<string, []>("reshape_181_shape_0"), val = tensor<int32, [4]>([2, 320, 128, 128])];
            tensor<fp16, [2, 320, 128, 128]> reshape_181_cast = reshape(shape = reshape_181_shape_0, x = real_div_45_cast)[name = tensor<string, []>("reshape_181_cast")];
            tensor<fp16, [320]> add_91_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_91_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1450025216)))];
            tensor<fp16, [320]> add_91_beta_0_to_fp16 = const()[name = tensor<string, []>("add_91_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1450025920)))];
            tensor<fp16, []> add_91_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_91_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 128, 128]> add_91_cast = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_181_cast)[name = tensor<string, []>("add_91_cast")];
            tensor<fp16, [2, 320, 128, 128]> input_cast = silu(x = add_91_cast)[name = tensor<string, []>("input_cast")];
            tensor<int32, []> var_14020 = const()[name = tensor<string, []>("op_14020"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_14023 = const()[name = tensor<string, []>("op_14023"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_14025 = const()[name = tensor<string, []>("op_14025"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_14027_pad_type_0 = const()[name = tensor<string, []>("op_14027_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_14027_pad_0 = const()[name = tensor<string, []>("op_14027_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [4, 320, 3, 3]> conv_out_weight_to_fp16 = const()[name = tensor<string, []>("conv_out_weight_to_fp16"), val = tensor<fp16, [4, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1450026624)))];
            tensor<fp16, [4]> conv_out_bias_to_fp16 = const()[name = tensor<string, []>("conv_out_bias_to_fp16"), val = tensor<fp16, [4]>([0x1.664p-9, -0x1.72p-10, 0x1.06p-9, -0x1.9b8p-9])];
            tensor<fp16, [2, 4, 128, 128]> var_14027_cast = conv(bias = conv_out_bias_to_fp16, dilations = var_14025, groups = var_14020, pad = var_14027_pad_0, pad_type = var_14027_pad_type_0, strides = var_14023, weight = conv_out_weight_to_fp16, x = input_cast)[name = tensor<string, []>("op_14027_cast")];
            tensor<string, []> var_14027_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_14027_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [2, 4, 128, 128]> noise_pred = cast(dtype = var_14027_cast_to_fp32_dtype_0, x = var_14027_cast)[name = tensor<string, []>("cast_0")];
        } -> (noise_pred);
}