program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "5.30.0"}, {"coremlc-version", "1839.0.0"}})]
{
    func main<ios16>(tensor<fp16, [2, 1024, 1, 77]> encoder_hidden_states, tensor<fp16, [2, 4, 64, 64]> sample, tensor<fp16, [2]> timestep) {
            tensor<int32, []> var_25 = const()[name = tensor<string, []>("op_25"), val = tensor<int32, []>(-1)];
            tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1]> var_42_cast = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor<string, []>("op_42_cast")];
            tensor<fp16, [1, 160]> var_44_to_fp16 = const()[name = tensor<string, []>("op_44_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_42_cast, y = var_44_to_fp16)[name = tensor<string, []>("emb_3_cast")];
            tensor<fp16, [2, 160]> var_49_cast = sin(x = emb_3_cast)[name = tensor<string, []>("op_49_cast")];
            tensor<fp16, [2, 160]> var_50_cast = cos(x = emb_3_cast)[name = tensor<string, []>("op_50_cast")];
            tensor<bool, []> emb_interleave_0 = const()[name = tensor<string, []>("emb_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> emb_cast = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast, var_50_cast))[name = tensor<string, []>("emb_cast")];
            tensor<int32, [2]> var_54_begin_0 = const()[name = tensor<string, []>("op_54_begin_0"), val = tensor<int32, [2]>([0, 160])];
            tensor<int32, [2]> var_54_end_0 = const()[name = tensor<string, []>("op_54_end_0"), val = tensor<int32, [2]>([2, 320])];
            tensor<bool, [2]> var_54_end_mask_0 = const()[name = tensor<string, []>("op_54_end_mask_0"), val = tensor<bool, [2]>([true, true])];
            tensor<fp16, [2, 160]> var_54_cast = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast)[name = tensor<string, []>("op_54_cast")];
            tensor<int32, [2]> var_56_begin_0 = const()[name = tensor<string, []>("op_56_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> var_56_end_0 = const()[name = tensor<string, []>("op_56_end_0"), val = tensor<int32, [2]>([2, 160])];
            tensor<bool, [2]> var_56_end_mask_0 = const()[name = tensor<string, []>("op_56_end_mask_0"), val = tensor<bool, [2]>([true, false])];
            tensor<fp16, [2, 160]> var_56_cast = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast)[name = tensor<string, []>("op_56_cast")];
            tensor<bool, []> sample_interleave_0 = const()[name = tensor<string, []>("sample_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> sample_cast = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast, var_56_cast))[name = tensor<string, []>("sample_cast")];
            tensor<int32, []> var_59 = const()[name = tensor<string, []>("op_59"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_66_axes_0 = const()[name = tensor<string, []>("op_66_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [2, 320, 1]> var_66_cast = expand_dims(axes = var_66_axes_0, x = sample_cast)[name = tensor<string, []>("op_66_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_66_cast)[name = tensor<string, []>("input_1_cast")];
            tensor<int32, [2]> var_70 = const()[name = tensor<string, []>("op_70"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_72 = const()[name = tensor<string, []>("op_72"), 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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307712))), 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, []>(307904)))];
            tensor<fp16, [2, 1280, 1, 1]> input_3_cast = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, 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_78 = const()[name = tensor<string, []>("op_78"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_80 = const()[name = tensor<string, []>("op_80"), 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]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> time_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1539392))), name = tensor<string, []>("time_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            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, []>(1539584)))];
            tensor<fp16, [2, 1280, 1, 1]> input_13_cast = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_80, groups = var_59, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight_to_fp16_palettized, x = input_5_cast)[name = tensor<string, []>("input_13_cast")];
            tensor<int32, []> var_86 = const()[name = tensor<string, []>("op_86"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_89 = const()[name = tensor<string, []>("op_89"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_91 = const()[name = tensor<string, []>("op_91"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 4, 3, 3]> conv_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [8640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1542208))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1550912))), name = tensor<string, []>("conv_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 4, 3, 3])];
            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, []>(1551104)))];
            tensor<fp16, [2, 320, 64, 64]> input_7_cast = conv(bias = conv_in_bias_to_fp16, dilations = var_91, groups = var_86, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_89, weight = conv_in_weight_to_fp16_palettized, x = sample)[name = tensor<string, []>("input_7_cast")];
            tensor<int32, []> var_95 = const()[name = tensor<string, []>("op_95"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_106 = const()[name = tensor<string, []>("op_106"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_111 = const()[name = tensor<string, []>("op_111"), 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, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_7_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, 64, 64]> sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor<string, []>("sub_0_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> 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, 64, 64]> 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, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> 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, []>(1551808)))];
            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, []>(1552512)))];
            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, []>(1553216)))];
            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, []>(1553920)))];
            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, 64, 64]> 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, 64, 64]> input_11_cast = silu(x = add_1_cast)[name = tensor<string, []>("input_11_cast")];
            tensor<int32, [2]> var_133 = const()[name = tensor<string, []>("op_133"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_135 = const()[name = tensor<string, []>("op_135"), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1554624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2245888))), name = tensor<string, []>("down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            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, []>(2246080)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_1_cast = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_111, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_133, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_11_cast)[name = tensor<string, []>("hidden_states_1_cast")];
            tensor<fp16, [2, 1280, 1, 1]> input_15_cast = silu(x = input_13_cast)[name = tensor<string, []>("input_15_cast")];
            tensor<int32, [2]> var_141 = const()[name = tensor<string, []>("op_141"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_143 = const()[name = tensor<string, []>("op_143"), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2246784))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2554048))), name = tensor<string, []>("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            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, []>(2554240)))];
            tensor<fp16, [2, 320, 1, 1]> temb_1_cast = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_143, groups = var_111, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_141, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_1_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_17_cast = add(x = hidden_states_1_cast, y = temb_1_cast)[name = tensor<string, []>("input_17_cast")];
            tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_17_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, 64, 64]> sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor<string, []>("sub_2_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> 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, 64, 64]> 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, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> 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, []>(2554944)))];
            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, []>(2555648)))];
            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, 64, 64]> 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, 64, 64]> input_21_cast = silu(x = add_3_cast)[name = tensor<string, []>("input_21_cast")];
            tensor<int32, [2]> var_153 = const()[name = tensor<string, []>("op_153"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_155 = const()[name = tensor<string, []>("op_155"), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2556352))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3247616))), name = tensor<string, []>("down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            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, []>(3247808)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_3_cast = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_155, groups = var_111, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_153, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_21_cast)[name = tensor<string, []>("hidden_states_3_cast")];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_5_cast = add(x = input_7_cast, y = hidden_states_3_cast)[name = tensor<string, []>("hidden_states_5_cast")];
            tensor<int32, [5]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_8_cast = reshape(shape = reshape_8_shape_0, x = hidden_states_5_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, 64, 64]> sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor<string, []>("sub_4_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> 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, []>(0x1.1p-20)];
            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, 64, 64]> 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, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> 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, []>(3248512)))];
            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, []>(3249216)))];
            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, 64, 64]> 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<int32, [2]> var_175 = const()[name = tensor<string, []>("op_175"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_177 = const()[name = tensor<string, []>("op_177"), 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]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3249920))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3326784))), name = tensor<string, []>("down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3326976)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_7_cast = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_177, groups = var_111, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_175, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized, x = add_5_cast)[name = tensor<string, []>("hidden_states_7_cast")];
            tensor<int32, [4]> var_182 = const()[name = tensor<string, []>("op_182"), val = tensor<int32, [4]>([2, 320, 1, 4096])];
            tensor<fp16, [2, 320, 1, 4096]> inputs_1_cast = reshape(shape = var_182, x = hidden_states_7_cast)[name = tensor<string, []>("inputs_1_cast")];
            tensor<int32, [1]> var_192 = const()[name = tensor<string, []>("op_192"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_1_cast = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast)[name = tensor<string, []>("channels_mean_1_cast")];
            tensor<fp16, [2, 320, 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, 320, 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_196 = const()[name = tensor<string, []>("op_196"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_197_cast = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast)[name = tensor<string, []>("op_197_cast")];
            tensor<fp16, []> var_198_to_fp16 = const()[name = tensor<string, []>("op_198_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_199_cast = add(x = var_197_cast, y = var_198_to_fp16)[name = tensor<string, []>("op_199_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_199_cast)[name = tensor<string, []>("denom_1_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast)[name = tensor<string, []>("out_1_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_203_to_fp16 = const()[name = tensor<string, []>("op_203_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3327680)))];
            tensor<fp16, [2, 320, 1, 4096]> var_204_cast = add(x = out_1_cast, y = var_203_to_fp16)[name = tensor<string, []>("op_204_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_206_to_fp16 = const()[name = tensor<string, []>("op_206_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3328384)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_9_cast = mul(x = var_204_cast, y = var_206_to_fp16)[name = tensor<string, []>("hidden_states_9_cast")];
            tensor<int32, [2]> var_213 = const()[name = tensor<string, []>("op_213"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_215 = const()[name = tensor<string, []>("op_215"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3329088))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3405952))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_1_cast = conv(dilations = var_215, groups = var_111, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_213, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor<string, []>("q_1_cast")];
            tensor<int32, [2]> var_219 = const()[name = tensor<string, []>("op_219"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_221 = const()[name = tensor<string, []>("op_221"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3406144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3483008))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> k_1_cast = conv(dilations = var_221, groups = var_111, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_219, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor<string, []>("k_1_cast")];
            tensor<int32, [2]> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_227 = const()[name = tensor<string, []>("op_227"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3483200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3560064))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> v_1_cast = conv(dilations = var_227, groups = var_111, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_225, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor<string, []>("v_1_cast")];
            tensor<int32, [4]> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_232_cast = reshape(shape = var_231, x = q_1_cast)[name = tensor<string, []>("op_232_cast")];
            tensor<int32, [4]> var_233 = const()[name = tensor<string, []>("op_233"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_234_cast = reshape(shape = var_233, x = k_1_cast)[name = tensor<string, []>("op_234_cast")];
            tensor<int32, [4]> var_235 = const()[name = tensor<string, []>("op_235"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_236_cast = reshape(shape = var_235, x = v_1_cast)[name = tensor<string, []>("op_236_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, 5, 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_232_cast, y = var_234_cast)[name = tensor<string, []>("attn_weights_1_cast")];
            tensor<fp16, []> var_102_to_fp16 = const()[name = tensor<string, []>("op_102_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 5, 4096, 4096]> attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_3_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> var_240_cast = softmax(axis = var_95, x = attn_weights_3_cast)[name = tensor<string, []>("op_240_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, 5, 64, 4096]> attn_1_cast = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_236_cast, y = var_240_cast)[name = tensor<string, []>("attn_1_cast")];
            tensor<int32, [4]> var_244 = const()[name = tensor<string, []>("op_244"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_25_cast = reshape(shape = var_244, x = attn_1_cast)[name = tensor<string, []>("input_25_cast")];
            tensor<int32, [2]> var_249 = const()[name = tensor<string, []>("op_249"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_251 = const()[name = tensor<string, []>("op_251"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_253_pad_type_0 = const()[name = tensor<string, []>("op_253_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_253_pad_0 = const()[name = tensor<string, []>("op_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3560256))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3637120))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3637312)))];
            tensor<fp16, [2, 320, 1, 4096]> var_253_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_251, groups = var_111, pad = var_253_pad_0, pad_type = var_253_pad_type_0, strides = var_249, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_25_cast)[name = tensor<string, []>("op_253_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_3_cast = add(x = var_253_cast, y = inputs_1_cast)[name = tensor<string, []>("inputs_3_cast")];
            tensor<int32, [1]> var_257 = const()[name = tensor<string, []>("op_257"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_3_cast = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast)[name = tensor<string, []>("channels_mean_3_cast")];
            tensor<fp16, [2, 320, 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, 320, 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_261 = const()[name = tensor<string, []>("op_261"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_262_cast = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast)[name = tensor<string, []>("op_262_cast")];
            tensor<fp16, []> var_263_to_fp16 = const()[name = tensor<string, []>("op_263_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_264_cast = add(x = var_262_cast, y = var_263_to_fp16)[name = tensor<string, []>("op_264_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_264_cast)[name = tensor<string, []>("denom_3_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast)[name = tensor<string, []>("out_3_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_268_to_fp16 = const()[name = tensor<string, []>("op_268_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3638016)))];
            tensor<fp16, [2, 320, 1, 4096]> var_269_cast = add(x = out_3_cast, y = var_268_to_fp16)[name = tensor<string, []>("op_269_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_271_to_fp16 = const()[name = tensor<string, []>("op_271_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3638720)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_11_cast = mul(x = var_269_cast, y = var_271_to_fp16)[name = tensor<string, []>("hidden_states_11_cast")];
            tensor<int32, [2]> var_278 = const()[name = tensor<string, []>("op_278"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_280 = const()[name = tensor<string, []>("op_280"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3639424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3716288))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_3_cast = conv(dilations = var_280, groups = var_111, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_278, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_11_cast)[name = tensor<string, []>("q_3_cast")];
            tensor<int32, [2]> var_284 = const()[name = tensor<string, []>("op_284"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_286 = const()[name = tensor<string, []>("op_286"), 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, [320, 1024, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3716480))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3962304))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> k_3_cast = conv(dilations = var_286, groups = var_111, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_284, weight = down_blocks_0_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_290 = const()[name = tensor<string, []>("op_290"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_292 = const()[name = tensor<string, []>("op_292"), 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, [320, 1024, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3962496))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4208320))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> v_3_cast = conv(dilations = var_292, groups = var_111, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_290, weight = down_blocks_0_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_296 = const()[name = tensor<string, []>("op_296"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_297_cast = reshape(shape = var_296, x = q_3_cast)[name = tensor<string, []>("op_297_cast")];
            tensor<int32, [4]> var_298 = const()[name = tensor<string, []>("op_298"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_299_cast = reshape(shape = var_298, x = k_3_cast)[name = tensor<string, []>("op_299_cast")];
            tensor<int32, [4]> var_300 = const()[name = tensor<string, []>("op_300"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_301_cast = reshape(shape = var_300, x = v_3_cast)[name = tensor<string, []>("op_301_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, 5, 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_297_cast, y = var_299_cast)[name = tensor<string, []>("attn_weights_5_cast")];
            tensor<fp16, [2, 5, 4096, 77]> attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_7_cast")];
            tensor<fp16, [2, 5, 4096, 77]> var_305_cast = softmax(axis = var_95, x = attn_weights_7_cast)[name = tensor<string, []>("op_305_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, 5, 64, 4096]> attn_3_cast = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_301_cast, y = var_305_cast)[name = tensor<string, []>("attn_3_cast")];
            tensor<int32, [4]> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_27_cast = reshape(shape = var_309, x = attn_3_cast)[name = tensor<string, []>("input_27_cast")];
            tensor<int32, [2]> var_314 = const()[name = tensor<string, []>("op_314"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_316 = const()[name = tensor<string, []>("op_316"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_318_pad_type_0 = const()[name = tensor<string, []>("op_318_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_318_pad_0 = const()[name = tensor<string, []>("op_318_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4208512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4285376))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4285568)))];
            tensor<fp16, [2, 320, 1, 4096]> var_318_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_316, groups = var_111, pad = var_318_pad_0, pad_type = var_318_pad_type_0, strides = var_314, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_27_cast)[name = tensor<string, []>("op_318_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_5_cast = add(x = var_318_cast, y = inputs_3_cast)[name = tensor<string, []>("inputs_5_cast")];
            tensor<int32, [1]> var_322 = const()[name = tensor<string, []>("op_322"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_5_cast = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast)[name = tensor<string, []>("channels_mean_5_cast")];
            tensor<fp16, [2, 320, 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, 320, 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_326 = const()[name = tensor<string, []>("op_326"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_327_cast = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast)[name = tensor<string, []>("op_327_cast")];
            tensor<fp16, []> var_328_to_fp16 = const()[name = tensor<string, []>("op_328_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_329_cast = add(x = var_327_cast, y = var_328_to_fp16)[name = tensor<string, []>("op_329_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_329_cast)[name = tensor<string, []>("denom_5_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast)[name = tensor<string, []>("out_5_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_333_to_fp16 = const()[name = tensor<string, []>("op_333_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4286272)))];
            tensor<fp16, [2, 320, 1, 4096]> var_334_cast = add(x = out_5_cast, y = var_333_to_fp16)[name = tensor<string, []>("op_334_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_336_to_fp16 = const()[name = tensor<string, []>("op_336_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4286976)))];
            tensor<fp16, [2, 320, 1, 4096]> input_29_cast = mul(x = var_334_cast, y = var_336_to_fp16)[name = tensor<string, []>("input_29_cast")];
            tensor<int32, [2]> var_344 = const()[name = tensor<string, []>("op_344"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_346 = const()[name = tensor<string, []>("op_346"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_348_pad_type_0 = const()[name = tensor<string, []>("op_348_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_348_pad_0 = const()[name = tensor<string, []>("op_348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [2560, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_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, []>(4287680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4902144))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([2560, 320, 1, 1])];
            tensor<fp16, [2560]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4902336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4904320))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2, 2560, 1, 4096]> var_348_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_346, groups = var_111, pad = var_348_pad_0, pad_type = var_348_pad_type_0, strides = var_344, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_29_cast)[name = tensor<string, []>("op_348_cast")];
            tensor<int32, [2]> var_349_split_sizes_0 = const()[name = tensor<string, []>("op_349_split_sizes_0"), val = tensor<int32, [2]>([1280, 1280])];
            tensor<int32, []> var_349_axis_0 = const()[name = tensor<string, []>("op_349_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 1280, 1, 4096]> var_349_cast_0, tensor<fp16, [2, 1280, 1, 4096]> var_349_cast_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast)[name = tensor<string, []>("op_349_cast")];
            tensor<string, []> var_351_mode_0 = const()[name = tensor<string, []>("op_351_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 4096]> var_351_cast = gelu(mode = var_351_mode_0, x = var_349_cast_1)[name = tensor<string, []>("op_351_cast")];
            tensor<fp16, [2, 1280, 1, 4096]> input_31_cast = mul(x = var_349_cast_0, y = var_351_cast)[name = tensor<string, []>("input_31_cast")];
            tensor<int32, [2]> var_355 = const()[name = tensor<string, []>("op_355"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_357 = const()[name = tensor<string, []>("op_357"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_359_pad_type_0 = const()[name = tensor<string, []>("op_359_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_359_pad_0 = const()[name = tensor<string, []>("op_359_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4904512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5211776))), name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5211968)))];
            tensor<fp16, [2, 320, 1, 4096]> var_359_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_357, groups = var_111, pad = var_359_pad_0, pad_type = var_359_pad_type_0, strides = var_355, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_31_cast)[name = tensor<string, []>("op_359_cast")];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_15_cast = add(x = var_359_cast, y = inputs_5_cast)[name = tensor<string, []>("hidden_states_15_cast")];
            tensor<int32, [4]> var_361 = const()[name = tensor<string, []>("op_361"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> input_33_cast = reshape(shape = var_361, x = hidden_states_15_cast)[name = tensor<string, []>("input_33_cast")];
            tensor<int32, [2]> var_365 = const()[name = tensor<string, []>("op_365"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_367 = const()[name = tensor<string, []>("op_367"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_17_pad_type_0 = const()[name = tensor<string, []>("hidden_states_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = tensor<string, []>("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5212672))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5289536))), name = tensor<string, []>("down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5289728)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_17_cast = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_367, groups = var_111, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_365, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized, x = input_33_cast)[name = tensor<string, []>("hidden_states_17_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_35_cast = add(x = hidden_states_17_cast, y = hidden_states_5_cast)[name = tensor<string, []>("input_35_cast")];
            tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_35_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, 64, 64]> sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor<string, []>("sub_6_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> 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, 64, 64]> 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, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> 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, []>(5290432)))];
            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, []>(5291136)))];
            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, 64, 64]> 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, 64, 64]> input_39_cast = silu(x = add_7_cast)[name = tensor<string, []>("input_39_cast")];
            tensor<int32, [2]> var_382 = const()[name = tensor<string, []>("op_382"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_384 = const()[name = tensor<string, []>("op_384"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_19_pad_type_0 = const()[name = tensor<string, []>("hidden_states_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = tensor<string, []>("hidden_states_19_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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5291840))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5983104))), name = tensor<string, []>("down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            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, []>(5983296)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_19_cast = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_384, groups = var_111, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_382, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_39_cast)[name = tensor<string, []>("hidden_states_19_cast")];
            tensor<int32, [2]> var_390 = const()[name = tensor<string, []>("op_390"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_392 = const()[name = tensor<string, []>("op_392"), 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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5984000))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6291264))), 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, []>(6291456)))];
            tensor<fp16, [2, 320, 1, 1]> temb_3_cast = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_392, groups = var_111, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_390, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_3_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_43_cast = add(x = hidden_states_19_cast, y = temb_3_cast)[name = tensor<string, []>("input_43_cast")];
            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_43_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, []>(6292160)))];
            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, []>(6292864)))];
            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_47_cast = silu(x = add_9_cast)[name = tensor<string, []>("input_47_cast")];
            tensor<int32, [2]> var_402 = const()[name = tensor<string, []>("op_402"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_404 = const()[name = tensor<string, []>("op_404"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_21_pad_type_0 = const()[name = tensor<string, []>("hidden_states_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = tensor<string, []>("hidden_states_21_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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6293568))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6984832))), name = tensor<string, []>("down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            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, []>(6985024)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_21_cast = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_404, groups = var_111, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_402, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_47_cast)[name = tensor<string, []>("hidden_states_21_cast")];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_23_cast = add(x = input_35_cast, y = hidden_states_21_cast)[name = tensor<string, []>("hidden_states_23_cast")];
            tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_20_cast = reshape(shape = reshape_20_shape_0, x = hidden_states_23_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, 10, 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, 10, 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, []>(0x1.1p-20)];
            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, 10, 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, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor<string, []>("reshape_21_cast")];
            tensor<fp16, [320]> add_11_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_11_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6985728)))];
            tensor<fp16, [320]> add_11_beta_0_to_fp16 = const()[name = tensor<string, []>("add_11_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6986432)))];
            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, 320, 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast)[name = tensor<string, []>("add_11_cast")];
            tensor<int32, [2]> var_424 = const()[name = tensor<string, []>("op_424"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_426 = const()[name = tensor<string, []>("op_426"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_25_pad_type_0 = const()[name = tensor<string, []>("hidden_states_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_25_pad_0 = const()[name = tensor<string, []>("hidden_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6987136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7064000))), name = tensor<string, []>("down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7064192)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_25_cast = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_426, groups = var_111, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_424, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized, x = add_11_cast)[name = tensor<string, []>("hidden_states_25_cast")];
            tensor<int32, [4]> var_431 = const()[name = tensor<string, []>("op_431"), val = tensor<int32, [4]>([2, 320, 1, 4096])];
            tensor<fp16, [2, 320, 1, 4096]> inputs_7_cast = reshape(shape = var_431, x = hidden_states_25_cast)[name = tensor<string, []>("inputs_7_cast")];
            tensor<int32, [1]> var_441 = const()[name = tensor<string, []>("op_441"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_7_cast = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast)[name = tensor<string, []>("channels_mean_7_cast")];
            tensor<fp16, [2, 320, 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, 320, 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_445 = const()[name = tensor<string, []>("op_445"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_446_cast = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast)[name = tensor<string, []>("op_446_cast")];
            tensor<fp16, []> var_447_to_fp16 = const()[name = tensor<string, []>("op_447_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_448_cast = add(x = var_446_cast, y = var_447_to_fp16)[name = tensor<string, []>("op_448_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_448_cast)[name = tensor<string, []>("denom_7_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast)[name = tensor<string, []>("out_7_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_452_to_fp16 = const()[name = tensor<string, []>("op_452_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7064896)))];
            tensor<fp16, [2, 320, 1, 4096]> var_453_cast = add(x = out_7_cast, y = var_452_to_fp16)[name = tensor<string, []>("op_453_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_455_to_fp16 = const()[name = tensor<string, []>("op_455_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7065600)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_27_cast = mul(x = var_453_cast, y = var_455_to_fp16)[name = tensor<string, []>("hidden_states_27_cast")];
            tensor<int32, [2]> var_462 = const()[name = tensor<string, []>("op_462"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_464 = const()[name = tensor<string, []>("op_464"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7066304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7143168))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_5_cast = conv(dilations = var_464, groups = var_111, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_462, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor<string, []>("q_5_cast")];
            tensor<int32, [2]> var_468 = const()[name = tensor<string, []>("op_468"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_470 = const()[name = tensor<string, []>("op_470"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7143360))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7220224))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> k_5_cast = conv(dilations = var_470, groups = var_111, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_468, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor<string, []>("k_5_cast")];
            tensor<int32, [2]> var_474 = const()[name = tensor<string, []>("op_474"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_476 = const()[name = tensor<string, []>("op_476"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7220416))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7297280))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> v_5_cast = conv(dilations = var_476, groups = var_111, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_474, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor<string, []>("v_5_cast")];
            tensor<int32, [4]> var_480 = const()[name = tensor<string, []>("op_480"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_481_cast = reshape(shape = var_480, x = q_5_cast)[name = tensor<string, []>("op_481_cast")];
            tensor<int32, [4]> var_482 = const()[name = tensor<string, []>("op_482"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_483_cast = reshape(shape = var_482, x = k_5_cast)[name = tensor<string, []>("op_483_cast")];
            tensor<int32, [4]> var_484 = const()[name = tensor<string, []>("op_484"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_485_cast = reshape(shape = var_484, x = v_5_cast)[name = tensor<string, []>("op_485_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, 5, 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_481_cast, y = var_483_cast)[name = tensor<string, []>("attn_weights_9_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_11_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> var_489_cast = softmax(axis = var_95, x = attn_weights_11_cast)[name = tensor<string, []>("op_489_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, 5, 64, 4096]> attn_5_cast = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_485_cast, y = var_489_cast)[name = tensor<string, []>("attn_5_cast")];
            tensor<int32, [4]> var_493 = const()[name = tensor<string, []>("op_493"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_51_cast = reshape(shape = var_493, x = attn_5_cast)[name = tensor<string, []>("input_51_cast")];
            tensor<int32, [2]> var_498 = const()[name = tensor<string, []>("op_498"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_500 = const()[name = tensor<string, []>("op_500"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_502_pad_type_0 = const()[name = tensor<string, []>("op_502_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_502_pad_0 = const()[name = tensor<string, []>("op_502_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7297472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7374336))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7374528)))];
            tensor<fp16, [2, 320, 1, 4096]> var_502_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_500, groups = var_111, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_498, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_51_cast)[name = tensor<string, []>("op_502_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_9_cast = add(x = var_502_cast, y = inputs_7_cast)[name = tensor<string, []>("inputs_9_cast")];
            tensor<int32, [1]> var_506 = const()[name = tensor<string, []>("op_506"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_9_cast = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast)[name = tensor<string, []>("channels_mean_9_cast")];
            tensor<fp16, [2, 320, 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, 320, 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_510 = const()[name = tensor<string, []>("op_510"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_511_cast = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast)[name = tensor<string, []>("op_511_cast")];
            tensor<fp16, []> var_512_to_fp16 = const()[name = tensor<string, []>("op_512_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_513_cast = add(x = var_511_cast, y = var_512_to_fp16)[name = tensor<string, []>("op_513_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_513_cast)[name = tensor<string, []>("denom_9_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast)[name = tensor<string, []>("out_9_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_517_to_fp16 = const()[name = tensor<string, []>("op_517_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7375232)))];
            tensor<fp16, [2, 320, 1, 4096]> var_518_cast = add(x = out_9_cast, y = var_517_to_fp16)[name = tensor<string, []>("op_518_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_520_to_fp16 = const()[name = tensor<string, []>("op_520_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7375936)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_29_cast = mul(x = var_518_cast, y = var_520_to_fp16)[name = tensor<string, []>("hidden_states_29_cast")];
            tensor<int32, [2]> var_527 = const()[name = tensor<string, []>("op_527"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_529 = const()[name = tensor<string, []>("op_529"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7376640))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7453504))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_7_cast = conv(dilations = var_529, groups = var_111, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_527, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_29_cast)[name = tensor<string, []>("q_7_cast")];
            tensor<int32, [2]> var_533 = const()[name = tensor<string, []>("op_533"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_535 = const()[name = tensor<string, []>("op_535"), 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, [320, 1024, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7453696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7699520))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> k_7_cast = conv(dilations = var_535, groups = var_111, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_533, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_7_cast")];
            tensor<int32, [2]> var_539 = const()[name = tensor<string, []>("op_539"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_541 = const()[name = tensor<string, []>("op_541"), 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, [320, 1024, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7699712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7945536))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> v_7_cast = conv(dilations = var_541, groups = var_111, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_539, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_7_cast")];
            tensor<int32, [4]> var_545 = const()[name = tensor<string, []>("op_545"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_546_cast = reshape(shape = var_545, x = q_7_cast)[name = tensor<string, []>("op_546_cast")];
            tensor<int32, [4]> var_547 = const()[name = tensor<string, []>("op_547"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_548_cast = reshape(shape = var_547, x = k_7_cast)[name = tensor<string, []>("op_548_cast")];
            tensor<int32, [4]> var_549 = const()[name = tensor<string, []>("op_549"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_550_cast = reshape(shape = var_549, x = v_7_cast)[name = tensor<string, []>("op_550_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, 5, 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_546_cast, y = var_548_cast)[name = tensor<string, []>("attn_weights_13_cast")];
            tensor<fp16, [2, 5, 4096, 77]> attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_15_cast")];
            tensor<fp16, [2, 5, 4096, 77]> var_554_cast = softmax(axis = var_95, x = attn_weights_15_cast)[name = tensor<string, []>("op_554_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, 5, 64, 4096]> attn_7_cast = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_550_cast, y = var_554_cast)[name = tensor<string, []>("attn_7_cast")];
            tensor<int32, [4]> var_558 = const()[name = tensor<string, []>("op_558"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_53_cast = reshape(shape = var_558, x = attn_7_cast)[name = tensor<string, []>("input_53_cast")];
            tensor<int32, [2]> var_563 = const()[name = tensor<string, []>("op_563"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_565 = const()[name = tensor<string, []>("op_565"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_567_pad_type_0 = const()[name = tensor<string, []>("op_567_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_567_pad_0 = const()[name = tensor<string, []>("op_567_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7945728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8022592))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8022784)))];
            tensor<fp16, [2, 320, 1, 4096]> var_567_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_565, groups = var_111, pad = var_567_pad_0, pad_type = var_567_pad_type_0, strides = var_563, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_53_cast)[name = tensor<string, []>("op_567_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_11_cast = add(x = var_567_cast, y = inputs_9_cast)[name = tensor<string, []>("inputs_11_cast")];
            tensor<int32, [1]> var_571 = const()[name = tensor<string, []>("op_571"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_11_cast = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast)[name = tensor<string, []>("channels_mean_11_cast")];
            tensor<fp16, [2, 320, 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, 320, 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_575 = const()[name = tensor<string, []>("op_575"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_576_cast = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast)[name = tensor<string, []>("op_576_cast")];
            tensor<fp16, []> var_577_to_fp16 = const()[name = tensor<string, []>("op_577_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_578_cast = add(x = var_576_cast, y = var_577_to_fp16)[name = tensor<string, []>("op_578_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_578_cast)[name = tensor<string, []>("denom_11_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast)[name = tensor<string, []>("out_11_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_582_to_fp16 = const()[name = tensor<string, []>("op_582_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8023488)))];
            tensor<fp16, [2, 320, 1, 4096]> var_583_cast = add(x = out_11_cast, y = var_582_to_fp16)[name = tensor<string, []>("op_583_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_585_to_fp16 = const()[name = tensor<string, []>("op_585_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8024192)))];
            tensor<fp16, [2, 320, 1, 4096]> input_55_cast = mul(x = var_583_cast, y = var_585_to_fp16)[name = tensor<string, []>("input_55_cast")];
            tensor<int32, [2]> var_593 = const()[name = tensor<string, []>("op_593"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_595 = const()[name = tensor<string, []>("op_595"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_597_pad_type_0 = const()[name = tensor<string, []>("op_597_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_597_pad_0 = const()[name = tensor<string, []>("op_597_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [2560, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_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, []>(8024896))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8639360))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([2560, 320, 1, 1])];
            tensor<fp16, [2560]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8639552))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8641536))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2, 2560, 1, 4096]> var_597_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_595, groups = var_111, pad = var_597_pad_0, pad_type = var_597_pad_type_0, strides = var_593, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_55_cast)[name = tensor<string, []>("op_597_cast")];
            tensor<int32, [2]> var_598_split_sizes_0 = const()[name = tensor<string, []>("op_598_split_sizes_0"), val = tensor<int32, [2]>([1280, 1280])];
            tensor<int32, []> var_598_axis_0 = const()[name = tensor<string, []>("op_598_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 1280, 1, 4096]> var_598_cast_0, tensor<fp16, [2, 1280, 1, 4096]> var_598_cast_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast)[name = tensor<string, []>("op_598_cast")];
            tensor<string, []> var_600_mode_0 = const()[name = tensor<string, []>("op_600_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 4096]> var_600_cast = gelu(mode = var_600_mode_0, x = var_598_cast_1)[name = tensor<string, []>("op_600_cast")];
            tensor<fp16, [2, 1280, 1, 4096]> input_57_cast = mul(x = var_598_cast_0, y = var_600_cast)[name = tensor<string, []>("input_57_cast")];
            tensor<int32, [2]> var_604 = const()[name = tensor<string, []>("op_604"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_606 = const()[name = tensor<string, []>("op_606"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_608_pad_type_0 = const()[name = tensor<string, []>("op_608_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_608_pad_0 = const()[name = tensor<string, []>("op_608_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8641728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8948992))), name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8949184)))];
            tensor<fp16, [2, 320, 1, 4096]> var_608_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_606, groups = var_111, pad = var_608_pad_0, pad_type = var_608_pad_type_0, strides = var_604, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_57_cast)[name = tensor<string, []>("op_608_cast")];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_33_cast = add(x = var_608_cast, y = inputs_11_cast)[name = tensor<string, []>("hidden_states_33_cast")];
            tensor<int32, [4]> var_610 = const()[name = tensor<string, []>("op_610"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> input_59_cast = reshape(shape = var_610, x = hidden_states_33_cast)[name = tensor<string, []>("input_59_cast")];
            tensor<int32, [2]> var_614 = const()[name = tensor<string, []>("op_614"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_616 = const()[name = tensor<string, []>("op_616"), 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]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8949888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9026752))), name = tensor<string, []>("down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9026944)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_35_cast = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_616, groups = var_111, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_614, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized, x = input_59_cast)[name = tensor<string, []>("hidden_states_35_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_61_cast = add(x = hidden_states_35_cast, y = hidden_states_23_cast)[name = tensor<string, []>("input_61_cast")];
            tensor<int32, [2]> var_623 = const()[name = tensor<string, []>("op_623"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_625 = const()[name = tensor<string, []>("op_625"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_63_pad_type_0 = const()[name = tensor<string, []>("input_63_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_63_pad_0 = const()[name = tensor<string, []>("input_63_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, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9027648))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9718912))), 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, []>(9719104)))];
            tensor<fp16, [2, 320, 32, 32]> input_63_cast = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_625, groups = var_111, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_623, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized, x = input_61_cast)[name = tensor<string, []>("input_63_cast")];
            tensor<int32, []> var_633 = const()[name = tensor<string, []>("op_633"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_644 = const()[name = tensor<string, []>("op_644"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_649 = const()[name = tensor<string, []>("op_649"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 32, 32])];
            tensor<fp16, [2, 32, 10, 32, 32]> reshape_24_cast = reshape(shape = reshape_24_shape_0, x = input_63_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, 10, 32, 32]> sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor<string, []>("sub_12_cast")];
            tensor<fp16, [2, 32, 10, 32, 32]> 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, []>(0x1.5p-17)];
            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, 10, 32, 32]> 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, 320, 32, 32])];
            tensor<fp16, [2, 320, 32, 32]> reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor<string, []>("reshape_25_cast")];
            tensor<fp16, [320]> add_13_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_13_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9719808)))];
            tensor<fp16, [320]> add_13_beta_0_to_fp16 = const()[name = tensor<string, []>("add_13_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9720512)))];
            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, 320, 32, 32]> 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_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast)[name = tensor<string, []>("add_13_cast")];
            tensor<fp16, [2, 320, 32, 32]> input_67_cast = silu(x = add_13_cast)[name = tensor<string, []>("input_67_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, []> hidden_states_37_pad_type_0 = const()[name = tensor<string, []>("hidden_states_37_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = tensor<string, []>("hidden_states_37_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, [1382400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9721216))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11103680))), 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, []>(11103872)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_37_cast = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_674, groups = var_649, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_672, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_67_cast)[name = tensor<string, []>("hidden_states_37_cast")];
            tensor<int32, [2]> var_680 = const()[name = tensor<string, []>("op_680"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_682 = const()[name = tensor<string, []>("op_682"), 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, []>(11105216))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11719680))), 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, []>(11719872)))];
            tensor<fp16, [2, 640, 1, 1]> temb_5_cast = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_682, groups = var_649, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_680, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_5_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_71_cast = add(x = hidden_states_37_cast, y = temb_5_cast)[name = tensor<string, []>("input_71_cast")];
            tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_71_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, 32, 32]> sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor<string, []>("sub_14_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> 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, 32, 32]> 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, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> 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_mean_0_to_fp16 = const()[name = tensor<string, []>("add_15_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11721216)))];
            tensor<fp16, [640]> add_15_variance_0_to_fp16 = const()[name = tensor<string, []>("add_15_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11722560)))];
            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, []>(11723904)))];
            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, []>(11725248)))];
            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, 32, 32]> 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast)[name = tensor<string, []>("add_15_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_75_cast = silu(x = add_15_cast)[name = tensor<string, []>("input_75_cast")];
            tensor<int32, [2]> var_692 = const()[name = tensor<string, []>("op_692"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_694 = const()[name = tensor<string, []>("op_694"), 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]>([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, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11726592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14491456))), 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, []>(14491648)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_39_cast = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_694, groups = var_649, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_692, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_75_cast)[name = tensor<string, []>("hidden_states_39_cast")];
            tensor<int32, [2]> var_699 = const()[name = tensor<string, []>("op_699"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_701 = const()[name = tensor<string, []>("op_701"), 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, [153600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14492992))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14646656))), 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, []>(14646848)))];
            tensor<fp16, [2, 640, 32, 32]> x_1_cast = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_701, groups = var_649, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_699, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_63_cast)[name = tensor<string, []>("x_1_cast")];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_41_cast = add(x = x_1_cast, y = hidden_states_39_cast)[name = tensor<string, []>("hidden_states_41_cast")];
            tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_32_cast = reshape(shape = reshape_32_shape_0, x = hidden_states_41_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, 32, 32]> sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor<string, []>("sub_16_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> 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, []>(0x1.1p-20)];
            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, 32, 32]> 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, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> 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, []>(14648192)))];
            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, []>(14649536)))];
            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, 32, 32]> 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_33_cast)[name = tensor<string, []>("add_17_cast")];
            tensor<int32, [2]> var_721 = const()[name = tensor<string, []>("op_721"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_723 = const()[name = tensor<string, []>("op_723"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_43_pad_type_0 = const()[name = tensor<string, []>("hidden_states_43_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = tensor<string, []>("hidden_states_43_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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14650880))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14958144))), 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, []>(14958336)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_43_cast = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_723, groups = var_649, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_17_cast)[name = tensor<string, []>("hidden_states_43_cast")];
            tensor<int32, [4]> var_728 = const()[name = tensor<string, []>("op_728"), val = tensor<int32, [4]>([2, 640, 1, 1024])];
            tensor<fp16, [2, 640, 1, 1024]> inputs_13_cast = reshape(shape = var_728, x = hidden_states_43_cast)[name = tensor<string, []>("inputs_13_cast")];
            tensor<int32, [1]> var_738 = const()[name = tensor<string, []>("op_738"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_13_cast = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast)[name = tensor<string, []>("channels_mean_13_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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, 1024]> 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_742 = const()[name = tensor<string, []>("op_742"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_743_cast = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast)[name = tensor<string, []>("op_743_cast")];
            tensor<fp16, []> var_744_to_fp16 = const()[name = tensor<string, []>("op_744_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_745_cast = add(x = var_743_cast, y = var_744_to_fp16)[name = tensor<string, []>("op_745_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, 1024]> denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_745_cast)[name = tensor<string, []>("denom_13_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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_749_to_fp16 = const()[name = tensor<string, []>("op_749_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14959680)))];
            tensor<fp16, [2, 640, 1, 1024]> var_750_cast = add(x = out_13_cast, y = var_749_to_fp16)[name = tensor<string, []>("op_750_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_752_to_fp16 = const()[name = tensor<string, []>("op_752_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14961024)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_45_cast = mul(x = var_750_cast, y = var_752_to_fp16)[name = tensor<string, []>("hidden_states_45_cast")];
            tensor<int32, [2]> var_759 = const()[name = tensor<string, []>("op_759"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_761 = const()[name = tensor<string, []>("op_761"), 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_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, []>(14962368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15269632))), 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, 1024]> q_9_cast = conv(dilations = var_761, groups = var_649, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_759, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor<string, []>("q_9_cast")];
            tensor<int32, [2]> var_765 = const()[name = tensor<string, []>("op_765"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_767 = const()[name = tensor<string, []>("op_767"), 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_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, []>(15269824))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15577088))), 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, 1024]> k_9_cast = conv(dilations = var_767, groups = var_649, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_765, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor<string, []>("k_9_cast")];
            tensor<int32, [2]> var_771 = const()[name = tensor<string, []>("op_771"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_773 = const()[name = tensor<string, []>("op_773"), 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_0_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, []>(15577280))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15884544))), 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, 1024]> v_9_cast = conv(dilations = var_773, groups = var_649, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_771, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor<string, []>("v_9_cast")];
            tensor<int32, [4]> var_777 = const()[name = tensor<string, []>("op_777"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_778_cast = reshape(shape = var_777, x = q_9_cast)[name = tensor<string, []>("op_778_cast")];
            tensor<int32, [4]> var_779 = const()[name = tensor<string, []>("op_779"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_780_cast = reshape(shape = var_779, x = k_9_cast)[name = tensor<string, []>("op_780_cast")];
            tensor<int32, [4]> var_781 = const()[name = tensor<string, []>("op_781"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_782_cast = reshape(shape = var_781, x = v_9_cast)[name = tensor<string, []>("op_782_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, 1024, 1024]> attn_weights_17_cast = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_778_cast, y = var_780_cast)[name = tensor<string, []>("attn_weights_17_cast")];
            tensor<fp16, []> var_640_to_fp16 = const()[name = tensor<string, []>("op_640_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 10, 1024, 1024]> attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_19_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> var_786_cast = softmax(axis = var_633, x = attn_weights_19_cast)[name = tensor<string, []>("op_786_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, 1024]> attn_9_cast = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_782_cast, y = var_786_cast)[name = tensor<string, []>("attn_9_cast")];
            tensor<int32, [4]> var_790 = const()[name = tensor<string, []>("op_790"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_79_cast = reshape(shape = var_790, x = attn_9_cast)[name = tensor<string, []>("input_79_cast")];
            tensor<int32, [2]> var_795 = const()[name = tensor<string, []>("op_795"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_797 = const()[name = tensor<string, []>("op_797"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_799_pad_type_0 = const()[name = tensor<string, []>("op_799_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_799_pad_0 = const()[name = tensor<string, []>("op_799_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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15884736))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16192000))), 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, []>(16192192)))];
            tensor<fp16, [2, 640, 1, 1024]> var_799_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_797, groups = var_649, pad = var_799_pad_0, pad_type = var_799_pad_type_0, strides = var_795, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_79_cast)[name = tensor<string, []>("op_799_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_15_cast = add(x = var_799_cast, y = inputs_13_cast)[name = tensor<string, []>("inputs_15_cast")];
            tensor<int32, [1]> var_803 = const()[name = tensor<string, []>("op_803"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_15_cast = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast)[name = tensor<string, []>("channels_mean_15_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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, 1024]> 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_807 = const()[name = tensor<string, []>("op_807"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_808_cast = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast)[name = tensor<string, []>("op_808_cast")];
            tensor<fp16, []> var_809_to_fp16 = const()[name = tensor<string, []>("op_809_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_810_cast = add(x = var_808_cast, y = var_809_to_fp16)[name = tensor<string, []>("op_810_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, 1024]> denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_810_cast)[name = tensor<string, []>("denom_15_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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_814_to_fp16 = const()[name = tensor<string, []>("op_814_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16193536)))];
            tensor<fp16, [2, 640, 1, 1024]> var_815_cast = add(x = out_15_cast, y = var_814_to_fp16)[name = tensor<string, []>("op_815_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_817_to_fp16 = const()[name = tensor<string, []>("op_817_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16194880)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_47_cast = mul(x = var_815_cast, y = var_817_to_fp16)[name = tensor<string, []>("hidden_states_47_cast")];
            tensor<int32, [2]> var_824 = const()[name = tensor<string, []>("op_824"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_826 = const()[name = tensor<string, []>("op_826"), 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_0_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, []>(16196224))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16503488))), 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, 1024]> q_11_cast = conv(dilations = var_826, groups = var_649, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_824, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_47_cast)[name = tensor<string, []>("q_11_cast")];
            tensor<int32, [2]> var_830 = const()[name = tensor<string, []>("op_830"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_832 = const()[name = tensor<string, []>("op_832"), 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, 1024, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16503680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16995264))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_11_cast = conv(dilations = var_832, groups = var_649, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_830, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_11_cast")];
            tensor<int32, [2]> var_836 = const()[name = tensor<string, []>("op_836"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_838 = const()[name = tensor<string, []>("op_838"), 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, 1024, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16995456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17487040))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_11_cast = conv(dilations = var_838, groups = var_649, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_836, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_11_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, 1024]> var_843_cast = reshape(shape = var_842, x = q_11_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, 77]> var_845_cast = reshape(shape = var_844, x = k_11_cast)[name = tensor<string, []>("op_845_cast")];
            tensor<int32, [4]> var_846 = const()[name = tensor<string, []>("op_846"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_847_cast = reshape(shape = var_846, x = v_11_cast)[name = tensor<string, []>("op_847_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, 1024, 77]> attn_weights_21_cast = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_843_cast, y = var_845_cast)[name = tensor<string, []>("attn_weights_21_cast")];
            tensor<fp16, [2, 10, 1024, 77]> attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_23_cast")];
            tensor<fp16, [2, 10, 1024, 77]> var_851_cast = softmax(axis = var_633, x = attn_weights_23_cast)[name = tensor<string, []>("op_851_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, 1024]> attn_11_cast = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_847_cast, y = var_851_cast)[name = tensor<string, []>("attn_11_cast")];
            tensor<int32, [4]> var_855 = const()[name = tensor<string, []>("op_855"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_81_cast = reshape(shape = var_855, x = attn_11_cast)[name = tensor<string, []>("input_81_cast")];
            tensor<int32, [2]> var_860 = const()[name = tensor<string, []>("op_860"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_862 = const()[name = tensor<string, []>("op_862"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_864_pad_type_0 = const()[name = tensor<string, []>("op_864_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_864_pad_0 = const()[name = tensor<string, []>("op_864_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, []>(17487232))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17794496))), 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, []>(17794688)))];
            tensor<fp16, [2, 640, 1, 1024]> var_864_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_862, groups = var_649, pad = var_864_pad_0, pad_type = var_864_pad_type_0, strides = var_860, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_81_cast)[name = tensor<string, []>("op_864_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_17_cast = add(x = var_864_cast, y = inputs_15_cast)[name = tensor<string, []>("inputs_17_cast")];
            tensor<int32, [1]> var_868 = const()[name = tensor<string, []>("op_868"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_17_cast = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast)[name = tensor<string, []>("channels_mean_17_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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, 1024]> 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_872 = const()[name = tensor<string, []>("op_872"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_873_cast = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast)[name = tensor<string, []>("op_873_cast")];
            tensor<fp16, []> var_874_to_fp16 = const()[name = tensor<string, []>("op_874_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_875_cast = add(x = var_873_cast, y = var_874_to_fp16)[name = tensor<string, []>("op_875_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, 1024]> denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_875_cast)[name = tensor<string, []>("denom_17_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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_879_to_fp16 = const()[name = tensor<string, []>("op_879_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17796032)))];
            tensor<fp16, [2, 640, 1, 1024]> var_880_cast = add(x = out_17_cast, y = var_879_to_fp16)[name = tensor<string, []>("op_880_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_882_to_fp16 = const()[name = tensor<string, []>("op_882_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17797376)))];
            tensor<fp16, [2, 640, 1, 1024]> input_83_cast = mul(x = var_880_cast, y = var_882_to_fp16)[name = tensor<string, []>("input_83_cast")];
            tensor<int32, [2]> var_890 = const()[name = tensor<string, []>("op_890"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_892 = const()[name = tensor<string, []>("op_892"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_894_pad_type_0 = const()[name = tensor<string, []>("op_894_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_894_pad_0 = const()[name = tensor<string, []>("op_894_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, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17798720))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20256384))), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3840]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20256576))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20260480))), name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [2, 5120, 1, 1024]> var_894_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_892, groups = var_649, pad = var_894_pad_0, pad_type = var_894_pad_type_0, strides = var_890, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_83_cast)[name = tensor<string, []>("op_894_cast")];
            tensor<int32, [2]> var_895_split_sizes_0 = const()[name = tensor<string, []>("op_895_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_895_axis_0 = const()[name = tensor<string, []>("op_895_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 1024]> var_895_cast_0, tensor<fp16, [2, 2560, 1, 1024]> var_895_cast_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast)[name = tensor<string, []>("op_895_cast")];
            tensor<string, []> var_897_mode_0 = const()[name = tensor<string, []>("op_897_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 1024]> var_897_cast = gelu(mode = var_897_mode_0, x = var_895_cast_1)[name = tensor<string, []>("op_897_cast")];
            tensor<fp16, [2, 2560, 1, 1024]> input_85_cast = mul(x = var_895_cast_0, y = var_897_cast)[name = tensor<string, []>("input_85_cast")];
            tensor<int32, [2]> var_901 = const()[name = tensor<string, []>("op_901"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_903 = const()[name = tensor<string, []>("op_903"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_905_pad_type_0 = const()[name = tensor<string, []>("op_905_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_905_pad_0 = const()[name = tensor<string, []>("op_905_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20260672))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21489536))), 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, []>(21489728)))];
            tensor<fp16, [2, 640, 1, 1024]> var_905_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_903, groups = var_649, pad = var_905_pad_0, pad_type = var_905_pad_type_0, strides = var_901, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_85_cast)[name = tensor<string, []>("op_905_cast")];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_51_cast = add(x = var_905_cast, y = inputs_17_cast)[name = tensor<string, []>("hidden_states_51_cast")];
            tensor<int32, [4]> var_907 = const()[name = tensor<string, []>("op_907"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> input_87_cast = reshape(shape = var_907, x = hidden_states_51_cast)[name = tensor<string, []>("input_87_cast")];
            tensor<int32, [2]> var_911 = const()[name = tensor<string, []>("op_911"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_913 = const()[name = tensor<string, []>("op_913"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_53_pad_type_0 = const()[name = tensor<string, []>("hidden_states_53_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = tensor<string, []>("hidden_states_53_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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21491072))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21798336))), 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, []>(21798528)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_53_cast = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_913, groups = var_649, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_911, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_87_cast)[name = tensor<string, []>("hidden_states_53_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_89_cast = add(x = hidden_states_53_cast, y = hidden_states_41_cast)[name = tensor<string, []>("input_89_cast")];
            tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_89_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, 32, 32]> sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor<string, []>("sub_18_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> 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, []>(0x1.5p-17)];
            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, 32, 32]> 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, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> 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, []>(21799872)))];
            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, []>(21801216)))];
            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, 32, 32]> 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_37_cast)[name = tensor<string, []>("add_19_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_93_cast = silu(x = add_19_cast)[name = tensor<string, []>("input_93_cast")];
            tensor<int32, [2]> var_928 = const()[name = tensor<string, []>("op_928"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_930 = const()[name = tensor<string, []>("op_930"), 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]>([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, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21802560))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24567424))), 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, []>(24567616)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_55_cast = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_930, groups = var_649, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_928, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_93_cast)[name = tensor<string, []>("hidden_states_55_cast")];
            tensor<int32, [2]> var_936 = const()[name = tensor<string, []>("op_936"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_938 = const()[name = tensor<string, []>("op_938"), 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, []>(24568960))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25183424))), 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, []>(25183616)))];
            tensor<fp16, [2, 640, 1, 1]> temb_7_cast = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_938, groups = var_649, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_936, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_7_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_97_cast = add(x = hidden_states_55_cast, y = temb_7_cast)[name = tensor<string, []>("input_97_cast")];
            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_97_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, []>(25184960)))];
            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, []>(25186304)))];
            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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_41_cast)[name = tensor<string, []>("add_21_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_101_cast = silu(x = add_21_cast)[name = tensor<string, []>("input_101_cast")];
            tensor<int32, [2]> var_948 = const()[name = tensor<string, []>("op_948"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_950 = const()[name = tensor<string, []>("op_950"), 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, [640, 640, 3, 3]> down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25187648))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27952512))), 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, []>(27952704)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_57_cast = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_950, groups = var_649, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_948, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_101_cast)[name = tensor<string, []>("hidden_states_57_cast")];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_59_cast = add(x = input_89_cast, y = hidden_states_57_cast)[name = tensor<string, []>("hidden_states_59_cast")];
            tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_44_cast = reshape(shape = reshape_44_shape_0, x = hidden_states_59_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, 20, 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, 20, 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, []>(0x1.1p-20)];
            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, 20, 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, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor<string, []>("reshape_45_cast")];
            tensor<fp16, [640]> add_23_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_23_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27954048)))];
            tensor<fp16, [640]> add_23_beta_0_to_fp16 = const()[name = tensor<string, []>("add_23_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27955392)))];
            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, 640, 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_45_cast)[name = tensor<string, []>("add_23_cast")];
            tensor<int32, [2]> var_970 = const()[name = tensor<string, []>("op_970"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_972 = const()[name = tensor<string, []>("op_972"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_61_pad_type_0 = const()[name = tensor<string, []>("hidden_states_61_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_61_pad_0 = const()[name = tensor<string, []>("hidden_states_61_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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27956736))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28264000))), 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, []>(28264192)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_61_cast = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_972, groups = var_649, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_970, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_23_cast)[name = tensor<string, []>("hidden_states_61_cast")];
            tensor<int32, [4]> var_977 = const()[name = tensor<string, []>("op_977"), val = tensor<int32, [4]>([2, 640, 1, 1024])];
            tensor<fp16, [2, 640, 1, 1024]> inputs_19_cast = reshape(shape = var_977, x = hidden_states_61_cast)[name = tensor<string, []>("inputs_19_cast")];
            tensor<int32, [1]> var_987 = const()[name = tensor<string, []>("op_987"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_19_cast = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast)[name = tensor<string, []>("channels_mean_19_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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, 1024]> 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_991 = const()[name = tensor<string, []>("op_991"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_992_cast = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast)[name = tensor<string, []>("op_992_cast")];
            tensor<fp16, []> var_993_to_fp16 = const()[name = tensor<string, []>("op_993_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_994_cast = add(x = var_992_cast, y = var_993_to_fp16)[name = tensor<string, []>("op_994_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, 1024]> denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_994_cast)[name = tensor<string, []>("denom_19_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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_998_to_fp16 = const()[name = tensor<string, []>("op_998_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28265536)))];
            tensor<fp16, [2, 640, 1, 1024]> var_999_cast = add(x = out_19_cast, y = var_998_to_fp16)[name = tensor<string, []>("op_999_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_1001_to_fp16 = const()[name = tensor<string, []>("op_1001_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28266880)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_63_cast = mul(x = var_999_cast, y = var_1001_to_fp16)[name = tensor<string, []>("hidden_states_63_cast")];
            tensor<int32, [2]> var_1008 = const()[name = tensor<string, []>("op_1008"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1010 = const()[name = tensor<string, []>("op_1010"), 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_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, []>(28268224))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28575488))), 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, 1024]> q_13_cast = conv(dilations = var_1010, groups = var_649, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1008, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor<string, []>("q_13_cast")];
            tensor<int32, [2]> var_1014 = const()[name = tensor<string, []>("op_1014"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1016 = const()[name = tensor<string, []>("op_1016"), 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_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, []>(28575680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28882944))), 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, 1024]> k_13_cast = conv(dilations = var_1016, groups = var_649, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1014, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor<string, []>("k_13_cast")];
            tensor<int32, [2]> var_1020 = const()[name = tensor<string, []>("op_1020"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1022 = const()[name = tensor<string, []>("op_1022"), 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_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, []>(28883136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29190400))), 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, 1024]> v_13_cast = conv(dilations = var_1022, groups = var_649, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1020, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor<string, []>("v_13_cast")];
            tensor<int32, [4]> var_1026 = const()[name = tensor<string, []>("op_1026"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1027_cast = reshape(shape = var_1026, x = q_13_cast)[name = tensor<string, []>("op_1027_cast")];
            tensor<int32, [4]> var_1028 = const()[name = tensor<string, []>("op_1028"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1029_cast = reshape(shape = var_1028, x = k_13_cast)[name = tensor<string, []>("op_1029_cast")];
            tensor<int32, [4]> var_1030 = const()[name = tensor<string, []>("op_1030"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1031_cast = reshape(shape = var_1030, x = v_13_cast)[name = tensor<string, []>("op_1031_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, 1024, 1024]> attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1027_cast, y = var_1029_cast)[name = tensor<string, []>("attn_weights_25_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_27_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> var_1035_cast = softmax(axis = var_633, x = attn_weights_27_cast)[name = tensor<string, []>("op_1035_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, 1024]> attn_13_cast = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1031_cast, y = var_1035_cast)[name = tensor<string, []>("attn_13_cast")];
            tensor<int32, [4]> var_1039 = const()[name = tensor<string, []>("op_1039"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_105_cast = reshape(shape = var_1039, x = attn_13_cast)[name = tensor<string, []>("input_105_cast")];
            tensor<int32, [2]> var_1044 = const()[name = tensor<string, []>("op_1044"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1046 = const()[name = tensor<string, []>("op_1046"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1048_pad_type_0 = const()[name = tensor<string, []>("op_1048_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1048_pad_0 = const()[name = tensor<string, []>("op_1048_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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29190592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29497856))), 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, []>(29498048)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1048_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1046, groups = var_649, pad = var_1048_pad_0, pad_type = var_1048_pad_type_0, strides = var_1044, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_105_cast)[name = tensor<string, []>("op_1048_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_21_cast = add(x = var_1048_cast, y = inputs_19_cast)[name = tensor<string, []>("inputs_21_cast")];
            tensor<int32, [1]> var_1052 = const()[name = tensor<string, []>("op_1052"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_21_cast = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast)[name = tensor<string, []>("channels_mean_21_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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, 1024]> 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_1056 = const()[name = tensor<string, []>("op_1056"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1057_cast = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_cast)[name = tensor<string, []>("op_1057_cast")];
            tensor<fp16, []> var_1058_to_fp16 = const()[name = tensor<string, []>("op_1058_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1059_cast = add(x = var_1057_cast, y = var_1058_to_fp16)[name = tensor<string, []>("op_1059_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, 1024]> denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1059_cast)[name = tensor<string, []>("denom_21_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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_1063_to_fp16 = const()[name = tensor<string, []>("op_1063_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29499392)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1064_cast = add(x = out_21_cast, y = var_1063_to_fp16)[name = tensor<string, []>("op_1064_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_1066_to_fp16 = const()[name = tensor<string, []>("op_1066_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29500736)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_65_cast = mul(x = var_1064_cast, y = var_1066_to_fp16)[name = tensor<string, []>("hidden_states_65_cast")];
            tensor<int32, [2]> var_1073 = const()[name = tensor<string, []>("op_1073"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1075 = const()[name = tensor<string, []>("op_1075"), 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_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, []>(29502080))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29809344))), 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, 1024]> q_15_cast = conv(dilations = var_1075, groups = var_649, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1073, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_65_cast)[name = tensor<string, []>("q_15_cast")];
            tensor<int32, [2]> var_1079 = const()[name = tensor<string, []>("op_1079"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1081 = const()[name = tensor<string, []>("op_1081"), 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, 1024, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29809536))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30301120))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_15_cast = conv(dilations = var_1081, groups = var_649, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1079, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_15_cast")];
            tensor<int32, [2]> var_1085 = const()[name = tensor<string, []>("op_1085"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1087 = const()[name = tensor<string, []>("op_1087"), 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, 1024, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30301312))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30792896))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_15_cast = conv(dilations = var_1087, groups = var_649, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_15_cast")];
            tensor<int32, [4]> var_1091 = const()[name = tensor<string, []>("op_1091"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1092_cast = reshape(shape = var_1091, x = q_15_cast)[name = tensor<string, []>("op_1092_cast")];
            tensor<int32, [4]> var_1093 = const()[name = tensor<string, []>("op_1093"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_1094_cast = reshape(shape = var_1093, x = k_15_cast)[name = tensor<string, []>("op_1094_cast")];
            tensor<int32, [4]> var_1095 = const()[name = tensor<string, []>("op_1095"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_1096_cast = reshape(shape = var_1095, x = v_15_cast)[name = tensor<string, []>("op_1096_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, 1024, 77]> attn_weights_29_cast = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1092_cast, y = var_1094_cast)[name = tensor<string, []>("attn_weights_29_cast")];
            tensor<fp16, [2, 10, 1024, 77]> attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_31_cast")];
            tensor<fp16, [2, 10, 1024, 77]> var_1100_cast = softmax(axis = var_633, x = attn_weights_31_cast)[name = tensor<string, []>("op_1100_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, 1024]> attn_15_cast = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1096_cast, y = var_1100_cast)[name = tensor<string, []>("attn_15_cast")];
            tensor<int32, [4]> var_1104 = const()[name = tensor<string, []>("op_1104"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_107_cast = reshape(shape = var_1104, x = attn_15_cast)[name = tensor<string, []>("input_107_cast")];
            tensor<int32, [2]> var_1109 = const()[name = tensor<string, []>("op_1109"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1111 = const()[name = tensor<string, []>("op_1111"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1113_pad_type_0 = const()[name = tensor<string, []>("op_1113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1113_pad_0 = const()[name = tensor<string, []>("op_1113_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, []>(30793088))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31100352))), 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, []>(31100544)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1113_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1111, groups = var_649, pad = var_1113_pad_0, pad_type = var_1113_pad_type_0, strides = var_1109, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_107_cast)[name = tensor<string, []>("op_1113_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_23_cast = add(x = var_1113_cast, y = inputs_21_cast)[name = tensor<string, []>("inputs_23_cast")];
            tensor<int32, [1]> var_1117 = const()[name = tensor<string, []>("op_1117"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_23_cast = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast)[name = tensor<string, []>("channels_mean_23_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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, 1024]> 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_1121 = const()[name = tensor<string, []>("op_1121"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1122_cast = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast)[name = tensor<string, []>("op_1122_cast")];
            tensor<fp16, []> var_1123_to_fp16 = const()[name = tensor<string, []>("op_1123_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_1124_cast = add(x = var_1122_cast, y = var_1123_to_fp16)[name = tensor<string, []>("op_1124_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, 1024]> denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1124_cast)[name = tensor<string, []>("denom_23_cast")];
            tensor<fp16, [2, 640, 1, 1024]> 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_1128_to_fp16 = const()[name = tensor<string, []>("op_1128_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31101888)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1129_cast = add(x = out_23_cast, y = var_1128_to_fp16)[name = tensor<string, []>("op_1129_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_1131_to_fp16 = const()[name = tensor<string, []>("op_1131_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31103232)))];
            tensor<fp16, [2, 640, 1, 1024]> input_109_cast = mul(x = var_1129_cast, y = var_1131_to_fp16)[name = tensor<string, []>("input_109_cast")];
            tensor<int32, [2]> var_1139 = const()[name = tensor<string, []>("op_1139"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1141 = const()[name = tensor<string, []>("op_1141"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1143_pad_type_0 = const()[name = tensor<string, []>("op_1143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1143_pad_0 = const()[name = tensor<string, []>("op_1143_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, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31104576))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33562240))), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3840]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33562432))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33566336))), name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [2, 5120, 1, 1024]> var_1143_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1141, groups = var_649, pad = var_1143_pad_0, pad_type = var_1143_pad_type_0, strides = var_1139, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_109_cast)[name = tensor<string, []>("op_1143_cast")];
            tensor<int32, [2]> var_1144_split_sizes_0 = const()[name = tensor<string, []>("op_1144_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_1144_axis_0 = const()[name = tensor<string, []>("op_1144_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 1024]> var_1144_cast_0, tensor<fp16, [2, 2560, 1, 1024]> var_1144_cast_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast)[name = tensor<string, []>("op_1144_cast")];
            tensor<string, []> var_1146_mode_0 = const()[name = tensor<string, []>("op_1146_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 1024]> var_1146_cast = gelu(mode = var_1146_mode_0, x = var_1144_cast_1)[name = tensor<string, []>("op_1146_cast")];
            tensor<fp16, [2, 2560, 1, 1024]> input_111_cast = mul(x = var_1144_cast_0, y = var_1146_cast)[name = tensor<string, []>("input_111_cast")];
            tensor<int32, [2]> var_1150 = const()[name = tensor<string, []>("op_1150"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1152 = const()[name = tensor<string, []>("op_1152"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1154_pad_type_0 = const()[name = tensor<string, []>("op_1154_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1154_pad_0 = const()[name = tensor<string, []>("op_1154_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33566528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34795392))), 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, []>(34795584)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1154_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1152, groups = var_649, pad = var_1154_pad_0, pad_type = var_1154_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_111_cast)[name = tensor<string, []>("op_1154_cast")];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_69_cast = add(x = var_1154_cast, y = inputs_23_cast)[name = tensor<string, []>("hidden_states_69_cast")];
            tensor<int32, [4]> var_1156 = const()[name = tensor<string, []>("op_1156"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> input_113_cast = reshape(shape = var_1156, x = hidden_states_69_cast)[name = tensor<string, []>("input_113_cast")];
            tensor<int32, [2]> var_1160 = const()[name = tensor<string, []>("op_1160"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1162 = const()[name = tensor<string, []>("op_1162"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_71_pad_type_0 = const()[name = tensor<string, []>("hidden_states_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_71_pad_0 = const()[name = tensor<string, []>("hidden_states_71_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, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34796928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35104192))), 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, []>(35104384)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_71_cast = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1162, groups = var_649, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_1160, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_113_cast)[name = tensor<string, []>("hidden_states_71_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_115_cast = add(x = hidden_states_71_cast, y = hidden_states_59_cast)[name = tensor<string, []>("input_115_cast")];
            tensor<int32, [2]> var_1169 = const()[name = tensor<string, []>("op_1169"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_1171 = const()[name = tensor<string, []>("op_1171"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_117_pad_type_0 = const()[name = tensor<string, []>("input_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_117_pad_0 = const()[name = tensor<string, []>("input_117_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, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35105728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37870592))), 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, []>(37870784)))];
            tensor<fp16, [2, 640, 16, 16]> input_117_cast = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1171, groups = var_649, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1169, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized, x = input_115_cast)[name = tensor<string, []>("input_117_cast")];
            tensor<int32, []> var_1179 = const()[name = tensor<string, []>("op_1179"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_1190 = const()[name = tensor<string, []>("op_1190"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_1195 = const()[name = tensor<string, []>("op_1195"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 16, 16])];
            tensor<fp16, [2, 32, 20, 16, 16]> reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_117_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, 20, 16, 16]> sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor<string, []>("sub_24_cast")];
            tensor<fp16, [2, 32, 20, 16, 16]> 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, []>(0x1.5p-17)];
            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, 20, 16, 16]> 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, 640, 16, 16])];
            tensor<fp16, [2, 640, 16, 16]> reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor<string, []>("reshape_49_cast")];
            tensor<fp16, [640]> add_25_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_25_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37872128)))];
            tensor<fp16, [640]> add_25_beta_0_to_fp16 = const()[name = tensor<string, []>("add_25_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37873472)))];
            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, 640, 16, 16]> 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_49_cast)[name = tensor<string, []>("add_25_cast")];
            tensor<fp16, [2, 640, 16, 16]> input_121_cast = silu(x = add_25_cast)[name = tensor<string, []>("input_121_cast")];
            tensor<int32, [2]> var_1218 = const()[name = tensor<string, []>("op_1218"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1220 = const()[name = tensor<string, []>("op_1220"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_73_pad_type_0 = const()[name = tensor<string, []>("hidden_states_73_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = tensor<string, []>("hidden_states_73_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, []>(37874816))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43404480))), 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, []>(43404672)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_73_cast = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1220, groups = var_1195, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_1218, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_121_cast)[name = tensor<string, []>("hidden_states_73_cast")];
            tensor<int32, [2]> var_1226 = const()[name = tensor<string, []>("op_1226"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1228 = const()[name = tensor<string, []>("op_1228"), 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, []>(43407296))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44636160))), 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, []>(44636352)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_9_cast = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1228, groups = var_1195, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1226, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_9_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_125_cast = add(x = hidden_states_73_cast, y = temb_9_cast)[name = tensor<string, []>("input_125_cast")];
            tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_125_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, 16, 16]> sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor<string, []>("sub_26_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, 16, 16]> 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, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> 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_mean_0_to_fp16 = const()[name = tensor<string, []>("add_27_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44638976)))];
            tensor<fp16, [1280]> add_27_variance_0_to_fp16 = const()[name = tensor<string, []>("add_27_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44641600)))];
            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, []>(44644224)))];
            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, []>(44646848)))];
            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, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast)[name = tensor<string, []>("add_27_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_129_cast = silu(x = add_27_cast)[name = tensor<string, []>("input_129_cast")];
            tensor<int32, [2]> var_1238 = const()[name = tensor<string, []>("op_1238"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1240 = const()[name = tensor<string, []>("op_1240"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_75_pad_type_0 = const()[name = tensor<string, []>("hidden_states_75_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_75_pad_0 = const()[name = tensor<string, []>("hidden_states_75_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, []>(44649472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55708736))), 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, []>(55708928)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_75_cast = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1240, groups = var_1195, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_1238, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_129_cast)[name = tensor<string, []>("hidden_states_75_cast")];
            tensor<int32, [2]> var_1245 = const()[name = tensor<string, []>("op_1245"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1247 = const()[name = tensor<string, []>("op_1247"), 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, [614400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(55711552))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56326016))), 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, []>(56326208)))];
            tensor<fp16, [2, 1280, 16, 16]> x_3_cast = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1247, groups = var_1195, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1245, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_117_cast)[name = tensor<string, []>("x_3_cast")];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_77_cast = add(x = x_3_cast, y = hidden_states_75_cast)[name = tensor<string, []>("hidden_states_77_cast")];
            tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_56_cast = reshape(shape = reshape_56_shape_0, x = hidden_states_77_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, 16, 16]> sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor<string, []>("sub_28_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, []>(0x1.1p-20)];
            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, 16, 16]> 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, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> 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, []>(56328832)))];
            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, []>(56331456)))];
            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, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_57_cast)[name = tensor<string, []>("add_29_cast")];
            tensor<int32, [2]> var_1267 = const()[name = tensor<string, []>("op_1267"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1269 = const()[name = tensor<string, []>("op_1269"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_79_pad_type_0 = const()[name = tensor<string, []>("hidden_states_79_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_79_pad_0 = const()[name = tensor<string, []>("hidden_states_79_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, []>(56334080))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57562944))), 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, []>(57563136)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_79_cast = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1269, groups = var_1195, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1267, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_29_cast)[name = tensor<string, []>("hidden_states_79_cast")];
            tensor<int32, [4]> var_1274 = const()[name = tensor<string, []>("op_1274"), val = tensor<int32, [4]>([2, 1280, 1, 256])];
            tensor<fp16, [2, 1280, 1, 256]> inputs_25_cast = reshape(shape = var_1274, x = hidden_states_79_cast)[name = tensor<string, []>("inputs_25_cast")];
            tensor<int32, [1]> var_1284 = const()[name = tensor<string, []>("op_1284"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_25_cast = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast)[name = tensor<string, []>("channels_mean_25_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_1288 = const()[name = tensor<string, []>("op_1288"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1289_cast = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast)[name = tensor<string, []>("op_1289_cast")];
            tensor<fp16, []> var_1290_to_fp16 = const()[name = tensor<string, []>("op_1290_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_1291_cast = add(x = var_1289_cast, y = var_1290_to_fp16)[name = tensor<string, []>("op_1291_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, 256]> denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1291_cast)[name = tensor<string, []>("denom_25_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_1295_to_fp16 = const()[name = tensor<string, []>("op_1295_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57565760)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1296_cast = add(x = out_25_cast, y = var_1295_to_fp16)[name = tensor<string, []>("op_1296_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1298_to_fp16 = const()[name = tensor<string, []>("op_1298_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57568384)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_81_cast = mul(x = var_1296_cast, y = var_1298_to_fp16)[name = tensor<string, []>("hidden_states_81_cast")];
            tensor<int32, [2]> var_1305 = const()[name = tensor<string, []>("op_1305"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1307 = const()[name = tensor<string, []>("op_1307"), 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57571008))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58799872))), 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, 256]> q_17_cast = conv(dilations = var_1307, groups = var_1195, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1305, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor<string, []>("q_17_cast")];
            tensor<int32, [2]> var_1311 = const()[name = tensor<string, []>("op_1311"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1313 = const()[name = tensor<string, []>("op_1313"), 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58800064))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60028928))), 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, 256]> k_17_cast = conv(dilations = var_1313, groups = var_1195, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1311, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor<string, []>("k_17_cast")];
            tensor<int32, [2]> var_1317 = const()[name = tensor<string, []>("op_1317"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1319 = const()[name = tensor<string, []>("op_1319"), 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60029120))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61257984))), 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, 256]> v_17_cast = conv(dilations = var_1319, groups = var_1195, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1317, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor<string, []>("v_17_cast")];
            tensor<int32, [4]> var_1323 = const()[name = tensor<string, []>("op_1323"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1324_cast = reshape(shape = var_1323, x = q_17_cast)[name = tensor<string, []>("op_1324_cast")];
            tensor<int32, [4]> var_1325 = const()[name = tensor<string, []>("op_1325"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1326_cast = reshape(shape = var_1325, x = k_17_cast)[name = tensor<string, []>("op_1326_cast")];
            tensor<int32, [4]> var_1327 = const()[name = tensor<string, []>("op_1327"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1328_cast = reshape(shape = var_1327, x = v_17_cast)[name = tensor<string, []>("op_1328_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, 256, 256]> attn_weights_33_cast = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1324_cast, y = var_1326_cast)[name = tensor<string, []>("attn_weights_33_cast")];
            tensor<fp16, []> var_1186_to_fp16 = const()[name = tensor<string, []>("op_1186_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 256, 256]> attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_35_cast")];
            tensor<fp16, [2, 20, 256, 256]> var_1332_cast = softmax(axis = var_1179, x = attn_weights_35_cast)[name = tensor<string, []>("op_1332_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, 256]> attn_17_cast = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1328_cast, y = var_1332_cast)[name = tensor<string, []>("attn_17_cast")];
            tensor<int32, [4]> var_1336 = const()[name = tensor<string, []>("op_1336"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_133_cast = reshape(shape = var_1336, x = attn_17_cast)[name = tensor<string, []>("input_133_cast")];
            tensor<int32, [2]> var_1341 = const()[name = tensor<string, []>("op_1341"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1343 = const()[name = tensor<string, []>("op_1343"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1345_pad_type_0 = const()[name = tensor<string, []>("op_1345_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1345_pad_0 = const()[name = tensor<string, []>("op_1345_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, []>(61258176))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62487040))), 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, []>(62487232)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1345_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1343, groups = var_1195, pad = var_1345_pad_0, pad_type = var_1345_pad_type_0, strides = var_1341, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_133_cast)[name = tensor<string, []>("op_1345_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_27_cast = add(x = var_1345_cast, y = inputs_25_cast)[name = tensor<string, []>("inputs_27_cast")];
            tensor<int32, [1]> var_1349 = const()[name = tensor<string, []>("op_1349"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_27_cast = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast)[name = tensor<string, []>("channels_mean_27_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_1353 = const()[name = tensor<string, []>("op_1353"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1354_cast = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast)[name = tensor<string, []>("op_1354_cast")];
            tensor<fp16, []> var_1355_to_fp16 = const()[name = tensor<string, []>("op_1355_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_1356_cast = add(x = var_1354_cast, y = var_1355_to_fp16)[name = tensor<string, []>("op_1356_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, 256]> denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1356_cast)[name = tensor<string, []>("denom_27_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_1360_to_fp16 = const()[name = tensor<string, []>("op_1360_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62489856)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1361_cast = add(x = out_27_cast, y = var_1360_to_fp16)[name = tensor<string, []>("op_1361_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1363_to_fp16 = const()[name = tensor<string, []>("op_1363_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62492480)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_83_cast = mul(x = var_1361_cast, y = var_1363_to_fp16)[name = tensor<string, []>("hidden_states_83_cast")];
            tensor<int32, [2]> var_1370 = const()[name = tensor<string, []>("op_1370"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1372 = const()[name = tensor<string, []>("op_1372"), 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62495104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63723968))), 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, 256]> q_19_cast = conv(dilations = var_1372, groups = var_1195, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1370, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_83_cast)[name = tensor<string, []>("q_19_cast")];
            tensor<int32, [2]> var_1376 = const()[name = tensor<string, []>("op_1376"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1378 = const()[name = tensor<string, []>("op_1378"), 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, 1024, 1, 1]> down_blocks_2_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, []>(63724160))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64707264))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_19_cast = conv(dilations = var_1378, groups = var_1195, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1376, 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_1382 = const()[name = tensor<string, []>("op_1382"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1384 = const()[name = tensor<string, []>("op_1384"), 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, 1024, 1, 1]> down_blocks_2_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, []>(64707456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65690560))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_19_cast = conv(dilations = var_1384, groups = var_1195, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1382, 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_1388 = const()[name = tensor<string, []>("op_1388"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1389_cast = reshape(shape = var_1388, x = q_19_cast)[name = tensor<string, []>("op_1389_cast")];
            tensor<int32, [4]> var_1390 = const()[name = tensor<string, []>("op_1390"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1391_cast = reshape(shape = var_1390, x = k_19_cast)[name = tensor<string, []>("op_1391_cast")];
            tensor<int32, [4]> var_1392 = const()[name = tensor<string, []>("op_1392"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1393_cast = reshape(shape = var_1392, x = v_19_cast)[name = tensor<string, []>("op_1393_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, 256, 77]> attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1389_cast, y = var_1391_cast)[name = tensor<string, []>("attn_weights_37_cast")];
            tensor<fp16, [2, 20, 256, 77]> attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_39_cast")];
            tensor<fp16, [2, 20, 256, 77]> var_1397_cast = softmax(axis = var_1179, x = attn_weights_39_cast)[name = tensor<string, []>("op_1397_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, 256]> attn_19_cast = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1393_cast, y = var_1397_cast)[name = tensor<string, []>("attn_19_cast")];
            tensor<int32, [4]> var_1401 = const()[name = tensor<string, []>("op_1401"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_135_cast = reshape(shape = var_1401, x = attn_19_cast)[name = tensor<string, []>("input_135_cast")];
            tensor<int32, [2]> var_1406 = const()[name = tensor<string, []>("op_1406"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1408 = const()[name = tensor<string, []>("op_1408"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1410_pad_type_0 = const()[name = tensor<string, []>("op_1410_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1410_pad_0 = const()[name = tensor<string, []>("op_1410_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65690752))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66919616))), 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, []>(66919808)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1410_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1408, groups = var_1195, pad = var_1410_pad_0, pad_type = var_1410_pad_type_0, strides = var_1406, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_135_cast)[name = tensor<string, []>("op_1410_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_29_cast = add(x = var_1410_cast, y = inputs_27_cast)[name = tensor<string, []>("inputs_29_cast")];
            tensor<int32, [1]> var_1414 = const()[name = tensor<string, []>("op_1414"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_29_cast = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast)[name = tensor<string, []>("channels_mean_29_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_1418 = const()[name = tensor<string, []>("op_1418"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1419_cast = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast)[name = tensor<string, []>("op_1419_cast")];
            tensor<fp16, []> var_1420_to_fp16 = const()[name = tensor<string, []>("op_1420_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_1421_cast = add(x = var_1419_cast, y = var_1420_to_fp16)[name = tensor<string, []>("op_1421_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, 256]> denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1421_cast)[name = tensor<string, []>("denom_29_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_1425_to_fp16 = const()[name = tensor<string, []>("op_1425_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66922432)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1426_cast = add(x = out_29_cast, y = var_1425_to_fp16)[name = tensor<string, []>("op_1426_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1428_to_fp16 = const()[name = tensor<string, []>("op_1428_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66925056)))];
            tensor<fp16, [2, 1280, 1, 256]> input_137_cast = mul(x = var_1426_cast, y = var_1428_to_fp16)[name = tensor<string, []>("input_137_cast")];
            tensor<int32, [2]> var_1436 = const()[name = tensor<string, []>("op_1436"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1438 = const()[name = tensor<string, []>("op_1438"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1440_pad_type_0 = const()[name = tensor<string, []>("op_1440_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1440_pad_0 = const()[name = tensor<string, []>("op_1440_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, []>(66927680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76758144))), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76758336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76766080))), name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([10240])];
            tensor<fp16, [2, 10240, 1, 256]> var_1440_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1438, groups = var_1195, pad = var_1440_pad_0, pad_type = var_1440_pad_type_0, strides = var_1436, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_137_cast)[name = tensor<string, []>("op_1440_cast")];
            tensor<int32, [2]> var_1441_split_sizes_0 = const()[name = tensor<string, []>("op_1441_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1441_axis_0 = const()[name = tensor<string, []>("op_1441_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 256]> var_1441_cast_0, tensor<fp16, [2, 5120, 1, 256]> var_1441_cast_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast)[name = tensor<string, []>("op_1441_cast")];
            tensor<string, []> var_1443_mode_0 = const()[name = tensor<string, []>("op_1443_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 256]> var_1443_cast = gelu(mode = var_1443_mode_0, x = var_1441_cast_1)[name = tensor<string, []>("op_1443_cast")];
            tensor<fp16, [2, 5120, 1, 256]> input_139_cast = mul(x = var_1441_cast_0, y = var_1443_cast)[name = tensor<string, []>("input_139_cast")];
            tensor<int32, [2]> var_1447 = const()[name = tensor<string, []>("op_1447"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1449 = const()[name = tensor<string, []>("op_1449"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1451_pad_type_0 = const()[name = tensor<string, []>("op_1451_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1451_pad_0 = const()[name = tensor<string, []>("op_1451_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, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76766272))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81681536))), 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, []>(81681728)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1451_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1449, groups = var_1195, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1447, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_139_cast)[name = tensor<string, []>("op_1451_cast")];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_87_cast = add(x = var_1451_cast, y = inputs_29_cast)[name = tensor<string, []>("hidden_states_87_cast")];
            tensor<int32, [4]> var_1453 = const()[name = tensor<string, []>("op_1453"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> input_141_cast = reshape(shape = var_1453, x = hidden_states_87_cast)[name = tensor<string, []>("input_141_cast")];
            tensor<int32, [2]> var_1457 = const()[name = tensor<string, []>("op_1457"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1459 = const()[name = tensor<string, []>("op_1459"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_89_pad_type_0 = const()[name = tensor<string, []>("hidden_states_89_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_89_pad_0 = const()[name = tensor<string, []>("hidden_states_89_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, []>(81684352))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82913216))), 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, []>(82913408)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_89_cast = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_1459, groups = var_1195, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_1457, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_141_cast)[name = tensor<string, []>("hidden_states_89_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_143_cast = add(x = hidden_states_89_cast, y = hidden_states_77_cast)[name = tensor<string, []>("input_143_cast")];
            tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_143_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, 16, 16]> sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor<string, []>("sub_30_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, []>(0x1.5p-17)];
            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, 16, 16]> 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, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> 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, []>(82916032)))];
            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, []>(82918656)))];
            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, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_61_cast)[name = tensor<string, []>("add_31_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_147_cast = silu(x = add_31_cast)[name = tensor<string, []>("input_147_cast")];
            tensor<int32, [2]> var_1474 = const()[name = tensor<string, []>("op_1474"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1476 = const()[name = tensor<string, []>("op_1476"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_91_pad_type_0 = const()[name = tensor<string, []>("hidden_states_91_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_91_pad_0 = const()[name = tensor<string, []>("hidden_states_91_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, []>(82921280))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93980544))), 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, []>(93980736)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_91_cast = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_1476, groups = var_1195, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_1474, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_147_cast)[name = tensor<string, []>("hidden_states_91_cast")];
            tensor<int32, [2]> var_1482 = const()[name = tensor<string, []>("op_1482"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1484 = const()[name = tensor<string, []>("op_1484"), 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93983360))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95212224))), 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, []>(95212416)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_11_cast = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1484, groups = var_1195, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_1482, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_11_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_151_cast = add(x = hidden_states_91_cast, y = temb_11_cast)[name = tensor<string, []>("input_151_cast")];
            tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_151_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, 16, 16]> sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor<string, []>("sub_32_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, 16, 16]> 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, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> 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, []>(95215040)))];
            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, []>(95217664)))];
            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, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_65_cast)[name = tensor<string, []>("add_33_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_155_cast = silu(x = add_33_cast)[name = tensor<string, []>("input_155_cast")];
            tensor<int32, [2]> var_1494 = const()[name = tensor<string, []>("op_1494"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1496 = const()[name = tensor<string, []>("op_1496"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_93_pad_type_0 = const()[name = tensor<string, []>("hidden_states_93_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = tensor<string, []>("hidden_states_93_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, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95220288))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(106279552))), 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, []>(106279744)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_93_cast = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_1496, groups = var_1195, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_1494, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_155_cast)[name = tensor<string, []>("hidden_states_93_cast")];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_95_cast = add(x = input_143_cast, y = hidden_states_93_cast)[name = tensor<string, []>("hidden_states_95_cast")];
            tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_68_cast = reshape(shape = reshape_68_shape_0, x = hidden_states_95_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, 16, 16]> sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor<string, []>("sub_34_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, []>(0x1.1p-20)];
            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, 16, 16]> 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, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> 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, []>(106282368)))];
            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, []>(106284992)))];
            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, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_69_cast)[name = tensor<string, []>("add_35_cast")];
            tensor<int32, [2]> var_1516 = const()[name = tensor<string, []>("op_1516"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1518 = const()[name = tensor<string, []>("op_1518"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_97_pad_type_0 = const()[name = tensor<string, []>("hidden_states_97_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = tensor<string, []>("hidden_states_97_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(106287616))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107516480))), 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, []>(107516672)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_97_cast = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_1518, groups = var_1195, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_1516, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_35_cast)[name = tensor<string, []>("hidden_states_97_cast")];
            tensor<int32, [4]> var_1523 = const()[name = tensor<string, []>("op_1523"), val = tensor<int32, [4]>([2, 1280, 1, 256])];
            tensor<fp16, [2, 1280, 1, 256]> inputs_31_cast = reshape(shape = var_1523, x = hidden_states_97_cast)[name = tensor<string, []>("inputs_31_cast")];
            tensor<int32, [1]> var_1533 = const()[name = tensor<string, []>("op_1533"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_31_cast = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast)[name = tensor<string, []>("channels_mean_31_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_1537 = const()[name = tensor<string, []>("op_1537"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1538_cast = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_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, 256]> var_1540_cast = add(x = var_1538_cast, y = var_1539_to_fp16)[name = tensor<string, []>("op_1540_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, 256]> denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1540_cast)[name = tensor<string, []>("denom_31_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_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, []>(107519296)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1545_cast = add(x = out_31_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, []>(107521920)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_99_cast = mul(x = var_1545_cast, y = var_1547_to_fp16)[name = tensor<string, []>("hidden_states_99_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_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_1_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, []>(107524544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108753408))), 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, 256]> q_21_cast = conv(dilations = var_1556, groups = var_1195, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor<string, []>("q_21_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_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_1_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, []>(108753600))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109982464))), 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, 256]> k_21_cast = conv(dilations = var_1562, groups = var_1195, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor<string, []>("k_21_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_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_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, []>(109982656))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111211520))), 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, 256]> v_21_cast = conv(dilations = var_1568, groups = var_1195, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor<string, []>("v_21_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, 256]> var_1573_cast = reshape(shape = var_1572, x = q_21_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, 256]> var_1575_cast = reshape(shape = var_1574, x = k_21_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, 256]> var_1577_cast = reshape(shape = var_1576, x = v_21_cast)[name = tensor<string, []>("op_1577_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, 256, 256]> attn_weights_41_cast = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1573_cast, y = var_1575_cast)[name = tensor<string, []>("attn_weights_41_cast")];
            tensor<fp16, [2, 20, 256, 256]> attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_43_cast")];
            tensor<fp16, [2, 20, 256, 256]> var_1581_cast = softmax(axis = var_1179, x = attn_weights_43_cast)[name = tensor<string, []>("op_1581_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, 256]> attn_21_cast = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1577_cast, y = var_1581_cast)[name = tensor<string, []>("attn_21_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, 256]> input_159_cast = reshape(shape = var_1585, x = attn_21_cast)[name = tensor<string, []>("input_159_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_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, []>(111211712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112440576))), 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, []>(112440768)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1594_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1195, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_159_cast)[name = tensor<string, []>("op_1594_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_33_cast = add(x = var_1594_cast, y = inputs_31_cast)[name = tensor<string, []>("inputs_33_cast")];
            tensor<int32, [1]> var_1598 = const()[name = tensor<string, []>("op_1598"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_33_cast = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast)[name = tensor<string, []>("channels_mean_33_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_1602 = const()[name = tensor<string, []>("op_1602"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1603_cast = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_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, 256]> var_1605_cast = add(x = var_1603_cast, y = var_1604_to_fp16)[name = tensor<string, []>("op_1605_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, 256]> denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1605_cast)[name = tensor<string, []>("denom_33_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_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, []>(112443392)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1610_cast = add(x = out_33_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, []>(112446016)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_101_cast = mul(x = var_1610_cast, y = var_1612_to_fp16)[name = tensor<string, []>("hidden_states_101_cast")];
            tensor<int32, [2]> var_1619 = const()[name = tensor<string, []>("op_1619"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1621 = const()[name = tensor<string, []>("op_1621"), 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_1_transformer_blocks_0_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, []>(112448640))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113677504))), 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, 256]> q_23_cast = conv(dilations = var_1621, groups = var_1195, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1619, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_101_cast)[name = tensor<string, []>("q_23_cast")];
            tensor<int32, [2]> var_1625 = const()[name = tensor<string, []>("op_1625"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1627 = const()[name = tensor<string, []>("op_1627"), 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, 1024, 1, 1]> down_blocks_2_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, []>(113677696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114660800))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_23_cast = conv(dilations = var_1627, groups = var_1195, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1625, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_23_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, []> 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, 1024, 1, 1]> down_blocks_2_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, []>(114660992))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115644096))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_23_cast = conv(dilations = var_1633, groups = var_1195, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_23_cast")];
            tensor<int32, [4]> var_1637 = const()[name = tensor<string, []>("op_1637"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1638_cast = reshape(shape = var_1637, x = q_23_cast)[name = tensor<string, []>("op_1638_cast")];
            tensor<int32, [4]> var_1639 = const()[name = tensor<string, []>("op_1639"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1640_cast = reshape(shape = var_1639, x = k_23_cast)[name = tensor<string, []>("op_1640_cast")];
            tensor<int32, [4]> var_1641 = const()[name = tensor<string, []>("op_1641"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1642_cast = reshape(shape = var_1641, x = v_23_cast)[name = tensor<string, []>("op_1642_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, 256, 77]> attn_weights_45_cast = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1638_cast, y = var_1640_cast)[name = tensor<string, []>("attn_weights_45_cast")];
            tensor<fp16, [2, 20, 256, 77]> attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_47_cast")];
            tensor<fp16, [2, 20, 256, 77]> var_1646_cast = softmax(axis = var_1179, x = attn_weights_47_cast)[name = tensor<string, []>("op_1646_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, 256]> attn_23_cast = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1642_cast, y = var_1646_cast)[name = tensor<string, []>("attn_23_cast")];
            tensor<int32, [4]> var_1650 = const()[name = tensor<string, []>("op_1650"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_161_cast = reshape(shape = var_1650, x = attn_23_cast)[name = tensor<string, []>("input_161_cast")];
            tensor<int32, [2]> var_1655 = const()[name = tensor<string, []>("op_1655"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1657 = const()[name = tensor<string, []>("op_1657"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1659_pad_type_0 = const()[name = tensor<string, []>("op_1659_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1659_pad_0 = const()[name = tensor<string, []>("op_1659_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115644288))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116873152))), 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, []>(116873344)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1659_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1657, groups = var_1195, pad = var_1659_pad_0, pad_type = var_1659_pad_type_0, strides = var_1655, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_161_cast)[name = tensor<string, []>("op_1659_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_35_cast = add(x = var_1659_cast, y = inputs_33_cast)[name = tensor<string, []>("inputs_35_cast")];
            tensor<int32, [1]> var_1663 = const()[name = tensor<string, []>("op_1663"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_35_cast = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast)[name = tensor<string, []>("channels_mean_35_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_1667 = const()[name = tensor<string, []>("op_1667"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1668_cast = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast)[name = tensor<string, []>("op_1668_cast")];
            tensor<fp16, []> var_1669_to_fp16 = const()[name = tensor<string, []>("op_1669_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_1670_cast = add(x = var_1668_cast, y = var_1669_to_fp16)[name = tensor<string, []>("op_1670_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, 256]> denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1670_cast)[name = tensor<string, []>("denom_35_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_1674_to_fp16 = const()[name = tensor<string, []>("op_1674_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116875968)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1675_cast = add(x = out_35_cast, y = var_1674_to_fp16)[name = tensor<string, []>("op_1675_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1677_to_fp16 = const()[name = tensor<string, []>("op_1677_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116878592)))];
            tensor<fp16, [2, 1280, 1, 256]> input_163_cast = mul(x = var_1675_cast, y = var_1677_to_fp16)[name = tensor<string, []>("input_163_cast")];
            tensor<int32, [2]> var_1685 = const()[name = tensor<string, []>("op_1685"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1687 = const()[name = tensor<string, []>("op_1687"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1689_pad_type_0 = const()[name = tensor<string, []>("op_1689_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1689_pad_0 = const()[name = tensor<string, []>("op_1689_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, []>(116881216))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126711680))), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126711872))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126719616))), name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([10240])];
            tensor<fp16, [2, 10240, 1, 256]> var_1689_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1687, groups = var_1195, pad = var_1689_pad_0, pad_type = var_1689_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_163_cast)[name = tensor<string, []>("op_1689_cast")];
            tensor<int32, [2]> var_1690_split_sizes_0 = const()[name = tensor<string, []>("op_1690_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1690_axis_0 = const()[name = tensor<string, []>("op_1690_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 256]> var_1690_cast_0, tensor<fp16, [2, 5120, 1, 256]> var_1690_cast_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast)[name = tensor<string, []>("op_1690_cast")];
            tensor<string, []> var_1692_mode_0 = const()[name = tensor<string, []>("op_1692_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 256]> var_1692_cast = gelu(mode = var_1692_mode_0, x = var_1690_cast_1)[name = tensor<string, []>("op_1692_cast")];
            tensor<fp16, [2, 5120, 1, 256]> input_165_cast = mul(x = var_1690_cast_0, y = var_1692_cast)[name = tensor<string, []>("input_165_cast")];
            tensor<int32, [2]> var_1696 = const()[name = tensor<string, []>("op_1696"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1698 = const()[name = tensor<string, []>("op_1698"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1700_pad_type_0 = const()[name = tensor<string, []>("op_1700_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1700_pad_0 = const()[name = tensor<string, []>("op_1700_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, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126719808))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131635072))), 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, []>(131635264)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1700_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1698, groups = var_1195, pad = var_1700_pad_0, pad_type = var_1700_pad_type_0, strides = var_1696, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_165_cast)[name = tensor<string, []>("op_1700_cast")];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_105_cast = add(x = var_1700_cast, y = inputs_35_cast)[name = tensor<string, []>("hidden_states_105_cast")];
            tensor<int32, [4]> var_1702 = const()[name = tensor<string, []>("op_1702"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> input_167_cast = reshape(shape = var_1702, x = hidden_states_105_cast)[name = tensor<string, []>("input_167_cast")];
            tensor<int32, [2]> var_1706 = const()[name = tensor<string, []>("op_1706"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1708 = const()[name = tensor<string, []>("op_1708"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_107_pad_type_0 = const()[name = tensor<string, []>("hidden_states_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = tensor<string, []>("hidden_states_107_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, []>(131637888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132866752))), 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, []>(132866944)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_107_cast = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_1708, groups = var_1195, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_1706, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_167_cast)[name = tensor<string, []>("hidden_states_107_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_169_cast = add(x = hidden_states_107_cast, y = hidden_states_95_cast)[name = tensor<string, []>("input_169_cast")];
            tensor<int32, [2]> var_1715 = const()[name = tensor<string, []>("op_1715"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_1717 = const()[name = tensor<string, []>("op_1717"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_171_pad_type_0 = const()[name = tensor<string, []>("input_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_171_pad_0 = const()[name = tensor<string, []>("input_171_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132869568))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143928832))), name = tensor<string, []>("down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143929024)))];
            tensor<fp16, [2, 1280, 8, 8]> input_171_cast = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_1717, groups = var_1195, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1715, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized, x = input_169_cast)[name = tensor<string, []>("input_171_cast")];
            tensor<int32, []> var_1729 = const()[name = tensor<string, []>("op_1729"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_72_cast = reshape(shape = reshape_72_shape_0, x = input_171_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, 8, 8]> sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor<string, []>("sub_36_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, []>(0x1.5p-17)];
            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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(143931648)))];
            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, []>(143934272)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_73_cast)[name = tensor<string, []>("add_37_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_175_cast = silu(x = add_37_cast)[name = tensor<string, []>("input_175_cast")];
            tensor<int32, [2]> var_1745 = const()[name = tensor<string, []>("op_1745"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1747 = const()[name = tensor<string, []>("op_1747"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_109_pad_type_0 = const()[name = tensor<string, []>("hidden_states_109_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_109_pad_0 = const()[name = tensor<string, []>("hidden_states_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_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, []>(143936896))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154996160))), name = tensor<string, []>("down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154996352)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_109_cast = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_1747, groups = var_1729, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_1745, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_175_cast)[name = tensor<string, []>("hidden_states_109_cast")];
            tensor<int32, [2]> var_1753 = const()[name = tensor<string, []>("op_1753"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1755 = const()[name = tensor<string, []>("op_1755"), 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]> down_blocks_3_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, []>(154998976))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156227840))), name = tensor<string, []>("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156228032)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_13_cast = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1755, groups = var_1729, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_1753, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_13_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_179_cast = add(x = hidden_states_109_cast, y = temb_13_cast)[name = tensor<string, []>("input_179_cast")];
            tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_179_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, 8, 8]> sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor<string, []>("sub_38_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(156230656)))];
            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, []>(156233280)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_77_cast)[name = tensor<string, []>("add_39_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_183_cast = silu(x = add_39_cast)[name = tensor<string, []>("input_183_cast")];
            tensor<int32, [2]> var_1765 = const()[name = tensor<string, []>("op_1765"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1767 = const()[name = tensor<string, []>("op_1767"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_111_pad_type_0 = const()[name = tensor<string, []>("hidden_states_111_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_111_pad_0 = const()[name = tensor<string, []>("hidden_states_111_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_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, []>(156235904))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167295168))), name = tensor<string, []>("down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167295360)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_111_cast = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_1767, groups = var_1729, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_1765, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_183_cast)[name = tensor<string, []>("hidden_states_111_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_185_cast = add(x = input_171_cast, y = hidden_states_111_cast)[name = tensor<string, []>("input_185_cast")];
            tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_185_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, 8, 8]> sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor<string, []>("sub_40_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(167297984)))];
            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, []>(167300608)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_81_cast)[name = tensor<string, []>("add_41_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_189_cast = silu(x = add_41_cast)[name = tensor<string, []>("input_189_cast")];
            tensor<int32, [2]> var_1782 = const()[name = tensor<string, []>("op_1782"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1784 = const()[name = tensor<string, []>("op_1784"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_113_pad_type_0 = const()[name = tensor<string, []>("hidden_states_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = tensor<string, []>("hidden_states_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_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, []>(167303232))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178362496))), name = tensor<string, []>("down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178362688)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_113_cast = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_1784, groups = var_1729, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_1782, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_189_cast)[name = tensor<string, []>("hidden_states_113_cast")];
            tensor<int32, [2]> var_1790 = const()[name = tensor<string, []>("op_1790"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1792 = const()[name = tensor<string, []>("op_1792"), 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]> down_blocks_3_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, []>(178365312))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179594176))), name = tensor<string, []>("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179594368)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_15_cast = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1792, groups = var_1729, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_1790, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_15_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_193_cast = add(x = hidden_states_113_cast, y = temb_15_cast)[name = tensor<string, []>("input_193_cast")];
            tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_193_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, 40, 8, 8]> sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor<string, []>("sub_42_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, 40, 8, 8]> 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, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor<string, []>("reshape_85_cast")];
            tensor<fp16, [1280]> add_43_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_43_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179596992)))];
            tensor<fp16, [1280]> add_43_beta_0_to_fp16 = const()[name = tensor<string, []>("add_43_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179599616)))];
            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, 1280, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_85_cast)[name = tensor<string, []>("add_43_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_197_cast = silu(x = add_43_cast)[name = tensor<string, []>("input_197_cast")];
            tensor<int32, [2]> var_1802 = const()[name = tensor<string, []>("op_1802"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1804 = const()[name = tensor<string, []>("op_1804"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_115_pad_type_0 = const()[name = tensor<string, []>("hidden_states_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_115_pad_0 = const()[name = tensor<string, []>("hidden_states_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_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, []>(179602240))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190661504))), name = tensor<string, []>("down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190661696)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_115_cast = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_1804, groups = var_1729, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_1802, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_197_cast)[name = tensor<string, []>("hidden_states_115_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_199_cast = add(x = input_185_cast, y = hidden_states_115_cast)[name = tensor<string, []>("input_199_cast")];
            tensor<int32, []> var_1812 = const()[name = tensor<string, []>("op_1812"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_1823 = const()[name = tensor<string, []>("op_1823"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_1828 = const()[name = tensor<string, []>("op_1828"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_88_shape_0 = const()[name = tensor<string, []>("reshape_88_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_199_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, 8, 8]> sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor<string, []>("sub_44_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(190664320)))];
            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, []>(190666944)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_89_cast)[name = tensor<string, []>("add_45_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_203_cast = silu(x = add_45_cast)[name = tensor<string, []>("input_203_cast")];
            tensor<int32, [2]> var_1846 = const()[name = tensor<string, []>("op_1846"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1848 = const()[name = tensor<string, []>("op_1848"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_117_pad_type_0 = const()[name = tensor<string, []>("hidden_states_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = tensor<string, []>("hidden_states_117_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, []>(190669568))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201728832))), 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, []>(201729024)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_117_cast = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_1848, groups = var_1828, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_1846, weight = mid_block_resnets_0_conv1_weight_to_fp16_palettized, x = input_203_cast)[name = tensor<string, []>("hidden_states_117_cast")];
            tensor<int32, [2]> var_1854 = const()[name = tensor<string, []>("op_1854"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1856 = const()[name = tensor<string, []>("op_1856"), 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]> mid_block_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, []>(201731648))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202960512))), 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, []>(202960704)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_17_cast = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1856, groups = var_1828, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_1854, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_17_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_207_cast = add(x = hidden_states_117_cast, y = temb_17_cast)[name = tensor<string, []>("input_207_cast")];
            tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_92_cast = reshape(shape = reshape_92_shape_0, x = input_207_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, 8, 8]> sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor<string, []>("sub_46_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, []>(0x1.5p-17)];
            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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(202963328)))];
            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, []>(202965952)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_93_cast)[name = tensor<string, []>("add_47_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_211_cast = silu(x = add_47_cast)[name = tensor<string, []>("input_211_cast")];
            tensor<int32, [2]> var_1866 = const()[name = tensor<string, []>("op_1866"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1868 = const()[name = tensor<string, []>("op_1868"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_119_pad_type_0 = const()[name = tensor<string, []>("hidden_states_119_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_119_pad_0 = const()[name = tensor<string, []>("hidden_states_119_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, []>(202968576))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214027840))), 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, []>(214028032)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_119_cast = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_1868, groups = var_1828, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_1866, weight = mid_block_resnets_0_conv2_weight_to_fp16_palettized, x = input_211_cast)[name = tensor<string, []>("hidden_states_119_cast")];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_121_cast = add(x = input_199_cast, y = hidden_states_119_cast)[name = tensor<string, []>("hidden_states_121_cast")];
            tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_96_cast = reshape(shape = reshape_96_shape_0, x = hidden_states_121_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, 40, 8, 8]> sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor<string, []>("sub_48_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, []>(0x1.1p-20)];
            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, 40, 8, 8]> 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, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor<string, []>("reshape_97_cast")];
            tensor<fp16, [1280]> add_49_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_49_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214030656)))];
            tensor<fp16, [1280]> add_49_beta_0_to_fp16 = const()[name = tensor<string, []>("add_49_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214033280)))];
            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, 1280, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_97_cast)[name = tensor<string, []>("add_49_cast")];
            tensor<int32, [2]> var_1888 = const()[name = tensor<string, []>("op_1888"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1890 = const()[name = tensor<string, []>("op_1890"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_123_pad_type_0 = const()[name = tensor<string, []>("hidden_states_123_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = tensor<string, []>("hidden_states_123_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, []>(214035904))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215264768))), 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, []>(215264960)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_123_cast = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_1890, groups = var_1828, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_1888, weight = mid_block_attentions_0_proj_in_weight_to_fp16_palettized, x = add_49_cast)[name = tensor<string, []>("hidden_states_123_cast")];
            tensor<int32, [4]> var_1895 = const()[name = tensor<string, []>("op_1895"), val = tensor<int32, [4]>([2, 1280, 1, 64])];
            tensor<fp16, [2, 1280, 1, 64]> inputs_37_cast = reshape(shape = var_1895, x = hidden_states_123_cast)[name = tensor<string, []>("inputs_37_cast")];
            tensor<int32, [1]> var_1905 = const()[name = tensor<string, []>("op_1905"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> channels_mean_37_cast = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast)[name = tensor<string, []>("channels_mean_37_cast")];
            tensor<fp16, [2, 1280, 1, 64]> 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, 64]> 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_1909 = const()[name = tensor<string, []>("op_1909"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> var_1910_cast = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast)[name = tensor<string, []>("op_1910_cast")];
            tensor<fp16, []> var_1911_to_fp16 = const()[name = tensor<string, []>("op_1911_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 64]> var_1912_cast = add(x = var_1910_cast, y = var_1911_to_fp16)[name = tensor<string, []>("op_1912_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, 64]> denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1912_cast)[name = tensor<string, []>("denom_37_cast")];
            tensor<fp16, [2, 1280, 1, 64]> 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_1916_to_fp16 = const()[name = tensor<string, []>("op_1916_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215267584)))];
            tensor<fp16, [2, 1280, 1, 64]> var_1917_cast = add(x = out_37_cast, y = var_1916_to_fp16)[name = tensor<string, []>("op_1917_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1919_to_fp16 = const()[name = tensor<string, []>("op_1919_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215270208)))];
            tensor<fp16, [2, 1280, 1, 64]> hidden_states_125_cast = mul(x = var_1917_cast, y = var_1919_to_fp16)[name = tensor<string, []>("hidden_states_125_cast")];
            tensor<int32, [2]> var_1926 = const()[name = tensor<string, []>("op_1926"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1928 = const()[name = tensor<string, []>("op_1928"), 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]> 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, []>(215272832))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216501696))), 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, 64]> q_25_cast = conv(dilations = var_1928, groups = var_1828, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1926, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor<string, []>("q_25_cast")];
            tensor<int32, [2]> var_1932 = const()[name = tensor<string, []>("op_1932"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1934 = const()[name = tensor<string, []>("op_1934"), 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]> 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, []>(216501888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217730752))), 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, 64]> k_25_cast = conv(dilations = var_1934, groups = var_1828, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1932, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor<string, []>("k_25_cast")];
            tensor<int32, [2]> var_1938 = const()[name = tensor<string, []>("op_1938"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1940 = const()[name = tensor<string, []>("op_1940"), 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]> 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, []>(217730944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218959808))), 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, 64]> v_25_cast = conv(dilations = var_1940, groups = var_1828, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1938, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor<string, []>("v_25_cast")];
            tensor<int32, [4]> var_1944 = const()[name = tensor<string, []>("op_1944"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_1945_cast = reshape(shape = var_1944, x = q_25_cast)[name = tensor<string, []>("op_1945_cast")];
            tensor<int32, [4]> var_1946 = const()[name = tensor<string, []>("op_1946"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_1947_cast = reshape(shape = var_1946, x = k_25_cast)[name = tensor<string, []>("op_1947_cast")];
            tensor<int32, [4]> var_1948 = const()[name = tensor<string, []>("op_1948"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_1949_cast = reshape(shape = var_1948, x = v_25_cast)[name = tensor<string, []>("op_1949_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, 64, 64]> attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1945_cast, y = var_1947_cast)[name = tensor<string, []>("attn_weights_49_cast")];
            tensor<fp16, []> var_1819_to_fp16 = const()[name = tensor<string, []>("op_1819_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 64, 64]> attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_1819_to_fp16)[name = tensor<string, []>("attn_weights_51_cast")];
            tensor<fp16, [2, 20, 64, 64]> var_1953_cast = softmax(axis = var_1812, x = attn_weights_51_cast)[name = tensor<string, []>("op_1953_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, 64]> attn_25_cast = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1949_cast, y = var_1953_cast)[name = tensor<string, []>("attn_25_cast")];
            tensor<int32, [4]> var_1957 = const()[name = tensor<string, []>("op_1957"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 64]> input_215_cast = reshape(shape = var_1957, x = attn_25_cast)[name = tensor<string, []>("input_215_cast")];
            tensor<int32, [2]> var_1962 = const()[name = tensor<string, []>("op_1962"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1964 = const()[name = tensor<string, []>("op_1964"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1966_pad_type_0 = const()[name = tensor<string, []>("op_1966_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1966_pad_0 = const()[name = tensor<string, []>("op_1966_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218960000))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220188864))), 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, []>(220189056)))];
            tensor<fp16, [2, 1280, 1, 64]> var_1966_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1964, groups = var_1828, pad = var_1966_pad_0, pad_type = var_1966_pad_type_0, strides = var_1962, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_215_cast)[name = tensor<string, []>("op_1966_cast")];
            tensor<fp16, [2, 1280, 1, 64]> inputs_39_cast = add(x = var_1966_cast, y = inputs_37_cast)[name = tensor<string, []>("inputs_39_cast")];
            tensor<int32, [1]> var_1970 = const()[name = tensor<string, []>("op_1970"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> channels_mean_39_cast = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast)[name = tensor<string, []>("channels_mean_39_cast")];
            tensor<fp16, [2, 1280, 1, 64]> 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, 64]> 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_1974 = const()[name = tensor<string, []>("op_1974"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> var_1975_cast = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast)[name = tensor<string, []>("op_1975_cast")];
            tensor<fp16, []> var_1976_to_fp16 = const()[name = tensor<string, []>("op_1976_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 64]> var_1977_cast = add(x = var_1975_cast, y = var_1976_to_fp16)[name = tensor<string, []>("op_1977_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, 64]> denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1977_cast)[name = tensor<string, []>("denom_39_cast")];
            tensor<fp16, [2, 1280, 1, 64]> 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_1981_to_fp16 = const()[name = tensor<string, []>("op_1981_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220191680)))];
            tensor<fp16, [2, 1280, 1, 64]> var_1982_cast = add(x = out_39_cast, y = var_1981_to_fp16)[name = tensor<string, []>("op_1982_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_1984_to_fp16 = const()[name = tensor<string, []>("op_1984_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220194304)))];
            tensor<fp16, [2, 1280, 1, 64]> hidden_states_127_cast = mul(x = var_1982_cast, y = var_1984_to_fp16)[name = tensor<string, []>("hidden_states_127_cast")];
            tensor<int32, [2]> var_1991 = const()[name = tensor<string, []>("op_1991"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1993 = const()[name = tensor<string, []>("op_1993"), 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]> mid_block_attentions_0_transformer_blocks_0_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, []>(220196928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(221425792))), 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, 64]> q_27_cast = conv(dilations = var_1993, groups = var_1828, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1991, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("q_27_cast")];
            tensor<int32, [2]> var_1997 = const()[name = tensor<string, []>("op_1997"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1999 = const()[name = tensor<string, []>("op_1999"), 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, 1024, 1, 1]> mid_block_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, []>(221425984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222409088))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_27_cast = conv(dilations = var_1999, groups = var_1828, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1997, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_27_cast")];
            tensor<int32, [2]> var_2003 = const()[name = tensor<string, []>("op_2003"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2005 = const()[name = tensor<string, []>("op_2005"), 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, 1024, 1, 1]> mid_block_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, []>(222409280))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(223392384))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_27_cast = conv(dilations = var_2005, groups = var_1828, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_2003, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_27_cast")];
            tensor<int32, [4]> var_2009 = const()[name = tensor<string, []>("op_2009"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_2010_cast = reshape(shape = var_2009, x = q_27_cast)[name = tensor<string, []>("op_2010_cast")];
            tensor<int32, [4]> var_2011 = const()[name = tensor<string, []>("op_2011"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2012_cast = reshape(shape = var_2011, x = k_27_cast)[name = tensor<string, []>("op_2012_cast")];
            tensor<int32, [4]> var_2013 = const()[name = tensor<string, []>("op_2013"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2014_cast = reshape(shape = var_2013, x = v_27_cast)[name = tensor<string, []>("op_2014_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, 64, 77]> attn_weights_53_cast = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_2010_cast, y = var_2012_cast)[name = tensor<string, []>("attn_weights_53_cast")];
            tensor<fp16, [2, 20, 64, 77]> attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_1819_to_fp16)[name = tensor<string, []>("attn_weights_55_cast")];
            tensor<fp16, [2, 20, 64, 77]> var_2018_cast = softmax(axis = var_1812, x = attn_weights_55_cast)[name = tensor<string, []>("op_2018_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, 64]> attn_27_cast = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_2014_cast, y = var_2018_cast)[name = tensor<string, []>("attn_27_cast")];
            tensor<int32, [4]> var_2022 = const()[name = tensor<string, []>("op_2022"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 64]> input_217_cast = reshape(shape = var_2022, x = attn_27_cast)[name = tensor<string, []>("input_217_cast")];
            tensor<int32, [2]> var_2027 = const()[name = tensor<string, []>("op_2027"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2029 = const()[name = tensor<string, []>("op_2029"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2031_pad_type_0 = const()[name = tensor<string, []>("op_2031_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2031_pad_0 = const()[name = tensor<string, []>("op_2031_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(223392576))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224621440))), 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, []>(224621632)))];
            tensor<fp16, [2, 1280, 1, 64]> var_2031_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2029, groups = var_1828, pad = var_2031_pad_0, pad_type = var_2031_pad_type_0, strides = var_2027, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_217_cast)[name = tensor<string, []>("op_2031_cast")];
            tensor<fp16, [2, 1280, 1, 64]> inputs_41_cast = add(x = var_2031_cast, y = inputs_39_cast)[name = tensor<string, []>("inputs_41_cast")];
            tensor<int32, [1]> var_2035 = const()[name = tensor<string, []>("op_2035"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> channels_mean_41_cast = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast)[name = tensor<string, []>("channels_mean_41_cast")];
            tensor<fp16, [2, 1280, 1, 64]> 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, 64]> 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_2039 = const()[name = tensor<string, []>("op_2039"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> var_2040_cast = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast)[name = tensor<string, []>("op_2040_cast")];
            tensor<fp16, []> var_2041_to_fp16 = const()[name = tensor<string, []>("op_2041_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 64]> var_2042_cast = add(x = var_2040_cast, y = var_2041_to_fp16)[name = tensor<string, []>("op_2042_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, 64]> denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_2042_cast)[name = tensor<string, []>("denom_41_cast")];
            tensor<fp16, [2, 1280, 1, 64]> 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_2046_to_fp16 = const()[name = tensor<string, []>("op_2046_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224624256)))];
            tensor<fp16, [2, 1280, 1, 64]> var_2047_cast = add(x = out_41_cast, y = var_2046_to_fp16)[name = tensor<string, []>("op_2047_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2049_to_fp16 = const()[name = tensor<string, []>("op_2049_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224626880)))];
            tensor<fp16, [2, 1280, 1, 64]> input_219_cast = mul(x = var_2047_cast, y = var_2049_to_fp16)[name = tensor<string, []>("input_219_cast")];
            tensor<int32, [2]> var_2057 = const()[name = tensor<string, []>("op_2057"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2059 = const()[name = tensor<string, []>("op_2059"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2061_pad_type_0 = const()[name = tensor<string, []>("op_2061_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2061_pad_0 = const()[name = tensor<string, []>("op_2061_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, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224629504))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234459968))), 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_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234460160))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234467904))), name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([10240])];
            tensor<fp16, [2, 10240, 1, 64]> var_2061_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2059, groups = var_1828, pad = var_2061_pad_0, pad_type = var_2061_pad_type_0, strides = var_2057, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_219_cast)[name = tensor<string, []>("op_2061_cast")];
            tensor<int32, [2]> var_2062_split_sizes_0 = const()[name = tensor<string, []>("op_2062_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2062_axis_0 = const()[name = tensor<string, []>("op_2062_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 64]> var_2062_cast_0, tensor<fp16, [2, 5120, 1, 64]> var_2062_cast_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast)[name = tensor<string, []>("op_2062_cast")];
            tensor<string, []> var_2064_mode_0 = const()[name = tensor<string, []>("op_2064_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 64]> var_2064_cast = gelu(mode = var_2064_mode_0, x = var_2062_cast_1)[name = tensor<string, []>("op_2064_cast")];
            tensor<fp16, [2, 5120, 1, 64]> input_221_cast = mul(x = var_2062_cast_0, y = var_2064_cast)[name = tensor<string, []>("input_221_cast")];
            tensor<int32, [2]> var_2068 = const()[name = tensor<string, []>("op_2068"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2070 = const()[name = tensor<string, []>("op_2070"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2072_pad_type_0 = const()[name = tensor<string, []>("op_2072_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2072_pad_0 = const()[name = tensor<string, []>("op_2072_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, [4915200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234468096))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239383360))), 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, []>(239383552)))];
            tensor<fp16, [2, 1280, 1, 64]> var_2072_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2070, groups = var_1828, pad = var_2072_pad_0, pad_type = var_2072_pad_type_0, strides = var_2068, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_221_cast)[name = tensor<string, []>("op_2072_cast")];
            tensor<fp16, [2, 1280, 1, 64]> hidden_states_131_cast = add(x = var_2072_cast, y = inputs_41_cast)[name = tensor<string, []>("hidden_states_131_cast")];
            tensor<int32, [4]> var_2074 = const()[name = tensor<string, []>("op_2074"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> input_223_cast = reshape(shape = var_2074, x = hidden_states_131_cast)[name = tensor<string, []>("input_223_cast")];
            tensor<int32, [2]> var_2078 = const()[name = tensor<string, []>("op_2078"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2080 = const()[name = tensor<string, []>("op_2080"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_133_pad_type_0 = const()[name = tensor<string, []>("hidden_states_133_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = tensor<string, []>("hidden_states_133_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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239386176))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240615040))), 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, []>(240615232)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_133_cast = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_2080, groups = var_1828, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_2078, weight = mid_block_attentions_0_proj_out_weight_to_fp16_palettized, x = input_223_cast)[name = tensor<string, []>("hidden_states_133_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_225_cast = add(x = hidden_states_133_cast, y = hidden_states_121_cast)[name = tensor<string, []>("input_225_cast")];
            tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_225_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, 8, 8]> sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor<string, []>("sub_50_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(240617856)))];
            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, []>(240620480)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_101_cast)[name = tensor<string, []>("add_51_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_229_cast = silu(x = add_51_cast)[name = tensor<string, []>("input_229_cast")];
            tensor<int32, [2]> var_2095 = const()[name = tensor<string, []>("op_2095"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2097 = const()[name = tensor<string, []>("op_2097"), 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]>([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, []>(240623104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251682368))), 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, []>(251682560)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_135_cast = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_2097, groups = var_1828, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_2095, weight = mid_block_resnets_1_conv1_weight_to_fp16_palettized, x = input_229_cast)[name = tensor<string, []>("hidden_states_135_cast")];
            tensor<int32, [2]> var_2103 = const()[name = tensor<string, []>("op_2103"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2105 = const()[name = tensor<string, []>("op_2105"), 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]> mid_block_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, []>(251685184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252914048))), 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, []>(252914240)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_19_cast = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2105, groups = var_1828, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_2103, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_19_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_233_cast = add(x = hidden_states_135_cast, y = temb_19_cast)[name = tensor<string, []>("input_233_cast")];
            tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_104_cast = reshape(shape = reshape_104_shape_0, x = input_233_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, 8, 8]> sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor<string, []>("sub_52_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, []>(0x1.5p-17)];
            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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(252916864)))];
            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, []>(252919488)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_105_cast)[name = tensor<string, []>("add_53_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_237_cast = silu(x = add_53_cast)[name = tensor<string, []>("input_237_cast")];
            tensor<int32, [2]> var_2115 = const()[name = tensor<string, []>("op_2115"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2117 = const()[name = tensor<string, []>("op_2117"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_137_pad_type_0 = const()[name = tensor<string, []>("hidden_states_137_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = tensor<string, []>("hidden_states_137_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, []>(252922112))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263981376))), 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, []>(263981568)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_137_cast = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_2117, groups = var_1828, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_2115, weight = mid_block_resnets_1_conv2_weight_to_fp16_palettized, x = input_237_cast)[name = tensor<string, []>("hidden_states_137_cast")];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_139_cast = add(x = input_225_cast, y = hidden_states_137_cast)[name = tensor<string, []>("hidden_states_139_cast")];
            tensor<int32, []> var_2128 = const()[name = tensor<string, []>("op_2128"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_239_interleave_0 = const()[name = tensor<string, []>("input_239_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 8, 8]> input_239_cast = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast, input_199_cast))[name = tensor<string, []>("input_239_cast")];
            tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 8, 8])];
            tensor<fp16, [2, 32, 80, 8, 8]> reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_239_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, 80, 8, 8]> sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor<string, []>("sub_54_cast")];
            tensor<fp16, [2, 32, 80, 8, 8]> 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, 80, 8, 8]> 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, 2560, 8, 8])];
            tensor<fp16, [2, 2560, 8, 8]> reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor<string, []>("reshape_109_cast")];
            tensor<fp16, [2560]> add_55_mean_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263984192))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263986176))), name = tensor<string, []>("add_55_mean_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2560]> add_55_variance_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263986368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263988352))), name = tensor<string, []>("add_55_variance_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2560]> add_55_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263988544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263990528))), name = tensor<string, []>("add_55_gamma_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2560]> add_55_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263990720))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263992704))), name = tensor<string, []>("add_55_beta_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            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, 2560, 8, 8]> add_55_cast = batch_norm(beta = add_55_beta_0_to_fp16_palettized, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_109_cast)[name = tensor<string, []>("add_55_cast")];
            tensor<fp16, [2, 2560, 8, 8]> input_243_cast = silu(x = add_55_cast)[name = tensor<string, []>("input_243_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, []> hidden_states_141_pad_type_0 = const()[name = tensor<string, []>("hidden_states_141_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_141_pad_0 = const()[name = tensor<string, []>("hidden_states_141_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, []>(263992896))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286111360))), 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, []>(286111552)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_141_cast = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_2153, groups = var_2128, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_2151, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_243_cast)[name = tensor<string, []>("hidden_states_141_cast")];
            tensor<int32, [2]> var_2159 = const()[name = tensor<string, []>("op_2159"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2161 = const()[name = tensor<string, []>("op_2161"), 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_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, []>(286114176))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287343040))), 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, []>(287343232)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_21_cast = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2161, groups = var_2128, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_2159, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_21_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_247_cast = add(x = hidden_states_141_cast, y = temb_21_cast)[name = tensor<string, []>("input_247_cast")];
            tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_247_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, 8, 8]> sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor<string, []>("sub_56_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, 8, 8]> 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, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> 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, []>(287345856)))];
            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, []>(287348480)))];
            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, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_113_cast)[name = tensor<string, []>("add_57_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_251_cast = silu(x = add_57_cast)[name = tensor<string, []>("input_251_cast")];
            tensor<int32, [2]> var_2171 = const()[name = tensor<string, []>("op_2171"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2173 = const()[name = tensor<string, []>("op_2173"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_143_pad_type_0 = const()[name = tensor<string, []>("hidden_states_143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = tensor<string, []>("hidden_states_143_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, []>(287351104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298410368))), 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, []>(298410560)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_143_cast = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_2173, groups = var_2128, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_2171, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_251_cast)[name = tensor<string, []>("hidden_states_143_cast")];
            tensor<int32, [2]> var_2178 = const()[name = tensor<string, []>("op_2178"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2180 = const()[name = tensor<string, []>("op_2180"), 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, []>(298413184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300870848))), 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, []>(300871040)))];
            tensor<fp16, [2, 1280, 8, 8]> x_5_cast = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2180, groups = var_2128, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_2178, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_239_cast)[name = tensor<string, []>("x_5_cast")];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_145_cast = add(x = x_5_cast, y = hidden_states_143_cast)[name = tensor<string, []>("hidden_states_145_cast")];
            tensor<bool, []> input_253_interleave_0 = const()[name = tensor<string, []>("input_253_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 8, 8]> input_253_cast = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast, input_185_cast))[name = tensor<string, []>("input_253_cast")];
            tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 8, 8])];
            tensor<fp16, [2, 32, 80, 8, 8]> reshape_116_cast = reshape(shape = reshape_116_shape_0, x = input_253_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, 80, 8, 8]> sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor<string, []>("sub_58_cast")];
            tensor<fp16, [2, 32, 80, 8, 8]> 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, []>(0x1.5p-17)];
            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, 80, 8, 8]> 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, 2560, 8, 8])];
            tensor<fp16, [2, 2560, 8, 8]> reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor<string, []>("reshape_117_cast")];
            tensor<fp16, [2560]> add_59_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300873664))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300875648))), name = tensor<string, []>("add_59_gamma_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2560]> add_59_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300875840))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300877824))), name = tensor<string, []>("add_59_beta_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            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, 2560, 8, 8]> add_59_cast = batch_norm(beta = add_59_beta_0_to_fp16_palettized, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_117_cast)[name = tensor<string, []>("add_59_cast")];
            tensor<fp16, [2, 2560, 8, 8]> input_257_cast = silu(x = add_59_cast)[name = tensor<string, []>("input_257_cast")];
            tensor<int32, [2]> var_2198 = const()[name = tensor<string, []>("op_2198"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2200 = const()[name = tensor<string, []>("op_2200"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_147_pad_type_0 = const()[name = tensor<string, []>("hidden_states_147_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = tensor<string, []>("hidden_states_147_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, []>(300878016))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322996480))), 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, []>(322996672)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_147_cast = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_2200, groups = var_2128, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_2198, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_257_cast)[name = tensor<string, []>("hidden_states_147_cast")];
            tensor<int32, [2]> var_2206 = const()[name = tensor<string, []>("op_2206"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2208 = const()[name = tensor<string, []>("op_2208"), 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, [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, []>(322999296))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324228160))), 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, []>(324228352)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_23_cast = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2208, groups = var_2128, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_2206, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_23_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_261_cast = add(x = hidden_states_147_cast, y = temb_23_cast)[name = tensor<string, []>("input_261_cast")];
            tensor<int32, [5]> reshape_120_shape_0 = const()[name = tensor<string, []>("reshape_120_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_261_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, 40, 8, 8]> sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast)[name = tensor<string, []>("sub_60_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, 40, 8, 8]> 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, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast)[name = tensor<string, []>("reshape_121_cast")];
            tensor<fp16, [1280]> add_61_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_61_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324230976)))];
            tensor<fp16, [1280]> add_61_beta_0_to_fp16 = const()[name = tensor<string, []>("add_61_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324233600)))];
            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, 1280, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_121_cast)[name = tensor<string, []>("add_61_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_265_cast = silu(x = add_61_cast)[name = tensor<string, []>("input_265_cast")];
            tensor<int32, [2]> var_2218 = const()[name = tensor<string, []>("op_2218"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2220 = const()[name = tensor<string, []>("op_2220"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_149_pad_type_0 = const()[name = tensor<string, []>("hidden_states_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_149_pad_0 = const()[name = tensor<string, []>("hidden_states_149_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, []>(324236224))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335295488))), 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, []>(335295680)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_149_cast = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_2220, groups = var_2128, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_2218, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_265_cast)[name = tensor<string, []>("hidden_states_149_cast")];
            tensor<int32, [2]> var_2225 = const()[name = tensor<string, []>("op_2225"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2227 = const()[name = tensor<string, []>("op_2227"), 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, []>(335298304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337755968))), 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, []>(337756160)))];
            tensor<fp16, [2, 1280, 8, 8]> x_7_cast = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2227, groups = var_2128, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_2225, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_253_cast)[name = tensor<string, []>("x_7_cast")];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_151_cast = add(x = x_7_cast, y = hidden_states_149_cast)[name = tensor<string, []>("hidden_states_151_cast")];
            tensor<bool, []> input_267_interleave_0 = const()[name = tensor<string, []>("input_267_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 8, 8]> input_267_cast = concat(axis = var_2128, interleave = input_267_interleave_0, values = (hidden_states_151_cast, input_171_cast))[name = tensor<string, []>("input_267_cast")];
            tensor<int32, [5]> reshape_124_shape_0 = const()[name = tensor<string, []>("reshape_124_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 8, 8])];
            tensor<fp16, [2, 32, 80, 8, 8]> reshape_124_cast = reshape(shape = reshape_124_shape_0, x = input_267_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, 80, 8, 8]> sub_62_cast = sub(x = reshape_124_cast, y = reduce_mean_93_cast)[name = tensor<string, []>("sub_62_cast")];
            tensor<fp16, [2, 32, 80, 8, 8]> 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, 80, 8, 8]> 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, 2560, 8, 8])];
            tensor<fp16, [2, 2560, 8, 8]> reshape_125_cast = reshape(shape = reshape_125_shape_0, x = real_div_31_cast)[name = tensor<string, []>("reshape_125_cast")];
            tensor<fp16, [2560]> add_63_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337758784))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337760768))), name = tensor<string, []>("add_63_gamma_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2560]> add_63_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337760960))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337762944))), name = tensor<string, []>("add_63_beta_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            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, 2560, 8, 8]> add_63_cast = batch_norm(beta = add_63_beta_0_to_fp16_palettized, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_125_cast)[name = tensor<string, []>("add_63_cast")];
            tensor<fp16, [2, 2560, 8, 8]> input_271_cast = silu(x = add_63_cast)[name = tensor<string, []>("input_271_cast")];
            tensor<int32, [2]> var_2245 = const()[name = tensor<string, []>("op_2245"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2247 = const()[name = tensor<string, []>("op_2247"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_153_pad_type_0 = const()[name = tensor<string, []>("hidden_states_153_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = tensor<string, []>("hidden_states_153_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 2560, 3, 3]> up_blocks_0_resnets_2_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, []>(337763136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359881600))), name = tensor<string, []>("up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 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, []>(359881792)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_153_cast = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_2247, groups = var_2128, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_2245, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized, x = input_271_cast)[name = tensor<string, []>("hidden_states_153_cast")];
            tensor<int32, [2]> var_2253 = const()[name = tensor<string, []>("op_2253"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2255 = const()[name = tensor<string, []>("op_2255"), 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, [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, []>(359884416))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361113280))), 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, []>(361113472)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_25_cast = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2255, groups = var_2128, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_2253, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_25_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_275_cast = add(x = hidden_states_153_cast, y = temb_25_cast)[name = tensor<string, []>("input_275_cast")];
            tensor<int32, [5]> reshape_128_shape_0 = const()[name = tensor<string, []>("reshape_128_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_128_cast = reshape(shape = reshape_128_shape_0, x = input_275_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, 40, 8, 8]> sub_64_cast = sub(x = reshape_128_cast, y = reduce_mean_96_cast)[name = tensor<string, []>("sub_64_cast")];
            tensor<fp16, [2, 32, 40, 8, 8]> 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, []>(0x1.5p-17)];
            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, 40, 8, 8]> 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, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_129_cast = reshape(shape = reshape_129_shape_0, x = real_div_32_cast)[name = tensor<string, []>("reshape_129_cast")];
            tensor<fp16, [1280]> add_65_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_65_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361116096)))];
            tensor<fp16, [1280]> add_65_beta_0_to_fp16 = const()[name = tensor<string, []>("add_65_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361118720)))];
            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, 1280, 8, 8]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_129_cast)[name = tensor<string, []>("add_65_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_279_cast = silu(x = add_65_cast)[name = tensor<string, []>("input_279_cast")];
            tensor<int32, [2]> var_2265 = const()[name = tensor<string, []>("op_2265"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2267 = const()[name = tensor<string, []>("op_2267"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_155_pad_type_0 = const()[name = tensor<string, []>("hidden_states_155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_155_pad_0 = const()[name = tensor<string, []>("hidden_states_155_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, []>(361121344))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372180608))), 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, []>(372180800)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_155_cast = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_2267, groups = var_2128, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_2265, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized, x = input_279_cast)[name = tensor<string, []>("hidden_states_155_cast")];
            tensor<int32, [2]> var_2272 = const()[name = tensor<string, []>("op_2272"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2274 = const()[name = tensor<string, []>("op_2274"), 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, 2560, 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, []>(372183424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(374641088))), name = tensor<string, []>("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 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, []>(374641280)))];
            tensor<fp16, [2, 1280, 8, 8]> x_9_cast = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2274, groups = var_2128, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_2272, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_267_cast)[name = tensor<string, []>("x_9_cast")];
            tensor<fp16, [2, 1280, 8, 8]> input_281_cast = add(x = x_9_cast, y = hidden_states_155_cast)[name = tensor<string, []>("input_281_cast")];
            tensor<fp32, []> input_283_scale_factor_height_0 = const()[name = tensor<string, []>("input_283_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, []> input_283_scale_factor_width_0 = const()[name = tensor<string, []>("input_283_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp16, [2, 1280, 16, 16]> input_283_cast = upsample_nearest_neighbor(scale_factor_height = input_283_scale_factor_height_0, scale_factor_width = input_283_scale_factor_width_0, x = input_281_cast)[name = tensor<string, []>("input_283_cast")];
            tensor<int32, [2]> var_2283 = const()[name = tensor<string, []>("op_2283"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2285 = const()[name = tensor<string, []>("op_2285"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_157_pad_type_0 = const()[name = tensor<string, []>("hidden_states_157_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = tensor<string, []>("hidden_states_157_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, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(374643904))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385703168))), 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, []>(385703360)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_157_cast = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_2285, groups = var_2128, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_2283, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized, x = input_283_cast)[name = tensor<string, []>("hidden_states_157_cast")];
            tensor<int32, []> var_2290 = const()[name = tensor<string, []>("op_2290"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_2301 = const()[name = tensor<string, []>("op_2301"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_2306 = const()[name = tensor<string, []>("op_2306"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_285_interleave_0 = const()[name = tensor<string, []>("input_285_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 16, 16]> input_285_cast = concat(axis = var_2306, interleave = input_285_interleave_0, values = (hidden_states_157_cast, input_169_cast))[name = tensor<string, []>("input_285_cast")];
            tensor<int32, [5]> reshape_132_shape_0 = const()[name = tensor<string, []>("reshape_132_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 16, 16])];
            tensor<fp16, [2, 32, 80, 16, 16]> reshape_132_cast = reshape(shape = reshape_132_shape_0, x = input_285_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, 80, 16, 16]> sub_66_cast = sub(x = reshape_132_cast, y = reduce_mean_99_cast)[name = tensor<string, []>("sub_66_cast")];
            tensor<fp16, [2, 32, 80, 16, 16]> 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, 80, 16, 16]> 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, 2560, 16, 16])];
            tensor<fp16, [2, 2560, 16, 16]> reshape_133_cast = reshape(shape = reshape_133_shape_0, x = real_div_33_cast)[name = tensor<string, []>("reshape_133_cast")];
            tensor<fp16, [2560]> add_67_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385705984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385707968))), name = tensor<string, []>("add_67_gamma_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2560]> add_67_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385708160))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385710144))), name = tensor<string, []>("add_67_beta_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            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, 2560, 16, 16]> add_67_cast = batch_norm(beta = add_67_beta_0_to_fp16_palettized, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_133_cast)[name = tensor<string, []>("add_67_cast")];
            tensor<fp16, [2, 2560, 16, 16]> input_289_cast = silu(x = add_67_cast)[name = tensor<string, []>("input_289_cast")];
            tensor<int32, [2]> var_2335 = const()[name = tensor<string, []>("op_2335"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2337 = const()[name = tensor<string, []>("op_2337"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_159_pad_type_0 = const()[name = tensor<string, []>("hidden_states_159_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_159_pad_0 = const()[name = tensor<string, []>("hidden_states_159_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 2560, 3, 3]> up_blocks_1_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, []>(385710336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407828800))), name = tensor<string, []>("up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 3, 3])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407828992)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_159_cast = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_2337, groups = var_2306, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_2335, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_289_cast)[name = tensor<string, []>("hidden_states_159_cast")];
            tensor<int32, [2]> var_2343 = const()[name = tensor<string, []>("op_2343"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2345 = const()[name = tensor<string, []>("op_2345"), 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, [1280, 1280, 1, 1]> up_blocks_1_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, []>(407831616))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409060480))), name = tensor<string, []>("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409060672)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_27_cast = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2345, groups = var_2306, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_2343, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_27_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_293_cast = add(x = hidden_states_159_cast, y = temb_27_cast)[name = tensor<string, []>("input_293_cast")];
            tensor<int32, [5]> reshape_136_shape_0 = const()[name = tensor<string, []>("reshape_136_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_136_cast = reshape(shape = reshape_136_shape_0, x = input_293_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, 40, 16, 16]> sub_68_cast = sub(x = reshape_136_cast, y = reduce_mean_102_cast)[name = tensor<string, []>("sub_68_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, 40, 16, 16]> 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, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_137_cast = reshape(shape = reshape_137_shape_0, x = real_div_34_cast)[name = tensor<string, []>("reshape_137_cast")];
            tensor<fp16, [1280]> add_69_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_69_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409063296)))];
            tensor<fp16, [1280]> add_69_beta_0_to_fp16 = const()[name = tensor<string, []>("add_69_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409065920)))];
            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, 1280, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_137_cast)[name = tensor<string, []>("add_69_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_297_cast = silu(x = add_69_cast)[name = tensor<string, []>("input_297_cast")];
            tensor<int32, [2]> var_2355 = const()[name = tensor<string, []>("op_2355"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2357 = const()[name = tensor<string, []>("op_2357"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_161_pad_type_0 = const()[name = tensor<string, []>("hidden_states_161_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_161_pad_0 = const()[name = tensor<string, []>("hidden_states_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_1_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, []>(409068544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420127808))), name = tensor<string, []>("up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420128000)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_161_cast = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_2357, groups = var_2306, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_2355, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_297_cast)[name = tensor<string, []>("hidden_states_161_cast")];
            tensor<int32, [2]> var_2362 = const()[name = tensor<string, []>("op_2362"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2364 = const()[name = tensor<string, []>("op_2364"), 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, [1280, 2560, 1, 1]> up_blocks_1_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, []>(420130624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422588288))), name = tensor<string, []>("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422588480)))];
            tensor<fp16, [2, 1280, 16, 16]> x_11_cast = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2364, groups = var_2306, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_2362, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_285_cast)[name = tensor<string, []>("x_11_cast")];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_163_cast = add(x = x_11_cast, y = hidden_states_161_cast)[name = tensor<string, []>("hidden_states_163_cast")];
            tensor<int32, [5]> reshape_140_shape_0 = const()[name = tensor<string, []>("reshape_140_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_140_cast = reshape(shape = reshape_140_shape_0, x = hidden_states_163_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, 40, 16, 16]> sub_70_cast = sub(x = reshape_140_cast, y = reduce_mean_105_cast)[name = tensor<string, []>("sub_70_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, 40, 16, 16]> 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, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_141_cast = reshape(shape = reshape_141_shape_0, x = real_div_35_cast)[name = tensor<string, []>("reshape_141_cast")];
            tensor<fp16, [1280]> add_71_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_71_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422591104)))];
            tensor<fp16, [1280]> add_71_beta_0_to_fp16 = const()[name = tensor<string, []>("add_71_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422593728)))];
            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, 1280, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_141_cast)[name = tensor<string, []>("add_71_cast")];
            tensor<int32, [2]> var_2384 = const()[name = tensor<string, []>("op_2384"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2386 = const()[name = tensor<string, []>("op_2386"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_165_pad_type_0 = const()[name = tensor<string, []>("hidden_states_165_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_165_pad_0 = const()[name = tensor<string, []>("hidden_states_165_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_1_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, []>(422596352))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423825216))), name = tensor<string, []>("up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423825408)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_165_cast = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_2386, groups = var_2306, pad = hidden_states_165_pad_0, pad_type = hidden_states_165_pad_type_0, strides = var_2384, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_71_cast)[name = tensor<string, []>("hidden_states_165_cast")];
            tensor<int32, [4]> var_2391 = const()[name = tensor<string, []>("op_2391"), val = tensor<int32, [4]>([2, 1280, 1, 256])];
            tensor<fp16, [2, 1280, 1, 256]> inputs_43_cast = reshape(shape = var_2391, x = hidden_states_165_cast)[name = tensor<string, []>("inputs_43_cast")];
            tensor<int32, [1]> var_2401 = const()[name = tensor<string, []>("op_2401"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_43_cast = reduce_mean(axes = var_2401, keep_dims = var_2301, x = inputs_43_cast)[name = tensor<string, []>("channels_mean_43_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2405 = const()[name = tensor<string, []>("op_2405"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2406_cast = reduce_mean(axes = var_2405, keep_dims = var_2301, x = zero_mean_sq_43_cast)[name = tensor<string, []>("op_2406_cast")];
            tensor<fp16, []> var_2407_to_fp16 = const()[name = tensor<string, []>("op_2407_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_2408_cast = add(x = var_2406_cast, y = var_2407_to_fp16)[name = tensor<string, []>("op_2408_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, 256]> denom_43_cast = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_2408_cast)[name = tensor<string, []>("denom_43_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_2412_to_fp16 = const()[name = tensor<string, []>("op_2412_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423828032)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2413_cast = add(x = out_43_cast, y = var_2412_to_fp16)[name = tensor<string, []>("op_2413_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2415_to_fp16 = const()[name = tensor<string, []>("op_2415_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(423830656)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_167_cast = mul(x = var_2413_cast, y = var_2415_to_fp16)[name = tensor<string, []>("hidden_states_167_cast")];
            tensor<int32, [2]> var_2422 = const()[name = tensor<string, []>("op_2422"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2424 = const()[name = tensor<string, []>("op_2424"), 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]> up_blocks_1_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, []>(423833280))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425062144))), name = tensor<string, []>("up_blocks_1_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, 256]> q_29_cast = conv(dilations = var_2424, groups = var_2306, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_2422, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor<string, []>("q_29_cast")];
            tensor<int32, [2]> var_2428 = const()[name = tensor<string, []>("op_2428"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2430 = const()[name = tensor<string, []>("op_2430"), 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]> up_blocks_1_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, []>(425062336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(426291200))), name = tensor<string, []>("up_blocks_1_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, 256]> k_29_cast = conv(dilations = var_2430, groups = var_2306, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_2428, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor<string, []>("k_29_cast")];
            tensor<int32, [2]> var_2434 = const()[name = tensor<string, []>("op_2434"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2436 = const()[name = tensor<string, []>("op_2436"), 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]> up_blocks_1_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, []>(426291392))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(427520256))), name = tensor<string, []>("up_blocks_1_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, 256]> v_29_cast = conv(dilations = var_2436, groups = var_2306, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_2434, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor<string, []>("v_29_cast")];
            tensor<int32, [4]> var_2440 = const()[name = tensor<string, []>("op_2440"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2441_cast = reshape(shape = var_2440, x = q_29_cast)[name = tensor<string, []>("op_2441_cast")];
            tensor<int32, [4]> var_2442 = const()[name = tensor<string, []>("op_2442"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2443_cast = reshape(shape = var_2442, x = k_29_cast)[name = tensor<string, []>("op_2443_cast")];
            tensor<int32, [4]> var_2444 = const()[name = tensor<string, []>("op_2444"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2445_cast = reshape(shape = var_2444, x = v_29_cast)[name = tensor<string, []>("op_2445_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, 256, 256]> attn_weights_57_cast = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2441_cast, y = var_2443_cast)[name = tensor<string, []>("attn_weights_57_cast")];
            tensor<fp16, []> var_2297_to_fp16 = const()[name = tensor<string, []>("op_2297_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 256, 256]> attn_weights_59_cast = mul(x = attn_weights_57_cast, y = var_2297_to_fp16)[name = tensor<string, []>("attn_weights_59_cast")];
            tensor<fp16, [2, 20, 256, 256]> var_2449_cast = softmax(axis = var_2290, x = attn_weights_59_cast)[name = tensor<string, []>("op_2449_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, 256]> attn_29_cast = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_2445_cast, y = var_2449_cast)[name = tensor<string, []>("attn_29_cast")];
            tensor<int32, [4]> var_2453 = const()[name = tensor<string, []>("op_2453"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_301_cast = reshape(shape = var_2453, x = attn_29_cast)[name = tensor<string, []>("input_301_cast")];
            tensor<int32, [2]> var_2458 = const()[name = tensor<string, []>("op_2458"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2460 = const()[name = tensor<string, []>("op_2460"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2462_pad_type_0 = const()[name = tensor<string, []>("op_2462_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2462_pad_0 = const()[name = tensor<string, []>("op_2462_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(427520448))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428749312))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428749504)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2462_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2460, groups = var_2306, pad = var_2462_pad_0, pad_type = var_2462_pad_type_0, strides = var_2458, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_301_cast)[name = tensor<string, []>("op_2462_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_45_cast = add(x = var_2462_cast, y = inputs_43_cast)[name = tensor<string, []>("inputs_45_cast")];
            tensor<int32, [1]> var_2466 = const()[name = tensor<string, []>("op_2466"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_45_cast = reduce_mean(axes = var_2466, keep_dims = var_2301, x = inputs_45_cast)[name = tensor<string, []>("channels_mean_45_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2470 = const()[name = tensor<string, []>("op_2470"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2471_cast = reduce_mean(axes = var_2470, keep_dims = var_2301, x = zero_mean_sq_45_cast)[name = tensor<string, []>("op_2471_cast")];
            tensor<fp16, []> var_2472_to_fp16 = const()[name = tensor<string, []>("op_2472_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_2473_cast = add(x = var_2471_cast, y = var_2472_to_fp16)[name = tensor<string, []>("op_2473_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, 256]> denom_45_cast = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_2473_cast)[name = tensor<string, []>("denom_45_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_2477_to_fp16 = const()[name = tensor<string, []>("op_2477_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428752128)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2478_cast = add(x = out_45_cast, y = var_2477_to_fp16)[name = tensor<string, []>("op_2478_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2480_to_fp16 = const()[name = tensor<string, []>("op_2480_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428754752)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_169_cast = mul(x = var_2478_cast, y = var_2480_to_fp16)[name = tensor<string, []>("hidden_states_169_cast")];
            tensor<int32, [2]> var_2487 = const()[name = tensor<string, []>("op_2487"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2489 = const()[name = tensor<string, []>("op_2489"), 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]> up_blocks_1_attentions_0_transformer_blocks_0_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, []>(428757376))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429986240))), name = tensor<string, []>("up_blocks_1_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, 256]> q_31_cast = conv(dilations = var_2489, groups = var_2306, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_2487, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_169_cast)[name = tensor<string, []>("q_31_cast")];
            tensor<int32, [2]> var_2493 = const()[name = tensor<string, []>("op_2493"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2495 = const()[name = tensor<string, []>("op_2495"), 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, 1024, 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, []>(429986432))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430969536))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_31_cast = conv(dilations = var_2495, groups = var_2306, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_2493, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_31_cast")];
            tensor<int32, [2]> var_2499 = const()[name = tensor<string, []>("op_2499"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2501 = const()[name = tensor<string, []>("op_2501"), 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, 1024, 1, 1]> up_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, []>(430969728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431952832))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_31_cast = conv(dilations = var_2501, groups = var_2306, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_2499, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_31_cast")];
            tensor<int32, [4]> var_2505 = const()[name = tensor<string, []>("op_2505"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2506_cast = reshape(shape = var_2505, x = q_31_cast)[name = tensor<string, []>("op_2506_cast")];
            tensor<int32, [4]> var_2507 = const()[name = tensor<string, []>("op_2507"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2508_cast = reshape(shape = var_2507, x = k_31_cast)[name = tensor<string, []>("op_2508_cast")];
            tensor<int32, [4]> var_2509 = const()[name = tensor<string, []>("op_2509"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2510_cast = reshape(shape = var_2509, x = v_31_cast)[name = tensor<string, []>("op_2510_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, 256, 77]> attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_2506_cast, y = var_2508_cast)[name = tensor<string, []>("attn_weights_61_cast")];
            tensor<fp16, [2, 20, 256, 77]> attn_weights_63_cast = mul(x = attn_weights_61_cast, y = var_2297_to_fp16)[name = tensor<string, []>("attn_weights_63_cast")];
            tensor<fp16, [2, 20, 256, 77]> var_2514_cast = softmax(axis = var_2290, x = attn_weights_63_cast)[name = tensor<string, []>("op_2514_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, 256]> attn_31_cast = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_2510_cast, y = var_2514_cast)[name = tensor<string, []>("attn_31_cast")];
            tensor<int32, [4]> var_2518 = const()[name = tensor<string, []>("op_2518"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_303_cast = reshape(shape = var_2518, x = attn_31_cast)[name = tensor<string, []>("input_303_cast")];
            tensor<int32, [2]> var_2523 = const()[name = tensor<string, []>("op_2523"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2525 = const()[name = tensor<string, []>("op_2525"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2527_pad_type_0 = const()[name = tensor<string, []>("op_2527_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2527_pad_0 = const()[name = tensor<string, []>("op_2527_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431953024))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433181888))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433182080)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2527_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2525, groups = var_2306, pad = var_2527_pad_0, pad_type = var_2527_pad_type_0, strides = var_2523, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_303_cast)[name = tensor<string, []>("op_2527_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_47_cast = add(x = var_2527_cast, y = inputs_45_cast)[name = tensor<string, []>("inputs_47_cast")];
            tensor<int32, [1]> var_2531 = const()[name = tensor<string, []>("op_2531"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_47_cast = reduce_mean(axes = var_2531, keep_dims = var_2301, x = inputs_47_cast)[name = tensor<string, []>("channels_mean_47_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2535 = const()[name = tensor<string, []>("op_2535"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2536_cast = reduce_mean(axes = var_2535, keep_dims = var_2301, x = zero_mean_sq_47_cast)[name = tensor<string, []>("op_2536_cast")];
            tensor<fp16, []> var_2537_to_fp16 = const()[name = tensor<string, []>("op_2537_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_2538_cast = add(x = var_2536_cast, y = var_2537_to_fp16)[name = tensor<string, []>("op_2538_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, 256]> denom_47_cast = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_2538_cast)[name = tensor<string, []>("denom_47_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_2542_to_fp16 = const()[name = tensor<string, []>("op_2542_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433184704)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2543_cast = add(x = out_47_cast, y = var_2542_to_fp16)[name = tensor<string, []>("op_2543_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2545_to_fp16 = const()[name = tensor<string, []>("op_2545_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433187328)))];
            tensor<fp16, [2, 1280, 1, 256]> input_305_cast = mul(x = var_2543_cast, y = var_2545_to_fp16)[name = tensor<string, []>("input_305_cast")];
            tensor<int32, [2]> var_2553 = const()[name = tensor<string, []>("op_2553"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2555 = const()[name = tensor<string, []>("op_2555"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2557_pad_type_0 = const()[name = tensor<string, []>("op_2557_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2557_pad_0 = const()[name = tensor<string, []>("op_2557_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 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, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433189952))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443020416))), name = tensor<string, []>("up_blocks_1_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_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443020608))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(443028352))), name = tensor<string, []>("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([10240])];
            tensor<fp16, [2, 10240, 1, 256]> var_2557_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2555, groups = var_2306, pad = var_2557_pad_0, pad_type = var_2557_pad_type_0, strides = var_2553, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_305_cast)[name = tensor<string, []>("op_2557_cast")];
            tensor<int32, [2]> var_2558_split_sizes_0 = const()[name = tensor<string, []>("op_2558_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2558_axis_0 = const()[name = tensor<string, []>("op_2558_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 256]> var_2558_cast_0, tensor<fp16, [2, 5120, 1, 256]> var_2558_cast_1 = split(axis = var_2558_axis_0, split_sizes = var_2558_split_sizes_0, x = var_2557_cast)[name = tensor<string, []>("op_2558_cast")];
            tensor<string, []> var_2560_mode_0 = const()[name = tensor<string, []>("op_2560_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 256]> var_2560_cast = gelu(mode = var_2560_mode_0, x = var_2558_cast_1)[name = tensor<string, []>("op_2560_cast")];
            tensor<fp16, [2, 5120, 1, 256]> input_307_cast = mul(x = var_2558_cast_0, y = var_2560_cast)[name = tensor<string, []>("input_307_cast")];
            tensor<int32, [2]> var_2564 = const()[name = tensor<string, []>("op_2564"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2566 = const()[name = tensor<string, []>("op_2566"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2568_pad_type_0 = const()[name = tensor<string, []>("op_2568_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2568_pad_0 = const()[name = tensor<string, []>("op_2568_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_1_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, []>(443028544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447943808))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447944000)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2568_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2566, groups = var_2306, pad = var_2568_pad_0, pad_type = var_2568_pad_type_0, strides = var_2564, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_307_cast)[name = tensor<string, []>("op_2568_cast")];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_173_cast = add(x = var_2568_cast, y = inputs_47_cast)[name = tensor<string, []>("hidden_states_173_cast")];
            tensor<int32, [4]> var_2570 = const()[name = tensor<string, []>("op_2570"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> input_309_cast = reshape(shape = var_2570, x = hidden_states_173_cast)[name = tensor<string, []>("input_309_cast")];
            tensor<int32, [2]> var_2574 = const()[name = tensor<string, []>("op_2574"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2576 = const()[name = tensor<string, []>("op_2576"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_175_pad_type_0 = const()[name = tensor<string, []>("hidden_states_175_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_175_pad_0 = const()[name = tensor<string, []>("hidden_states_175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_1_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, []>(447946624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449175488))), name = tensor<string, []>("up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449175680)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_175_cast = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_2576, groups = var_2306, pad = hidden_states_175_pad_0, pad_type = hidden_states_175_pad_type_0, strides = var_2574, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_309_cast)[name = tensor<string, []>("hidden_states_175_cast")];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_177_cast = add(x = hidden_states_175_cast, y = hidden_states_163_cast)[name = tensor<string, []>("hidden_states_177_cast")];
            tensor<bool, []> input_311_interleave_0 = const()[name = tensor<string, []>("input_311_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 16, 16]> input_311_cast = concat(axis = var_2306, interleave = input_311_interleave_0, values = (hidden_states_177_cast, input_143_cast))[name = tensor<string, []>("input_311_cast")];
            tensor<int32, [5]> reshape_144_shape_0 = const()[name = tensor<string, []>("reshape_144_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 16, 16])];
            tensor<fp16, [2, 32, 80, 16, 16]> reshape_144_cast = reshape(shape = reshape_144_shape_0, x = input_311_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, 80, 16, 16]> sub_72_cast = sub(x = reshape_144_cast, y = reduce_mean_108_cast)[name = tensor<string, []>("sub_72_cast")];
            tensor<fp16, [2, 32, 80, 16, 16]> 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, 80, 16, 16]> 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, 2560, 16, 16])];
            tensor<fp16, [2, 2560, 16, 16]> reshape_145_cast = reshape(shape = reshape_145_shape_0, x = real_div_36_cast)[name = tensor<string, []>("reshape_145_cast")];
            tensor<fp16, [2560]> add_73_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449178304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449180288))), name = tensor<string, []>("add_73_gamma_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2560]> add_73_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449180480))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449182464))), name = tensor<string, []>("add_73_beta_0_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            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, 2560, 16, 16]> add_73_cast = batch_norm(beta = add_73_beta_0_to_fp16_palettized, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_145_cast)[name = tensor<string, []>("add_73_cast")];
            tensor<fp16, [2, 2560, 16, 16]> input_315_cast = silu(x = add_73_cast)[name = tensor<string, []>("input_315_cast")];
            tensor<int32, [2]> var_2594 = const()[name = tensor<string, []>("op_2594"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2596 = const()[name = tensor<string, []>("op_2596"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_179_pad_type_0 = const()[name = tensor<string, []>("hidden_states_179_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_179_pad_0 = const()[name = tensor<string, []>("hidden_states_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 2560, 3, 3]> up_blocks_1_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, []>(449182656))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(471301120))), name = tensor<string, []>("up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 3, 3])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(471301312)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_179_cast = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_2596, groups = var_2306, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = var_2594, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_315_cast)[name = tensor<string, []>("hidden_states_179_cast")];
            tensor<int32, [2]> var_2602 = const()[name = tensor<string, []>("op_2602"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2604 = const()[name = tensor<string, []>("op_2604"), 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, [1280, 1280, 1, 1]> up_blocks_1_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, []>(471303936))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(472532800))), name = tensor<string, []>("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(472532992)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_29_cast = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2604, groups = var_2306, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_2602, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_29_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_319_cast = add(x = hidden_states_179_cast, y = temb_29_cast)[name = tensor<string, []>("input_319_cast")];
            tensor<int32, [5]> reshape_148_shape_0 = const()[name = tensor<string, []>("reshape_148_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_148_cast = reshape(shape = reshape_148_shape_0, x = input_319_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, 40, 16, 16]> sub_74_cast = sub(x = reshape_148_cast, y = reduce_mean_111_cast)[name = tensor<string, []>("sub_74_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, 40, 16, 16]> 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, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_149_cast = reshape(shape = reshape_149_shape_0, x = real_div_37_cast)[name = tensor<string, []>("reshape_149_cast")];
            tensor<fp16, [1280]> add_75_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_75_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(472535616)))];
            tensor<fp16, [1280]> add_75_beta_0_to_fp16 = const()[name = tensor<string, []>("add_75_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(472538240)))];
            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, 1280, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_149_cast)[name = tensor<string, []>("add_75_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_323_cast = silu(x = add_75_cast)[name = tensor<string, []>("input_323_cast")];
            tensor<int32, [2]> var_2614 = const()[name = tensor<string, []>("op_2614"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2616 = const()[name = tensor<string, []>("op_2616"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_181_pad_type_0 = const()[name = tensor<string, []>("hidden_states_181_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_181_pad_0 = const()[name = tensor<string, []>("hidden_states_181_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_1_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, []>(472540864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483600128))), name = tensor<string, []>("up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483600320)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_181_cast = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_2616, groups = var_2306, pad = hidden_states_181_pad_0, pad_type = hidden_states_181_pad_type_0, strides = var_2614, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_323_cast)[name = tensor<string, []>("hidden_states_181_cast")];
            tensor<int32, [2]> var_2621 = const()[name = tensor<string, []>("op_2621"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2623 = const()[name = tensor<string, []>("op_2623"), 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, [1280, 2560, 1, 1]> up_blocks_1_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, []>(483602944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486060608))), name = tensor<string, []>("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 2560, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486060800)))];
            tensor<fp16, [2, 1280, 16, 16]> x_13_cast = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2623, groups = var_2306, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_2621, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_311_cast)[name = tensor<string, []>("x_13_cast")];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_183_cast = add(x = x_13_cast, y = hidden_states_181_cast)[name = tensor<string, []>("hidden_states_183_cast")];
            tensor<int32, [5]> reshape_152_shape_0 = const()[name = tensor<string, []>("reshape_152_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_152_cast = reshape(shape = reshape_152_shape_0, x = hidden_states_183_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, 40, 16, 16]> sub_76_cast = sub(x = reshape_152_cast, y = reduce_mean_114_cast)[name = tensor<string, []>("sub_76_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, 40, 16, 16]> 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, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_153_cast = reshape(shape = reshape_153_shape_0, x = real_div_38_cast)[name = tensor<string, []>("reshape_153_cast")];
            tensor<fp16, [1280]> add_77_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_77_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486063424)))];
            tensor<fp16, [1280]> add_77_beta_0_to_fp16 = const()[name = tensor<string, []>("add_77_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486066048)))];
            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, 1280, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_153_cast)[name = tensor<string, []>("add_77_cast")];
            tensor<int32, [2]> var_2643 = const()[name = tensor<string, []>("op_2643"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2645 = const()[name = tensor<string, []>("op_2645"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_185_pad_type_0 = const()[name = tensor<string, []>("hidden_states_185_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_185_pad_0 = const()[name = tensor<string, []>("hidden_states_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_1_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, []>(486068672))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487297536))), name = tensor<string, []>("up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487297728)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_185_cast = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_2645, groups = var_2306, pad = hidden_states_185_pad_0, pad_type = hidden_states_185_pad_type_0, strides = var_2643, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_77_cast)[name = tensor<string, []>("hidden_states_185_cast")];
            tensor<int32, [4]> var_2650 = const()[name = tensor<string, []>("op_2650"), val = tensor<int32, [4]>([2, 1280, 1, 256])];
            tensor<fp16, [2, 1280, 1, 256]> inputs_49_cast = reshape(shape = var_2650, x = hidden_states_185_cast)[name = tensor<string, []>("inputs_49_cast")];
            tensor<int32, [1]> var_2660 = const()[name = tensor<string, []>("op_2660"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_49_cast = reduce_mean(axes = var_2660, keep_dims = var_2301, x = inputs_49_cast)[name = tensor<string, []>("channels_mean_49_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2664 = const()[name = tensor<string, []>("op_2664"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2665_cast = reduce_mean(axes = var_2664, keep_dims = var_2301, x = zero_mean_sq_49_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, 256]> var_2667_cast = add(x = var_2665_cast, y = var_2666_to_fp16)[name = tensor<string, []>("op_2667_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, 256]> denom_49_cast = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2667_cast)[name = tensor<string, []>("denom_49_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_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, []>(487300352)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2672_cast = add(x = out_49_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, []>(487302976)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_187_cast = mul(x = var_2672_cast, y = var_2674_to_fp16)[name = tensor<string, []>("hidden_states_187_cast")];
            tensor<int32, [2]> var_2681 = const()[name = tensor<string, []>("op_2681"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2683 = const()[name = tensor<string, []>("op_2683"), 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]> up_blocks_1_attentions_1_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, []>(487305600))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(488534464))), name = tensor<string, []>("up_blocks_1_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, 256]> q_33_cast = conv(dilations = var_2683, groups = var_2306, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2681, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor<string, []>("q_33_cast")];
            tensor<int32, [2]> var_2687 = const()[name = tensor<string, []>("op_2687"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2689 = const()[name = tensor<string, []>("op_2689"), 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]> up_blocks_1_attentions_1_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, []>(488534656))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(489763520))), name = tensor<string, []>("up_blocks_1_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, 256]> k_33_cast = conv(dilations = var_2689, groups = var_2306, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2687, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor<string, []>("k_33_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, []> 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]> up_blocks_1_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, []>(489763712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490992576))), name = tensor<string, []>("up_blocks_1_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, 256]> v_33_cast = conv(dilations = var_2695, groups = var_2306, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2693, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor<string, []>("v_33_cast")];
            tensor<int32, [4]> var_2699 = const()[name = tensor<string, []>("op_2699"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2700_cast = reshape(shape = var_2699, x = q_33_cast)[name = tensor<string, []>("op_2700_cast")];
            tensor<int32, [4]> var_2701 = const()[name = tensor<string, []>("op_2701"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2702_cast = reshape(shape = var_2701, x = k_33_cast)[name = tensor<string, []>("op_2702_cast")];
            tensor<int32, [4]> var_2703 = const()[name = tensor<string, []>("op_2703"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2704_cast = reshape(shape = var_2703, x = v_33_cast)[name = tensor<string, []>("op_2704_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, 256, 256]> attn_weights_65_cast = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2700_cast, y = var_2702_cast)[name = tensor<string, []>("attn_weights_65_cast")];
            tensor<fp16, [2, 20, 256, 256]> attn_weights_67_cast = mul(x = attn_weights_65_cast, y = var_2297_to_fp16)[name = tensor<string, []>("attn_weights_67_cast")];
            tensor<fp16, [2, 20, 256, 256]> var_2708_cast = softmax(axis = var_2290, x = attn_weights_67_cast)[name = tensor<string, []>("op_2708_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, 256]> attn_33_cast = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2704_cast, y = var_2708_cast)[name = tensor<string, []>("attn_33_cast")];
            tensor<int32, [4]> var_2712 = const()[name = tensor<string, []>("op_2712"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_327_cast = reshape(shape = var_2712, x = attn_33_cast)[name = tensor<string, []>("input_327_cast")];
            tensor<int32, [2]> var_2717 = const()[name = tensor<string, []>("op_2717"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2719 = const()[name = tensor<string, []>("op_2719"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2721_pad_type_0 = const()[name = tensor<string, []>("op_2721_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2721_pad_0 = const()[name = tensor<string, []>("op_2721_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490992768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(492221632))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(492221824)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2721_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2719, groups = var_2306, pad = var_2721_pad_0, pad_type = var_2721_pad_type_0, strides = var_2717, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_327_cast)[name = tensor<string, []>("op_2721_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_51_cast = add(x = var_2721_cast, y = inputs_49_cast)[name = tensor<string, []>("inputs_51_cast")];
            tensor<int32, [1]> var_2725 = const()[name = tensor<string, []>("op_2725"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_51_cast = reduce_mean(axes = var_2725, keep_dims = var_2301, x = inputs_51_cast)[name = tensor<string, []>("channels_mean_51_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2729 = const()[name = tensor<string, []>("op_2729"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2730_cast = reduce_mean(axes = var_2729, keep_dims = var_2301, x = zero_mean_sq_51_cast)[name = tensor<string, []>("op_2730_cast")];
            tensor<fp16, []> var_2731_to_fp16 = const()[name = tensor<string, []>("op_2731_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_2732_cast = add(x = var_2730_cast, y = var_2731_to_fp16)[name = tensor<string, []>("op_2732_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, 256]> denom_51_cast = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2732_cast)[name = tensor<string, []>("denom_51_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_2736_to_fp16 = const()[name = tensor<string, []>("op_2736_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(492224448)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2737_cast = add(x = out_51_cast, y = var_2736_to_fp16)[name = tensor<string, []>("op_2737_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2739_to_fp16 = const()[name = tensor<string, []>("op_2739_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(492227072)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_189_cast = mul(x = var_2737_cast, y = var_2739_to_fp16)[name = tensor<string, []>("hidden_states_189_cast")];
            tensor<int32, [2]> var_2746 = const()[name = tensor<string, []>("op_2746"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2748 = const()[name = tensor<string, []>("op_2748"), 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]> up_blocks_1_attentions_1_transformer_blocks_0_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, []>(492229696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(493458560))), name = tensor<string, []>("up_blocks_1_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, 256]> q_35_cast = conv(dilations = var_2748, groups = var_2306, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2746, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_189_cast)[name = tensor<string, []>("q_35_cast")];
            tensor<int32, [2]> var_2752 = const()[name = tensor<string, []>("op_2752"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2754 = const()[name = tensor<string, []>("op_2754"), 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, 1024, 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, []>(493458752))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494441856))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_35_cast = conv(dilations = var_2754, groups = var_2306, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2752, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_35_cast")];
            tensor<int32, [2]> var_2758 = const()[name = tensor<string, []>("op_2758"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2760 = const()[name = tensor<string, []>("op_2760"), 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, 1024, 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, []>(494442048))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(495425152))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_35_cast = conv(dilations = var_2760, groups = var_2306, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2758, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_35_cast")];
            tensor<int32, [4]> var_2764 = const()[name = tensor<string, []>("op_2764"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2765_cast = reshape(shape = var_2764, x = q_35_cast)[name = tensor<string, []>("op_2765_cast")];
            tensor<int32, [4]> var_2766 = const()[name = tensor<string, []>("op_2766"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2767_cast = reshape(shape = var_2766, x = k_35_cast)[name = tensor<string, []>("op_2767_cast")];
            tensor<int32, [4]> var_2768 = const()[name = tensor<string, []>("op_2768"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2769_cast = reshape(shape = var_2768, x = v_35_cast)[name = tensor<string, []>("op_2769_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, 256, 77]> attn_weights_69_cast = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2765_cast, y = var_2767_cast)[name = tensor<string, []>("attn_weights_69_cast")];
            tensor<fp16, [2, 20, 256, 77]> attn_weights_71_cast = mul(x = attn_weights_69_cast, y = var_2297_to_fp16)[name = tensor<string, []>("attn_weights_71_cast")];
            tensor<fp16, [2, 20, 256, 77]> var_2773_cast = softmax(axis = var_2290, x = attn_weights_71_cast)[name = tensor<string, []>("op_2773_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, 256]> attn_35_cast = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2769_cast, y = var_2773_cast)[name = tensor<string, []>("attn_35_cast")];
            tensor<int32, [4]> var_2777 = const()[name = tensor<string, []>("op_2777"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_329_cast = reshape(shape = var_2777, x = attn_35_cast)[name = tensor<string, []>("input_329_cast")];
            tensor<int32, [2]> var_2782 = const()[name = tensor<string, []>("op_2782"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2784 = const()[name = tensor<string, []>("op_2784"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2786_pad_type_0 = const()[name = tensor<string, []>("op_2786_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2786_pad_0 = const()[name = tensor<string, []>("op_2786_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(495425344))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496654208))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496654400)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2786_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2784, groups = var_2306, pad = var_2786_pad_0, pad_type = var_2786_pad_type_0, strides = var_2782, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_329_cast)[name = tensor<string, []>("op_2786_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_53_cast = add(x = var_2786_cast, y = inputs_51_cast)[name = tensor<string, []>("inputs_53_cast")];
            tensor<int32, [1]> var_2790 = const()[name = tensor<string, []>("op_2790"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_53_cast = reduce_mean(axes = var_2790, keep_dims = var_2301, x = inputs_53_cast)[name = tensor<string, []>("channels_mean_53_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2794 = const()[name = tensor<string, []>("op_2794"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2795_cast = reduce_mean(axes = var_2794, keep_dims = var_2301, x = zero_mean_sq_53_cast)[name = tensor<string, []>("op_2795_cast")];
            tensor<fp16, []> var_2796_to_fp16 = const()[name = tensor<string, []>("op_2796_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_2797_cast = add(x = var_2795_cast, y = var_2796_to_fp16)[name = tensor<string, []>("op_2797_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, 256]> denom_53_cast = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2797_cast)[name = tensor<string, []>("denom_53_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_2801_to_fp16 = const()[name = tensor<string, []>("op_2801_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496657024)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2802_cast = add(x = out_53_cast, y = var_2801_to_fp16)[name = tensor<string, []>("op_2802_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2804_to_fp16 = const()[name = tensor<string, []>("op_2804_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496659648)))];
            tensor<fp16, [2, 1280, 1, 256]> input_331_cast = mul(x = var_2802_cast, y = var_2804_to_fp16)[name = tensor<string, []>("input_331_cast")];
            tensor<int32, [2]> var_2812 = const()[name = tensor<string, []>("op_2812"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2814 = const()[name = tensor<string, []>("op_2814"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2816_pad_type_0 = const()[name = tensor<string, []>("op_2816_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2816_pad_0 = const()[name = tensor<string, []>("op_2816_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 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, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496662272))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506492736))), name = tensor<string, []>("up_blocks_1_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_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506492928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506500672))), name = tensor<string, []>("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([10240])];
            tensor<fp16, [2, 10240, 1, 256]> var_2816_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2814, groups = var_2306, pad = var_2816_pad_0, pad_type = var_2816_pad_type_0, strides = var_2812, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_331_cast)[name = tensor<string, []>("op_2816_cast")];
            tensor<int32, [2]> var_2817_split_sizes_0 = const()[name = tensor<string, []>("op_2817_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2817_axis_0 = const()[name = tensor<string, []>("op_2817_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 256]> var_2817_cast_0, tensor<fp16, [2, 5120, 1, 256]> var_2817_cast_1 = split(axis = var_2817_axis_0, split_sizes = var_2817_split_sizes_0, x = var_2816_cast)[name = tensor<string, []>("op_2817_cast")];
            tensor<string, []> var_2819_mode_0 = const()[name = tensor<string, []>("op_2819_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 256]> var_2819_cast = gelu(mode = var_2819_mode_0, x = var_2817_cast_1)[name = tensor<string, []>("op_2819_cast")];
            tensor<fp16, [2, 5120, 1, 256]> input_333_cast = mul(x = var_2817_cast_0, y = var_2819_cast)[name = tensor<string, []>("input_333_cast")];
            tensor<int32, [2]> var_2823 = const()[name = tensor<string, []>("op_2823"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2825 = const()[name = tensor<string, []>("op_2825"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2827_pad_type_0 = const()[name = tensor<string, []>("op_2827_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2827_pad_0 = const()[name = tensor<string, []>("op_2827_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_1_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, []>(506500864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511416128))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511416320)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2827_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2825, groups = var_2306, pad = var_2827_pad_0, pad_type = var_2827_pad_type_0, strides = var_2823, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_333_cast)[name = tensor<string, []>("op_2827_cast")];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_193_cast = add(x = var_2827_cast, y = inputs_53_cast)[name = tensor<string, []>("hidden_states_193_cast")];
            tensor<int32, [4]> var_2829 = const()[name = tensor<string, []>("op_2829"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> input_335_cast = reshape(shape = var_2829, x = hidden_states_193_cast)[name = tensor<string, []>("input_335_cast")];
            tensor<int32, [2]> var_2833 = const()[name = tensor<string, []>("op_2833"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2835 = const()[name = tensor<string, []>("op_2835"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_195_pad_type_0 = const()[name = tensor<string, []>("hidden_states_195_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_195_pad_0 = const()[name = tensor<string, []>("hidden_states_195_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_1_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, []>(511418944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512647808))), name = tensor<string, []>("up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512648000)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_195_cast = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_2835, groups = var_2306, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_2833, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_335_cast)[name = tensor<string, []>("hidden_states_195_cast")];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_197_cast = add(x = hidden_states_195_cast, y = hidden_states_183_cast)[name = tensor<string, []>("hidden_states_197_cast")];
            tensor<bool, []> input_337_interleave_0 = const()[name = tensor<string, []>("input_337_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 1920, 16, 16]> input_337_cast = concat(axis = var_2306, interleave = input_337_interleave_0, values = (hidden_states_197_cast, input_117_cast))[name = tensor<string, []>("input_337_cast")];
            tensor<int32, [5]> reshape_156_shape_0 = const()[name = tensor<string, []>("reshape_156_shape_0"), val = tensor<int32, [5]>([2, 32, 60, 16, 16])];
            tensor<fp16, [2, 32, 60, 16, 16]> reshape_156_cast = reshape(shape = reshape_156_shape_0, x = input_337_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, 60, 16, 16]> sub_78_cast = sub(x = reshape_156_cast, y = reduce_mean_117_cast)[name = tensor<string, []>("sub_78_cast")];
            tensor<fp16, [2, 32, 60, 16, 16]> 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, 60, 16, 16]> 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, 1920, 16, 16])];
            tensor<fp16, [2, 1920, 16, 16]> reshape_157_cast = reshape(shape = reshape_157_shape_0, x = real_div_39_cast)[name = tensor<string, []>("reshape_157_cast")];
            tensor<fp16, [1920]> add_79_mean_0_to_fp16 = const()[name = tensor<string, []>("add_79_mean_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512650624)))];
            tensor<fp16, [1920]> add_79_variance_0_to_fp16 = const()[name = tensor<string, []>("add_79_variance_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512654528)))];
            tensor<fp16, [1920]> add_79_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_79_gamma_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512658432)))];
            tensor<fp16, [1920]> add_79_beta_0_to_fp16 = const()[name = tensor<string, []>("add_79_beta_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512662336)))];
            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, 1920, 16, 16]> 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_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_157_cast)[name = tensor<string, []>("add_79_cast")];
            tensor<fp16, [2, 1920, 16, 16]> input_341_cast = silu(x = add_79_cast)[name = tensor<string, []>("input_341_cast")];
            tensor<int32, [2]> var_2853 = const()[name = tensor<string, []>("op_2853"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2855 = const()[name = tensor<string, []>("op_2855"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1920, 3, 3]> up_blocks_1_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, []>(512666240))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529255104))), name = tensor<string, []>("up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1920, 3, 3])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529255296)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_199_cast = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_2855, groups = var_2306, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_2853, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized, x = input_341_cast)[name = tensor<string, []>("hidden_states_199_cast")];
            tensor<int32, [2]> var_2861 = const()[name = tensor<string, []>("op_2861"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2863 = const()[name = tensor<string, []>("op_2863"), 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, [1280, 1280, 1, 1]> up_blocks_1_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, []>(529257920))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530486784))), name = tensor<string, []>("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530486976)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_31_cast = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2863, groups = var_2306, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_2861, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_31_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_345_cast = add(x = hidden_states_199_cast, y = temb_31_cast)[name = tensor<string, []>("input_345_cast")];
            tensor<int32, [5]> reshape_160_shape_0 = const()[name = tensor<string, []>("reshape_160_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_160_cast = reshape(shape = reshape_160_shape_0, x = input_345_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, 40, 16, 16]> sub_80_cast = sub(x = reshape_160_cast, y = reduce_mean_120_cast)[name = tensor<string, []>("sub_80_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, 40, 16, 16]> 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, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_161_cast = reshape(shape = reshape_161_shape_0, x = real_div_40_cast)[name = tensor<string, []>("reshape_161_cast")];
            tensor<fp16, [1280]> add_81_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_81_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530489600)))];
            tensor<fp16, [1280]> add_81_beta_0_to_fp16 = const()[name = tensor<string, []>("add_81_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530492224)))];
            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, 1280, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_161_cast)[name = tensor<string, []>("add_81_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_349_cast = silu(x = add_81_cast)[name = tensor<string, []>("input_349_cast")];
            tensor<int32, [2]> var_2873 = const()[name = tensor<string, []>("op_2873"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2875 = const()[name = tensor<string, []>("op_2875"), 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]> up_blocks_1_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, []>(530494848))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541554112))), name = tensor<string, []>("up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541554304)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_201_cast = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_2875, groups = var_2306, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_2873, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized, x = input_349_cast)[name = tensor<string, []>("hidden_states_201_cast")];
            tensor<int32, [2]> var_2880 = const()[name = tensor<string, []>("op_2880"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2882 = const()[name = tensor<string, []>("op_2882"), 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, [1280, 1920, 1, 1]> up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1843200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541556928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543400192))), name = tensor<string, []>("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1920, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543400384)))];
            tensor<fp16, [2, 1280, 16, 16]> x_15_cast = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2882, groups = var_2306, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_2880, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_337_cast)[name = tensor<string, []>("x_15_cast")];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_203_cast = add(x = x_15_cast, y = hidden_states_201_cast)[name = tensor<string, []>("hidden_states_203_cast")];
            tensor<int32, [5]> reshape_164_shape_0 = const()[name = tensor<string, []>("reshape_164_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_164_cast = reshape(shape = reshape_164_shape_0, x = hidden_states_203_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, 40, 16, 16]> sub_82_cast = sub(x = reshape_164_cast, y = reduce_mean_123_cast)[name = tensor<string, []>("sub_82_cast")];
            tensor<fp16, [2, 32, 40, 16, 16]> 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, []>(0x1.1p-20)];
            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, 40, 16, 16]> 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, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_165_cast = reshape(shape = reshape_165_shape_0, x = real_div_41_cast)[name = tensor<string, []>("reshape_165_cast")];
            tensor<fp16, [1280]> add_83_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_83_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543403008)))];
            tensor<fp16, [1280]> add_83_beta_0_to_fp16 = const()[name = tensor<string, []>("add_83_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543405632)))];
            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, 1280, 16, 16]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_165_cast)[name = tensor<string, []>("add_83_cast")];
            tensor<int32, [2]> var_2902 = const()[name = tensor<string, []>("op_2902"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2904 = const()[name = tensor<string, []>("op_2904"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_205_pad_type_0 = const()[name = tensor<string, []>("hidden_states_205_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_205_pad_0 = const()[name = tensor<string, []>("hidden_states_205_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_1_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, []>(543408256))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544637120))), name = tensor<string, []>("up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544637312)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_205_cast = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_2904, groups = var_2306, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_2902, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized, x = add_83_cast)[name = tensor<string, []>("hidden_states_205_cast")];
            tensor<int32, [4]> var_2909 = const()[name = tensor<string, []>("op_2909"), val = tensor<int32, [4]>([2, 1280, 1, 256])];
            tensor<fp16, [2, 1280, 1, 256]> inputs_55_cast = reshape(shape = var_2909, x = hidden_states_205_cast)[name = tensor<string, []>("inputs_55_cast")];
            tensor<int32, [1]> var_2919 = const()[name = tensor<string, []>("op_2919"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_55_cast = reduce_mean(axes = var_2919, keep_dims = var_2301, x = inputs_55_cast)[name = tensor<string, []>("channels_mean_55_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2923 = const()[name = tensor<string, []>("op_2923"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2924_cast = reduce_mean(axes = var_2923, keep_dims = var_2301, x = zero_mean_sq_55_cast)[name = tensor<string, []>("op_2924_cast")];
            tensor<fp16, []> var_2925_to_fp16 = const()[name = tensor<string, []>("op_2925_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_2926_cast = add(x = var_2924_cast, y = var_2925_to_fp16)[name = tensor<string, []>("op_2926_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, 256]> denom_55_cast = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2926_cast)[name = tensor<string, []>("denom_55_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_2930_to_fp16 = const()[name = tensor<string, []>("op_2930_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544639936)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2931_cast = add(x = out_55_cast, y = var_2930_to_fp16)[name = tensor<string, []>("op_2931_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2933_to_fp16 = const()[name = tensor<string, []>("op_2933_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544642560)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_207_cast = mul(x = var_2931_cast, y = var_2933_to_fp16)[name = tensor<string, []>("hidden_states_207_cast")];
            tensor<int32, [2]> var_2940 = const()[name = tensor<string, []>("op_2940"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2942 = const()[name = tensor<string, []>("op_2942"), 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]> up_blocks_1_attentions_2_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, []>(544645184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545874048))), name = tensor<string, []>("up_blocks_1_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, 256]> q_37_cast = conv(dilations = var_2942, groups = var_2306, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2940, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor<string, []>("q_37_cast")];
            tensor<int32, [2]> var_2946 = const()[name = tensor<string, []>("op_2946"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2948 = const()[name = tensor<string, []>("op_2948"), 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]> up_blocks_1_attentions_2_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, []>(545874240))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547103104))), name = tensor<string, []>("up_blocks_1_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, 256]> k_37_cast = conv(dilations = var_2948, groups = var_2306, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2946, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor<string, []>("k_37_cast")];
            tensor<int32, [2]> var_2952 = const()[name = tensor<string, []>("op_2952"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2954 = const()[name = tensor<string, []>("op_2954"), 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]> up_blocks_1_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, []>(547103296))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548332160))), name = tensor<string, []>("up_blocks_1_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, 256]> v_37_cast = conv(dilations = var_2954, groups = var_2306, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2952, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor<string, []>("v_37_cast")];
            tensor<int32, [4]> var_2958 = const()[name = tensor<string, []>("op_2958"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2959_cast = reshape(shape = var_2958, x = q_37_cast)[name = tensor<string, []>("op_2959_cast")];
            tensor<int32, [4]> var_2960 = const()[name = tensor<string, []>("op_2960"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2961_cast = reshape(shape = var_2960, x = k_37_cast)[name = tensor<string, []>("op_2961_cast")];
            tensor<int32, [4]> var_2962 = const()[name = tensor<string, []>("op_2962"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_2963_cast = reshape(shape = var_2962, x = v_37_cast)[name = tensor<string, []>("op_2963_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, 256, 256]> attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2959_cast, y = var_2961_cast)[name = tensor<string, []>("attn_weights_73_cast")];
            tensor<fp16, [2, 20, 256, 256]> attn_weights_75_cast = mul(x = attn_weights_73_cast, y = var_2297_to_fp16)[name = tensor<string, []>("attn_weights_75_cast")];
            tensor<fp16, [2, 20, 256, 256]> var_2967_cast = softmax(axis = var_2290, x = attn_weights_75_cast)[name = tensor<string, []>("op_2967_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, 256]> attn_37_cast = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2963_cast, y = var_2967_cast)[name = tensor<string, []>("attn_37_cast")];
            tensor<int32, [4]> var_2971 = const()[name = tensor<string, []>("op_2971"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_353_cast = reshape(shape = var_2971, x = attn_37_cast)[name = tensor<string, []>("input_353_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, []> var_2980_pad_type_0 = const()[name = tensor<string, []>("op_2980_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2980_pad_0 = const()[name = tensor<string, []>("op_2980_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548332352))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549561216))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549561408)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2980_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2978, groups = var_2306, pad = var_2980_pad_0, pad_type = var_2980_pad_type_0, strides = var_2976, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_353_cast)[name = tensor<string, []>("op_2980_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_57_cast = add(x = var_2980_cast, y = inputs_55_cast)[name = tensor<string, []>("inputs_57_cast")];
            tensor<int32, [1]> var_2984 = const()[name = tensor<string, []>("op_2984"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_57_cast = reduce_mean(axes = var_2984, keep_dims = var_2301, x = inputs_57_cast)[name = tensor<string, []>("channels_mean_57_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_2988 = const()[name = tensor<string, []>("op_2988"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_2989_cast = reduce_mean(axes = var_2988, keep_dims = var_2301, x = zero_mean_sq_57_cast)[name = tensor<string, []>("op_2989_cast")];
            tensor<fp16, []> var_2990_to_fp16 = const()[name = tensor<string, []>("op_2990_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_2991_cast = add(x = var_2989_cast, y = var_2990_to_fp16)[name = tensor<string, []>("op_2991_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, 256]> denom_57_cast = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2991_cast)[name = tensor<string, []>("denom_57_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_2995_to_fp16 = const()[name = tensor<string, []>("op_2995_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549564032)))];
            tensor<fp16, [2, 1280, 1, 256]> var_2996_cast = add(x = out_57_cast, y = var_2995_to_fp16)[name = tensor<string, []>("op_2996_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_2998_to_fp16 = const()[name = tensor<string, []>("op_2998_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(549566656)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_209_cast = mul(x = var_2996_cast, y = var_2998_to_fp16)[name = tensor<string, []>("hidden_states_209_cast")];
            tensor<int32, [2]> var_3005 = const()[name = tensor<string, []>("op_3005"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3007 = const()[name = tensor<string, []>("op_3007"), 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]> up_blocks_1_attentions_2_transformer_blocks_0_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, []>(549569280))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550798144))), name = tensor<string, []>("up_blocks_1_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, 256]> q_39_cast = conv(dilations = var_3007, groups = var_2306, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_3005, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_209_cast)[name = tensor<string, []>("q_39_cast")];
            tensor<int32, [2]> var_3011 = const()[name = tensor<string, []>("op_3011"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3013 = const()[name = tensor<string, []>("op_3013"), 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, 1024, 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, []>(550798336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551781440))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> k_39_cast = conv(dilations = var_3013, groups = var_2306, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_3011, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_39_cast")];
            tensor<int32, [2]> var_3017 = const()[name = tensor<string, []>("op_3017"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3019 = const()[name = tensor<string, []>("op_3019"), 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, 1024, 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, []>(551781632))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552764736))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1024, 1, 1])];
            tensor<fp16, [2, 1280, 1, 77]> v_39_cast = conv(dilations = var_3019, groups = var_2306, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_3017, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_39_cast")];
            tensor<int32, [4]> var_3023 = const()[name = tensor<string, []>("op_3023"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_3024_cast = reshape(shape = var_3023, x = q_39_cast)[name = tensor<string, []>("op_3024_cast")];
            tensor<int32, [4]> var_3025 = const()[name = tensor<string, []>("op_3025"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3026_cast = reshape(shape = var_3025, x = k_39_cast)[name = tensor<string, []>("op_3026_cast")];
            tensor<int32, [4]> var_3027 = const()[name = tensor<string, []>("op_3027"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_3028_cast = reshape(shape = var_3027, x = v_39_cast)[name = tensor<string, []>("op_3028_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, 256, 77]> attn_weights_77_cast = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_3024_cast, y = var_3026_cast)[name = tensor<string, []>("attn_weights_77_cast")];
            tensor<fp16, [2, 20, 256, 77]> attn_weights_79_cast = mul(x = attn_weights_77_cast, y = var_2297_to_fp16)[name = tensor<string, []>("attn_weights_79_cast")];
            tensor<fp16, [2, 20, 256, 77]> var_3032_cast = softmax(axis = var_2290, x = attn_weights_79_cast)[name = tensor<string, []>("op_3032_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, 256]> attn_39_cast = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_3028_cast, y = var_3032_cast)[name = tensor<string, []>("attn_39_cast")];
            tensor<int32, [4]> var_3036 = const()[name = tensor<string, []>("op_3036"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_355_cast = reshape(shape = var_3036, x = attn_39_cast)[name = tensor<string, []>("input_355_cast")];
            tensor<int32, [2]> var_3041 = const()[name = tensor<string, []>("op_3041"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3043 = const()[name = tensor<string, []>("op_3043"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3045_pad_type_0 = const()[name = tensor<string, []>("op_3045_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3045_pad_0 = const()[name = tensor<string, []>("op_3045_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 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, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552764928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553993792))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553993984)))];
            tensor<fp16, [2, 1280, 1, 256]> var_3045_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3043, groups = var_2306, pad = var_3045_pad_0, pad_type = var_3045_pad_type_0, strides = var_3041, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_355_cast)[name = tensor<string, []>("op_3045_cast")];
            tensor<fp16, [2, 1280, 1, 256]> inputs_59_cast = add(x = var_3045_cast, y = inputs_57_cast)[name = tensor<string, []>("inputs_59_cast")];
            tensor<int32, [1]> var_3049 = const()[name = tensor<string, []>("op_3049"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_59_cast = reduce_mean(axes = var_3049, keep_dims = var_2301, x = inputs_59_cast)[name = tensor<string, []>("channels_mean_59_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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, 256]> 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_3053 = const()[name = tensor<string, []>("op_3053"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_3054_cast = reduce_mean(axes = var_3053, keep_dims = var_2301, x = zero_mean_sq_59_cast)[name = tensor<string, []>("op_3054_cast")];
            tensor<fp16, []> var_3055_to_fp16 = const()[name = tensor<string, []>("op_3055_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 256]> var_3056_cast = add(x = var_3054_cast, y = var_3055_to_fp16)[name = tensor<string, []>("op_3056_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, 256]> denom_59_cast = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_3056_cast)[name = tensor<string, []>("denom_59_cast")];
            tensor<fp16, [2, 1280, 1, 256]> 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_3060_to_fp16 = const()[name = tensor<string, []>("op_3060_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553996608)))];
            tensor<fp16, [2, 1280, 1, 256]> var_3061_cast = add(x = out_59_cast, y = var_3060_to_fp16)[name = tensor<string, []>("op_3061_cast")];
            tensor<fp16, [1, 1280, 1, 1]> var_3063_to_fp16 = const()[name = tensor<string, []>("op_3063_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553999232)))];
            tensor<fp16, [2, 1280, 1, 256]> input_357_cast = mul(x = var_3061_cast, y = var_3063_to_fp16)[name = tensor<string, []>("input_357_cast")];
            tensor<int32, [2]> var_3071 = const()[name = tensor<string, []>("op_3071"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3073 = const()[name = tensor<string, []>("op_3073"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3075_pad_type_0 = const()[name = tensor<string, []>("op_3075_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3075_pad_0 = const()[name = tensor<string, []>("op_3075_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 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, [9830400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554001856))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563832320))), name = tensor<string, []>("up_blocks_1_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_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [7680]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563832512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563840256))), name = tensor<string, []>("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([10240])];
            tensor<fp16, [2, 10240, 1, 256]> var_3075_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3073, groups = var_2306, pad = var_3075_pad_0, pad_type = var_3075_pad_type_0, strides = var_3071, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_357_cast)[name = tensor<string, []>("op_3075_cast")];
            tensor<int32, [2]> var_3076_split_sizes_0 = const()[name = tensor<string, []>("op_3076_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_3076_axis_0 = const()[name = tensor<string, []>("op_3076_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 256]> var_3076_cast_0, tensor<fp16, [2, 5120, 1, 256]> var_3076_cast_1 = split(axis = var_3076_axis_0, split_sizes = var_3076_split_sizes_0, x = var_3075_cast)[name = tensor<string, []>("op_3076_cast")];
            tensor<string, []> var_3078_mode_0 = const()[name = tensor<string, []>("op_3078_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 256]> var_3078_cast = gelu(mode = var_3078_mode_0, x = var_3076_cast_1)[name = tensor<string, []>("op_3078_cast")];
            tensor<fp16, [2, 5120, 1, 256]> input_359_cast = mul(x = var_3076_cast_0, y = var_3078_cast)[name = tensor<string, []>("input_359_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, []> var_3086_pad_type_0 = const()[name = tensor<string, []>("op_3086_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3086_pad_0 = const()[name = tensor<string, []>("op_3086_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> up_blocks_1_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, []>(563840448))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568755712))), name = tensor<string, []>("up_blocks_1_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_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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568755904)))];
            tensor<fp16, [2, 1280, 1, 256]> var_3086_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3084, groups = var_2306, pad = var_3086_pad_0, pad_type = var_3086_pad_type_0, strides = var_3082, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_359_cast)[name = tensor<string, []>("op_3086_cast")];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_213_cast = add(x = var_3086_cast, y = inputs_59_cast)[name = tensor<string, []>("hidden_states_213_cast")];
            tensor<int32, [4]> var_3088 = const()[name = tensor<string, []>("op_3088"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> input_361_cast = reshape(shape = var_3088, x = hidden_states_213_cast)[name = tensor<string, []>("input_361_cast")];
            tensor<int32, [2]> var_3092 = const()[name = tensor<string, []>("op_3092"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3094 = const()[name = tensor<string, []>("op_3094"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_215_pad_type_0 = const()[name = tensor<string, []>("hidden_states_215_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_215_pad_0 = const()[name = tensor<string, []>("hidden_states_215_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_1_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, []>(568758528))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(569987392))), name = tensor<string, []>("up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 1, 1])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(569987584)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_215_cast = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_3094, groups = var_2306, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_3092, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized, x = input_361_cast)[name = tensor<string, []>("hidden_states_215_cast")];
            tensor<fp16, [2, 1280, 16, 16]> input_363_cast = add(x = hidden_states_215_cast, y = hidden_states_203_cast)[name = tensor<string, []>("input_363_cast")];
            tensor<fp32, []> input_365_scale_factor_height_0 = const()[name = tensor<string, []>("input_365_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, []> input_365_scale_factor_width_0 = const()[name = tensor<string, []>("input_365_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp16, [2, 1280, 32, 32]> input_365_cast = upsample_nearest_neighbor(scale_factor_height = input_365_scale_factor_height_0, scale_factor_width = input_365_scale_factor_width_0, x = input_363_cast)[name = tensor<string, []>("input_365_cast")];
            tensor<int32, [2]> var_3103 = const()[name = tensor<string, []>("op_3103"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3105 = const()[name = tensor<string, []>("op_3105"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_217_pad_type_0 = const()[name = tensor<string, []>("hidden_states_217_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = tensor<string, []>("hidden_states_217_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11059200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(569990208))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581049472))), name = tensor<string, []>("up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([1280, 1280, 3, 3])];
            tensor<fp16, [1280]> 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, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581049664)))];
            tensor<fp16, [2, 1280, 32, 32]> hidden_states_217_cast = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_3105, groups = var_2306, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_3103, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized, x = input_365_cast)[name = tensor<string, []>("hidden_states_217_cast")];
            tensor<int32, []> var_3110 = const()[name = tensor<string, []>("op_3110"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_3121 = const()[name = tensor<string, []>("op_3121"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_3126 = const()[name = tensor<string, []>("op_3126"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_367_interleave_0 = const()[name = tensor<string, []>("input_367_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 1920, 32, 32]> input_367_cast = concat(axis = var_3126, interleave = input_367_interleave_0, values = (hidden_states_217_cast, input_115_cast))[name = tensor<string, []>("input_367_cast")];
            tensor<int32, [5]> reshape_168_shape_0 = const()[name = tensor<string, []>("reshape_168_shape_0"), val = tensor<int32, [5]>([2, 32, 60, 32, 32])];
            tensor<fp16, [2, 32, 60, 32, 32]> reshape_168_cast = reshape(shape = reshape_168_shape_0, x = input_367_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, 60, 32, 32]> sub_84_cast = sub(x = reshape_168_cast, y = reduce_mean_126_cast)[name = tensor<string, []>("sub_84_cast")];
            tensor<fp16, [2, 32, 60, 32, 32]> 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, 60, 32, 32]> 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, 1920, 32, 32])];
            tensor<fp16, [2, 1920, 32, 32]> reshape_169_cast = reshape(shape = reshape_169_shape_0, x = real_div_42_cast)[name = tensor<string, []>("reshape_169_cast")];
            tensor<fp16, [1920]> add_85_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_85_gamma_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581052288)))];
            tensor<fp16, [1920]> add_85_beta_0_to_fp16 = const()[name = tensor<string, []>("add_85_beta_0_to_fp16"), val = tensor<fp16, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581056192)))];
            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, 1920, 32, 32]> 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_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_169_cast)[name = tensor<string, []>("add_85_cast")];
            tensor<fp16, [2, 1920, 32, 32]> input_371_cast = silu(x = add_85_cast)[name = tensor<string, []>("input_371_cast")];
            tensor<int32, [2]> var_3155 = const()[name = tensor<string, []>("op_3155"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3157 = const()[name = tensor<string, []>("op_3157"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_219_pad_type_0 = const()[name = tensor<string, []>("hidden_states_219_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_219_pad_0 = const()[name = tensor<string, []>("hidden_states_219_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 1920, 3, 3]> up_blocks_2_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, []>(581060096))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589354560))), name = tensor<string, []>("up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1920, 3, 3])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589354752)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_219_cast = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_3157, groups = var_3126, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_3155, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_371_cast)[name = tensor<string, []>("hidden_states_219_cast")];
            tensor<int32, [2]> var_3163 = const()[name = tensor<string, []>("op_3163"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3165 = const()[name = tensor<string, []>("op_3165"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_33_pad_type_0 = const()[name = tensor<string, []>("temb_33_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_33_pad_0 = const()[name = tensor<string, []>("temb_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> up_blocks_2_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, []>(589356096))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589970560))), name = tensor<string, []>("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589970752)))];
            tensor<fp16, [2, 640, 1, 1]> temb_33_cast = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3165, groups = var_3126, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_3163, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_33_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_375_cast = add(x = hidden_states_219_cast, y = temb_33_cast)[name = tensor<string, []>("input_375_cast")];
            tensor<int32, [5]> reshape_172_shape_0 = const()[name = tensor<string, []>("reshape_172_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_172_cast = reshape(shape = reshape_172_shape_0, x = input_375_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, 32, 32]> sub_86_cast = sub(x = reshape_172_cast, y = reduce_mean_129_cast)[name = tensor<string, []>("sub_86_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> 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, 32, 32]> 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, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> 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, []>(589972096)))];
            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, []>(589973440)))];
            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, 32, 32]> 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_173_cast)[name = tensor<string, []>("add_87_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_379_cast = silu(x = add_87_cast)[name = tensor<string, []>("input_379_cast")];
            tensor<int32, [2]> var_3175 = const()[name = tensor<string, []>("op_3175"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3177 = const()[name = tensor<string, []>("op_3177"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_221_pad_type_0 = const()[name = tensor<string, []>("hidden_states_221_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_221_pad_0 = const()[name = tensor<string, []>("hidden_states_221_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589974784))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592739648))), name = tensor<string, []>("up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592739840)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_221_cast = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_3177, groups = var_3126, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_3175, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_379_cast)[name = tensor<string, []>("hidden_states_221_cast")];
            tensor<int32, [2]> var_3182 = const()[name = tensor<string, []>("op_3182"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3184 = const()[name = tensor<string, []>("op_3184"), 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, [640, 1920, 1, 1]> up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [921600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592741184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593662848))), name = tensor<string, []>("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1920, 1, 1])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593663040)))];
            tensor<fp16, [2, 640, 32, 32]> x_17_cast = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_3184, groups = var_3126, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_3182, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_367_cast)[name = tensor<string, []>("x_17_cast")];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_223_cast = add(x = x_17_cast, y = hidden_states_221_cast)[name = tensor<string, []>("hidden_states_223_cast")];
            tensor<int32, [5]> reshape_176_shape_0 = const()[name = tensor<string, []>("reshape_176_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_176_cast = reshape(shape = reshape_176_shape_0, x = hidden_states_223_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, 20, 32, 32]> sub_88_cast = sub(x = reshape_176_cast, y = reduce_mean_132_cast)[name = tensor<string, []>("sub_88_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> 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, []>(0x1.1p-20)];
            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, 20, 32, 32]> 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, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_177_cast = reshape(shape = reshape_177_shape_0, x = real_div_44_cast)[name = tensor<string, []>("reshape_177_cast")];
            tensor<fp16, [640]> add_89_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_89_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593664384)))];
            tensor<fp16, [640]> add_89_beta_0_to_fp16 = const()[name = tensor<string, []>("add_89_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593665728)))];
            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, 640, 32, 32]> 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_177_cast)[name = tensor<string, []>("add_89_cast")];
            tensor<int32, [2]> var_3204 = const()[name = tensor<string, []>("op_3204"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3206 = const()[name = tensor<string, []>("op_3206"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_225_pad_type_0 = const()[name = tensor<string, []>("hidden_states_225_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_225_pad_0 = const()[name = tensor<string, []>("hidden_states_225_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593667072))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593974336))), name = tensor<string, []>("up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593974528)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_225_cast = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_3206, groups = var_3126, pad = hidden_states_225_pad_0, pad_type = hidden_states_225_pad_type_0, strides = var_3204, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_89_cast)[name = tensor<string, []>("hidden_states_225_cast")];
            tensor<int32, [4]> var_3211 = const()[name = tensor<string, []>("op_3211"), val = tensor<int32, [4]>([2, 640, 1, 1024])];
            tensor<fp16, [2, 640, 1, 1024]> inputs_61_cast = reshape(shape = var_3211, x = hidden_states_225_cast)[name = tensor<string, []>("inputs_61_cast")];
            tensor<int32, [1]> var_3221 = const()[name = tensor<string, []>("op_3221"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_61_cast = reduce_mean(axes = var_3221, keep_dims = var_3121, x = inputs_61_cast)[name = tensor<string, []>("channels_mean_61_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3225 = const()[name = tensor<string, []>("op_3225"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3226_cast = reduce_mean(axes = var_3225, keep_dims = var_3121, x = zero_mean_sq_61_cast)[name = tensor<string, []>("op_3226_cast")];
            tensor<fp16, []> var_3227_to_fp16 = const()[name = tensor<string, []>("op_3227_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3228_cast = add(x = var_3226_cast, y = var_3227_to_fp16)[name = tensor<string, []>("op_3228_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_3228_cast)[name = tensor<string, []>("denom_61_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_61_cast = mul(x = zero_mean_61_cast, y = denom_61_cast)[name = tensor<string, []>("out_61_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3232_to_fp16 = const()[name = tensor<string, []>("op_3232_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593975872)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3233_cast = add(x = out_61_cast, y = var_3232_to_fp16)[name = tensor<string, []>("op_3233_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3235_to_fp16 = const()[name = tensor<string, []>("op_3235_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593977216)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_227_cast = mul(x = var_3233_cast, y = var_3235_to_fp16)[name = tensor<string, []>("hidden_states_227_cast")];
            tensor<int32, [2]> var_3242 = const()[name = tensor<string, []>("op_3242"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3244 = const()[name = tensor<string, []>("op_3244"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(593978560))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594285824))), name = tensor<string, []>("up_blocks_2_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, 1024]> q_41_cast = conv(dilations = var_3244, groups = var_3126, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_3242, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor<string, []>("q_41_cast")];
            tensor<int32, [2]> var_3248 = const()[name = tensor<string, []>("op_3248"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3250 = const()[name = tensor<string, []>("op_3250"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(594286016))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594593280))), name = tensor<string, []>("up_blocks_2_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, 1024]> k_41_cast = conv(dilations = var_3250, groups = var_3126, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_3248, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor<string, []>("k_41_cast")];
            tensor<int32, [2]> var_3254 = const()[name = tensor<string, []>("op_3254"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3256 = const()[name = tensor<string, []>("op_3256"), 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, [640, 640, 1, 1]> up_blocks_2_attentions_0_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, []>(594593472))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594900736))), name = tensor<string, []>("up_blocks_2_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, 1024]> v_41_cast = conv(dilations = var_3256, groups = var_3126, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_3254, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor<string, []>("v_41_cast")];
            tensor<int32, [4]> var_3260 = const()[name = tensor<string, []>("op_3260"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3261_cast = reshape(shape = var_3260, x = q_41_cast)[name = tensor<string, []>("op_3261_cast")];
            tensor<int32, [4]> var_3262 = const()[name = tensor<string, []>("op_3262"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3263_cast = reshape(shape = var_3262, x = k_41_cast)[name = tensor<string, []>("op_3263_cast")];
            tensor<int32, [4]> var_3264 = const()[name = tensor<string, []>("op_3264"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3265_cast = reshape(shape = var_3264, x = v_41_cast)[name = tensor<string, []>("op_3265_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, 10, 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_3261_cast, y = var_3263_cast)[name = tensor<string, []>("attn_weights_81_cast")];
            tensor<fp16, []> var_3117_to_fp16 = const()[name = tensor<string, []>("op_3117_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 10, 1024, 1024]> attn_weights_83_cast = mul(x = attn_weights_81_cast, y = var_3117_to_fp16)[name = tensor<string, []>("attn_weights_83_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> var_3269_cast = softmax(axis = var_3110, x = attn_weights_83_cast)[name = tensor<string, []>("op_3269_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, 10, 64, 1024]> attn_41_cast = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_3265_cast, y = var_3269_cast)[name = tensor<string, []>("attn_41_cast")];
            tensor<int32, [4]> var_3273 = const()[name = tensor<string, []>("op_3273"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_383_cast = reshape(shape = var_3273, x = attn_41_cast)[name = tensor<string, []>("input_383_cast")];
            tensor<int32, [2]> var_3278 = const()[name = tensor<string, []>("op_3278"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3280 = const()[name = tensor<string, []>("op_3280"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3282_pad_type_0 = const()[name = tensor<string, []>("op_3282_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3282_pad_0 = const()[name = tensor<string, []>("op_3282_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_0_transformer_blocks_0_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, []>(594900928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595208192))), name = tensor<string, []>("up_blocks_2_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_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(595208384)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3282_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3280, groups = var_3126, pad = var_3282_pad_0, pad_type = var_3282_pad_type_0, strides = var_3278, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_383_cast)[name = tensor<string, []>("op_3282_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_63_cast = add(x = var_3282_cast, y = inputs_61_cast)[name = tensor<string, []>("inputs_63_cast")];
            tensor<int32, [1]> var_3286 = const()[name = tensor<string, []>("op_3286"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_63_cast = reduce_mean(axes = var_3286, keep_dims = var_3121, x = inputs_63_cast)[name = tensor<string, []>("channels_mean_63_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3290 = const()[name = tensor<string, []>("op_3290"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3291_cast = reduce_mean(axes = var_3290, keep_dims = var_3121, x = zero_mean_sq_63_cast)[name = tensor<string, []>("op_3291_cast")];
            tensor<fp16, []> var_3292_to_fp16 = const()[name = tensor<string, []>("op_3292_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3293_cast = add(x = var_3291_cast, y = var_3292_to_fp16)[name = tensor<string, []>("op_3293_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_3293_cast)[name = tensor<string, []>("denom_63_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_63_cast = mul(x = zero_mean_63_cast, y = denom_63_cast)[name = tensor<string, []>("out_63_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3297_to_fp16 = const()[name = tensor<string, []>("op_3297_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595209728)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3298_cast = add(x = out_63_cast, y = var_3297_to_fp16)[name = tensor<string, []>("op_3298_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3300_to_fp16 = const()[name = tensor<string, []>("op_3300_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595211072)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_229_cast = mul(x = var_3298_cast, y = var_3300_to_fp16)[name = tensor<string, []>("hidden_states_229_cast")];
            tensor<int32, [2]> var_3307 = const()[name = tensor<string, []>("op_3307"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3309 = const()[name = tensor<string, []>("op_3309"), 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, [640, 640, 1, 1]> up_blocks_2_attentions_0_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, []>(595212416))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595519680))), name = tensor<string, []>("up_blocks_2_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, 1024]> q_43_cast = conv(dilations = var_3309, groups = var_3126, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_3307, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_229_cast)[name = tensor<string, []>("q_43_cast")];
            tensor<int32, [2]> var_3313 = const()[name = tensor<string, []>("op_3313"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3315 = const()[name = tensor<string, []>("op_3315"), 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, [640, 1024, 1, 1]> up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595519872))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596011456))), name = tensor<string, []>("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_43_cast = conv(dilations = var_3315, groups = var_3126, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_3313, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_43_cast")];
            tensor<int32, [2]> var_3319 = const()[name = tensor<string, []>("op_3319"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3321 = const()[name = tensor<string, []>("op_3321"), 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, [640, 1024, 1, 1]> up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596011648))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596503232))), name = tensor<string, []>("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_43_cast = conv(dilations = var_3321, groups = var_3126, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_3319, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_43_cast")];
            tensor<int32, [4]> var_3325 = const()[name = tensor<string, []>("op_3325"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3326_cast = reshape(shape = var_3325, x = q_43_cast)[name = tensor<string, []>("op_3326_cast")];
            tensor<int32, [4]> var_3327 = const()[name = tensor<string, []>("op_3327"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_3328_cast = reshape(shape = var_3327, x = k_43_cast)[name = tensor<string, []>("op_3328_cast")];
            tensor<int32, [4]> var_3329 = const()[name = tensor<string, []>("op_3329"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_3330_cast = reshape(shape = var_3329, x = v_43_cast)[name = tensor<string, []>("op_3330_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, 10, 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_3326_cast, y = var_3328_cast)[name = tensor<string, []>("attn_weights_85_cast")];
            tensor<fp16, [2, 10, 1024, 77]> attn_weights_87_cast = mul(x = attn_weights_85_cast, y = var_3117_to_fp16)[name = tensor<string, []>("attn_weights_87_cast")];
            tensor<fp16, [2, 10, 1024, 77]> var_3334_cast = softmax(axis = var_3110, x = attn_weights_87_cast)[name = tensor<string, []>("op_3334_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, 10, 64, 1024]> attn_43_cast = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_3330_cast, y = var_3334_cast)[name = tensor<string, []>("attn_43_cast")];
            tensor<int32, [4]> var_3338 = const()[name = tensor<string, []>("op_3338"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_385_cast = reshape(shape = var_3338, x = attn_43_cast)[name = tensor<string, []>("input_385_cast")];
            tensor<int32, [2]> var_3343 = const()[name = tensor<string, []>("op_3343"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3345 = const()[name = tensor<string, []>("op_3345"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3347_pad_type_0 = const()[name = tensor<string, []>("op_3347_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3347_pad_0 = const()[name = tensor<string, []>("op_3347_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_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, []>(596503424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596810688))), name = tensor<string, []>("up_blocks_2_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_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(596810880)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3347_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3345, groups = var_3126, pad = var_3347_pad_0, pad_type = var_3347_pad_type_0, strides = var_3343, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_385_cast)[name = tensor<string, []>("op_3347_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_65_cast = add(x = var_3347_cast, y = inputs_63_cast)[name = tensor<string, []>("inputs_65_cast")];
            tensor<int32, [1]> var_3351 = const()[name = tensor<string, []>("op_3351"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_65_cast = reduce_mean(axes = var_3351, keep_dims = var_3121, x = inputs_65_cast)[name = tensor<string, []>("channels_mean_65_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3355 = const()[name = tensor<string, []>("op_3355"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3356_cast = reduce_mean(axes = var_3355, keep_dims = var_3121, x = zero_mean_sq_65_cast)[name = tensor<string, []>("op_3356_cast")];
            tensor<fp16, []> var_3357_to_fp16 = const()[name = tensor<string, []>("op_3357_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3358_cast = add(x = var_3356_cast, y = var_3357_to_fp16)[name = tensor<string, []>("op_3358_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_3358_cast)[name = tensor<string, []>("denom_65_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_65_cast = mul(x = zero_mean_65_cast, y = denom_65_cast)[name = tensor<string, []>("out_65_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3362_to_fp16 = const()[name = tensor<string, []>("op_3362_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596812224)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3363_cast = add(x = out_65_cast, y = var_3362_to_fp16)[name = tensor<string, []>("op_3363_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3365_to_fp16 = const()[name = tensor<string, []>("op_3365_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596813568)))];
            tensor<fp16, [2, 640, 1, 1024]> input_387_cast = mul(x = var_3363_cast, y = var_3365_to_fp16)[name = tensor<string, []>("input_387_cast")];
            tensor<int32, [2]> var_3373 = const()[name = tensor<string, []>("op_3373"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3375 = const()[name = tensor<string, []>("op_3375"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3377_pad_type_0 = const()[name = tensor<string, []>("op_3377_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3377_pad_0 = const()[name = tensor<string, []>("op_3377_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(596814912))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599272576))), name = tensor<string, []>("up_blocks_2_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_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3840]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599272768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599276672))), name = tensor<string, []>("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [2, 5120, 1, 1024]> var_3377_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3375, groups = var_3126, pad = var_3377_pad_0, pad_type = var_3377_pad_type_0, strides = var_3373, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_387_cast)[name = tensor<string, []>("op_3377_cast")];
            tensor<int32, [2]> var_3378_split_sizes_0 = const()[name = tensor<string, []>("op_3378_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_3378_axis_0 = const()[name = tensor<string, []>("op_3378_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 1024]> var_3378_cast_0, tensor<fp16, [2, 2560, 1, 1024]> var_3378_cast_1 = split(axis = var_3378_axis_0, split_sizes = var_3378_split_sizes_0, x = var_3377_cast)[name = tensor<string, []>("op_3378_cast")];
            tensor<string, []> var_3380_mode_0 = const()[name = tensor<string, []>("op_3380_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 1024]> var_3380_cast = gelu(mode = var_3380_mode_0, x = var_3378_cast_1)[name = tensor<string, []>("op_3380_cast")];
            tensor<fp16, [2, 2560, 1, 1024]> input_389_cast = mul(x = var_3378_cast_0, y = var_3380_cast)[name = tensor<string, []>("input_389_cast")];
            tensor<int32, [2]> var_3384 = const()[name = tensor<string, []>("op_3384"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3386 = const()[name = tensor<string, []>("op_3386"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3388_pad_type_0 = const()[name = tensor<string, []>("op_3388_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3388_pad_0 = const()[name = tensor<string, []>("op_3388_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(599276864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600505728))), name = tensor<string, []>("up_blocks_2_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_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(600505920)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3388_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3386, groups = var_3126, pad = var_3388_pad_0, pad_type = var_3388_pad_type_0, strides = var_3384, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_389_cast)[name = tensor<string, []>("op_3388_cast")];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_233_cast = add(x = var_3388_cast, y = inputs_65_cast)[name = tensor<string, []>("hidden_states_233_cast")];
            tensor<int32, [4]> var_3390 = const()[name = tensor<string, []>("op_3390"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> input_391_cast = reshape(shape = var_3390, x = hidden_states_233_cast)[name = tensor<string, []>("input_391_cast")];
            tensor<int32, [2]> var_3394 = const()[name = tensor<string, []>("op_3394"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3396 = const()[name = tensor<string, []>("op_3396"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_235_pad_type_0 = const()[name = tensor<string, []>("hidden_states_235_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_235_pad_0 = const()[name = tensor<string, []>("hidden_states_235_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600507264))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600814528))), name = tensor<string, []>("up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600814720)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_235_cast = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3396, groups = var_3126, pad = hidden_states_235_pad_0, pad_type = hidden_states_235_pad_type_0, strides = var_3394, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_391_cast)[name = tensor<string, []>("hidden_states_235_cast")];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_237_cast = add(x = hidden_states_235_cast, y = hidden_states_223_cast)[name = tensor<string, []>("hidden_states_237_cast")];
            tensor<bool, []> input_393_interleave_0 = const()[name = tensor<string, []>("input_393_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 1280, 32, 32]> input_393_cast = concat(axis = var_3126, interleave = input_393_interleave_0, values = (hidden_states_237_cast, input_89_cast))[name = tensor<string, []>("input_393_cast")];
            tensor<int32, [5]> reshape_180_shape_0 = const()[name = tensor<string, []>("reshape_180_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 32, 32])];
            tensor<fp16, [2, 32, 40, 32, 32]> reshape_180_cast = reshape(shape = reshape_180_shape_0, x = input_393_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, 40, 32, 32]> sub_90_cast = sub(x = reshape_180_cast, y = reduce_mean_135_cast)[name = tensor<string, []>("sub_90_cast")];
            tensor<fp16, [2, 32, 40, 32, 32]> 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, 40, 32, 32]> 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, 1280, 32, 32])];
            tensor<fp16, [2, 1280, 32, 32]> reshape_181_cast = reshape(shape = reshape_181_shape_0, x = real_div_45_cast)[name = tensor<string, []>("reshape_181_cast")];
            tensor<fp16, [1280]> add_91_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_91_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600816064)))];
            tensor<fp16, [1280]> add_91_beta_0_to_fp16 = const()[name = tensor<string, []>("add_91_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600818688)))];
            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, 1280, 32, 32]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_181_cast)[name = tensor<string, []>("add_91_cast")];
            tensor<fp16, [2, 1280, 32, 32]> input_397_cast = silu(x = add_91_cast)[name = tensor<string, []>("input_397_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, []> hidden_states_239_pad_type_0 = const()[name = tensor<string, []>("hidden_states_239_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_239_pad_0 = const()[name = tensor<string, []>("hidden_states_239_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 1280, 3, 3]> up_blocks_2_resnets_1_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, []>(600821312))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606350976))), name = tensor<string, []>("up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 3, 3])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606351168)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_239_cast = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3416, groups = var_3126, pad = hidden_states_239_pad_0, pad_type = hidden_states_239_pad_type_0, strides = var_3414, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_397_cast)[name = tensor<string, []>("hidden_states_239_cast")];
            tensor<int32, [2]> var_3422 = const()[name = tensor<string, []>("op_3422"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3424 = const()[name = tensor<string, []>("op_3424"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_35_pad_type_0 = const()[name = tensor<string, []>("temb_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_35_pad_0 = const()[name = tensor<string, []>("temb_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> up_blocks_2_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, []>(606352512))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606966976))), name = tensor<string, []>("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606967168)))];
            tensor<fp16, [2, 640, 1, 1]> temb_35_cast = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3424, groups = var_3126, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_3422, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_35_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_401_cast = add(x = hidden_states_239_cast, y = temb_35_cast)[name = tensor<string, []>("input_401_cast")];
            tensor<int32, [5]> reshape_184_shape_0 = const()[name = tensor<string, []>("reshape_184_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_184_cast = reshape(shape = reshape_184_shape_0, x = input_401_cast)[name = tensor<string, []>("reshape_184_cast")];
            tensor<int32, [3]> reduce_mean_138_axes_0 = const()[name = tensor<string, []>("reduce_mean_138_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_138_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_138_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_138_cast = reduce_mean(axes = reduce_mean_138_axes_0, keep_dims = reduce_mean_138_keep_dims_0, x = reshape_184_cast)[name = tensor<string, []>("reduce_mean_138_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> sub_92_cast = sub(x = reshape_184_cast, y = reduce_mean_138_cast)[name = tensor<string, []>("sub_92_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> square_46_cast = square(x = sub_92_cast)[name = tensor<string, []>("square_46_cast")];
            tensor<int32, [3]> reduce_mean_140_axes_0 = const()[name = tensor<string, []>("reduce_mean_140_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_140_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_140_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_140_cast = reduce_mean(axes = reduce_mean_140_axes_0, keep_dims = reduce_mean_140_keep_dims_0, x = square_46_cast)[name = tensor<string, []>("reduce_mean_140_cast")];
            tensor<fp16, []> add_92_y_0_to_fp16 = const()[name = tensor<string, []>("add_92_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_92_cast = add(x = reduce_mean_140_cast, y = add_92_y_0_to_fp16)[name = tensor<string, []>("add_92_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_46_cast = sqrt(x = add_92_cast)[name = tensor<string, []>("sqrt_46_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_46_cast = real_div(x = sub_92_cast, y = sqrt_46_cast)[name = tensor<string, []>("real_div_46_cast")];
            tensor<int32, [4]> reshape_185_shape_0 = const()[name = tensor<string, []>("reshape_185_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_185_cast = reshape(shape = reshape_185_shape_0, x = real_div_46_cast)[name = tensor<string, []>("reshape_185_cast")];
            tensor<fp16, [640]> add_93_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_93_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606968512)))];
            tensor<fp16, [640]> add_93_beta_0_to_fp16 = const()[name = tensor<string, []>("add_93_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606969856)))];
            tensor<fp16, []> add_93_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_93_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 32, 32]> add_93_cast = batch_norm(beta = add_93_beta_0_to_fp16, epsilon = add_93_epsilon_0_to_fp16, gamma = add_93_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_185_cast)[name = tensor<string, []>("add_93_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_405_cast = silu(x = add_93_cast)[name = tensor<string, []>("input_405_cast")];
            tensor<int32, [2]> var_3434 = const()[name = tensor<string, []>("op_3434"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3436 = const()[name = tensor<string, []>("op_3436"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_241_pad_type_0 = const()[name = tensor<string, []>("hidden_states_241_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_241_pad_0 = const()[name = tensor<string, []>("hidden_states_241_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606971200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(609736064))), name = tensor<string, []>("up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(609736256)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_241_cast = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3436, groups = var_3126, pad = hidden_states_241_pad_0, pad_type = hidden_states_241_pad_type_0, strides = var_3434, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_405_cast)[name = tensor<string, []>("hidden_states_241_cast")];
            tensor<int32, [2]> var_3441 = const()[name = tensor<string, []>("op_3441"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3443 = const()[name = tensor<string, []>("op_3443"), 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, [640, 1280, 1, 1]> up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [614400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(609737600))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610352064))), name = tensor<string, []>("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610352256)))];
            tensor<fp16, [2, 640, 32, 32]> x_19_cast = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_3443, groups = var_3126, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_3441, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_393_cast)[name = tensor<string, []>("x_19_cast")];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_243_cast = add(x = x_19_cast, y = hidden_states_241_cast)[name = tensor<string, []>("hidden_states_243_cast")];
            tensor<int32, [5]> reshape_188_shape_0 = const()[name = tensor<string, []>("reshape_188_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_188_cast = reshape(shape = reshape_188_shape_0, x = hidden_states_243_cast)[name = tensor<string, []>("reshape_188_cast")];
            tensor<int32, [3]> reduce_mean_141_axes_0 = const()[name = tensor<string, []>("reduce_mean_141_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_141_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_141_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_141_cast = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = reshape_188_cast)[name = tensor<string, []>("reduce_mean_141_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> sub_94_cast = sub(x = reshape_188_cast, y = reduce_mean_141_cast)[name = tensor<string, []>("sub_94_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> square_47_cast = square(x = sub_94_cast)[name = tensor<string, []>("square_47_cast")];
            tensor<int32, [3]> reduce_mean_143_axes_0 = const()[name = tensor<string, []>("reduce_mean_143_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_143_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_143_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_143_cast = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_47_cast)[name = tensor<string, []>("reduce_mean_143_cast")];
            tensor<fp16, []> add_94_y_0_to_fp16 = const()[name = tensor<string, []>("add_94_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_94_cast = add(x = reduce_mean_143_cast, y = add_94_y_0_to_fp16)[name = tensor<string, []>("add_94_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_47_cast = sqrt(x = add_94_cast)[name = tensor<string, []>("sqrt_47_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_47_cast = real_div(x = sub_94_cast, y = sqrt_47_cast)[name = tensor<string, []>("real_div_47_cast")];
            tensor<int32, [4]> reshape_189_shape_0 = const()[name = tensor<string, []>("reshape_189_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_189_cast = reshape(shape = reshape_189_shape_0, x = real_div_47_cast)[name = tensor<string, []>("reshape_189_cast")];
            tensor<fp16, [640]> add_95_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_95_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610353600)))];
            tensor<fp16, [640]> add_95_beta_0_to_fp16 = const()[name = tensor<string, []>("add_95_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610354944)))];
            tensor<fp16, []> add_95_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_95_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 32, 32]> add_95_cast = batch_norm(beta = add_95_beta_0_to_fp16, epsilon = add_95_epsilon_0_to_fp16, gamma = add_95_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_189_cast)[name = tensor<string, []>("add_95_cast")];
            tensor<int32, [2]> var_3463 = const()[name = tensor<string, []>("op_3463"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3465 = const()[name = tensor<string, []>("op_3465"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_245_pad_type_0 = const()[name = tensor<string, []>("hidden_states_245_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_245_pad_0 = const()[name = tensor<string, []>("hidden_states_245_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610356288))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610663552))), name = tensor<string, []>("up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610663744)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_245_cast = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3465, groups = var_3126, pad = hidden_states_245_pad_0, pad_type = hidden_states_245_pad_type_0, strides = var_3463, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_95_cast)[name = tensor<string, []>("hidden_states_245_cast")];
            tensor<int32, [4]> var_3470 = const()[name = tensor<string, []>("op_3470"), val = tensor<int32, [4]>([2, 640, 1, 1024])];
            tensor<fp16, [2, 640, 1, 1024]> inputs_67_cast = reshape(shape = var_3470, x = hidden_states_245_cast)[name = tensor<string, []>("inputs_67_cast")];
            tensor<int32, [1]> var_3480 = const()[name = tensor<string, []>("op_3480"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_67_cast = reduce_mean(axes = var_3480, keep_dims = var_3121, x = inputs_67_cast)[name = tensor<string, []>("channels_mean_67_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3484 = const()[name = tensor<string, []>("op_3484"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3485_cast = reduce_mean(axes = var_3484, keep_dims = var_3121, x = zero_mean_sq_67_cast)[name = tensor<string, []>("op_3485_cast")];
            tensor<fp16, []> var_3486_to_fp16 = const()[name = tensor<string, []>("op_3486_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3487_cast = add(x = var_3485_cast, y = var_3486_to_fp16)[name = tensor<string, []>("op_3487_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_3487_cast)[name = tensor<string, []>("denom_67_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_67_cast = mul(x = zero_mean_67_cast, y = denom_67_cast)[name = tensor<string, []>("out_67_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3491_to_fp16 = const()[name = tensor<string, []>("op_3491_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610665088)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3492_cast = add(x = out_67_cast, y = var_3491_to_fp16)[name = tensor<string, []>("op_3492_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3494_to_fp16 = const()[name = tensor<string, []>("op_3494_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610666432)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_247_cast = mul(x = var_3492_cast, y = var_3494_to_fp16)[name = tensor<string, []>("hidden_states_247_cast")];
            tensor<int32, [2]> var_3501 = const()[name = tensor<string, []>("op_3501"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3503 = const()[name = tensor<string, []>("op_3503"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(610667776))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(610975040))), name = tensor<string, []>("up_blocks_2_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, 1024]> q_45_cast = conv(dilations = var_3503, groups = var_3126, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_3501, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor<string, []>("q_45_cast")];
            tensor<int32, [2]> var_3507 = const()[name = tensor<string, []>("op_3507"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3509 = const()[name = tensor<string, []>("op_3509"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(610975232))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611282496))), name = tensor<string, []>("up_blocks_2_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, 1024]> k_45_cast = conv(dilations = var_3509, groups = var_3126, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_3507, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor<string, []>("k_45_cast")];
            tensor<int32, [2]> var_3513 = const()[name = tensor<string, []>("op_3513"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3515 = const()[name = tensor<string, []>("op_3515"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(611282688))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611589952))), name = tensor<string, []>("up_blocks_2_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, 1024]> v_45_cast = conv(dilations = var_3515, groups = var_3126, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_3513, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor<string, []>("v_45_cast")];
            tensor<int32, [4]> var_3519 = const()[name = tensor<string, []>("op_3519"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3520_cast = reshape(shape = var_3519, x = q_45_cast)[name = tensor<string, []>("op_3520_cast")];
            tensor<int32, [4]> var_3521 = const()[name = tensor<string, []>("op_3521"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3522_cast = reshape(shape = var_3521, x = k_45_cast)[name = tensor<string, []>("op_3522_cast")];
            tensor<int32, [4]> var_3523 = const()[name = tensor<string, []>("op_3523"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3524_cast = reshape(shape = var_3523, x = v_45_cast)[name = tensor<string, []>("op_3524_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, 10, 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_3520_cast, y = var_3522_cast)[name = tensor<string, []>("attn_weights_89_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> attn_weights_91_cast = mul(x = attn_weights_89_cast, y = var_3117_to_fp16)[name = tensor<string, []>("attn_weights_91_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> var_3528_cast = softmax(axis = var_3110, x = attn_weights_91_cast)[name = tensor<string, []>("op_3528_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, 10, 64, 1024]> attn_45_cast = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_3524_cast, y = var_3528_cast)[name = tensor<string, []>("attn_45_cast")];
            tensor<int32, [4]> var_3532 = const()[name = tensor<string, []>("op_3532"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_409_cast = reshape(shape = var_3532, x = attn_45_cast)[name = tensor<string, []>("input_409_cast")];
            tensor<int32, [2]> var_3537 = const()[name = tensor<string, []>("op_3537"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3539 = const()[name = tensor<string, []>("op_3539"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3541_pad_type_0 = const()[name = tensor<string, []>("op_3541_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3541_pad_0 = const()[name = tensor<string, []>("op_3541_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_1_transformer_blocks_0_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, []>(611590144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611897408))), name = tensor<string, []>("up_blocks_2_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_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(611897600)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3541_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3539, groups = var_3126, pad = var_3541_pad_0, pad_type = var_3541_pad_type_0, strides = var_3537, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_409_cast)[name = tensor<string, []>("op_3541_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_69_cast = add(x = var_3541_cast, y = inputs_67_cast)[name = tensor<string, []>("inputs_69_cast")];
            tensor<int32, [1]> var_3545 = const()[name = tensor<string, []>("op_3545"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_69_cast = reduce_mean(axes = var_3545, keep_dims = var_3121, x = inputs_69_cast)[name = tensor<string, []>("channels_mean_69_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3549 = const()[name = tensor<string, []>("op_3549"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3550_cast = reduce_mean(axes = var_3549, keep_dims = var_3121, x = zero_mean_sq_69_cast)[name = tensor<string, []>("op_3550_cast")];
            tensor<fp16, []> var_3551_to_fp16 = const()[name = tensor<string, []>("op_3551_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3552_cast = add(x = var_3550_cast, y = var_3551_to_fp16)[name = tensor<string, []>("op_3552_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_3552_cast)[name = tensor<string, []>("denom_69_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_69_cast = mul(x = zero_mean_69_cast, y = denom_69_cast)[name = tensor<string, []>("out_69_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3556_to_fp16 = const()[name = tensor<string, []>("op_3556_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611898944)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3557_cast = add(x = out_69_cast, y = var_3556_to_fp16)[name = tensor<string, []>("op_3557_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3559_to_fp16 = const()[name = tensor<string, []>("op_3559_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611900288)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_249_cast = mul(x = var_3557_cast, y = var_3559_to_fp16)[name = tensor<string, []>("hidden_states_249_cast")];
            tensor<int32, [2]> var_3566 = const()[name = tensor<string, []>("op_3566"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3568 = const()[name = tensor<string, []>("op_3568"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(611901632))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612208896))), name = tensor<string, []>("up_blocks_2_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, 1024]> q_47_cast = conv(dilations = var_3568, groups = var_3126, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_3566, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_249_cast)[name = tensor<string, []>("q_47_cast")];
            tensor<int32, [2]> var_3572 = const()[name = tensor<string, []>("op_3572"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3574 = const()[name = tensor<string, []>("op_3574"), 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, [640, 1024, 1, 1]> up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612209088))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612700672))), name = tensor<string, []>("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_47_cast = conv(dilations = var_3574, groups = var_3126, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_3572, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_47_cast")];
            tensor<int32, [2]> var_3578 = const()[name = tensor<string, []>("op_3578"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3580 = const()[name = tensor<string, []>("op_3580"), 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, [640, 1024, 1, 1]> up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612700864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(613192448))), name = tensor<string, []>("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_47_cast = conv(dilations = var_3580, groups = var_3126, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_3578, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_47_cast")];
            tensor<int32, [4]> var_3584 = const()[name = tensor<string, []>("op_3584"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3585_cast = reshape(shape = var_3584, x = q_47_cast)[name = tensor<string, []>("op_3585_cast")];
            tensor<int32, [4]> var_3586 = const()[name = tensor<string, []>("op_3586"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_3587_cast = reshape(shape = var_3586, x = k_47_cast)[name = tensor<string, []>("op_3587_cast")];
            tensor<int32, [4]> var_3588 = const()[name = tensor<string, []>("op_3588"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_3589_cast = reshape(shape = var_3588, x = v_47_cast)[name = tensor<string, []>("op_3589_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, 10, 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_3585_cast, y = var_3587_cast)[name = tensor<string, []>("attn_weights_93_cast")];
            tensor<fp16, [2, 10, 1024, 77]> attn_weights_95_cast = mul(x = attn_weights_93_cast, y = var_3117_to_fp16)[name = tensor<string, []>("attn_weights_95_cast")];
            tensor<fp16, [2, 10, 1024, 77]> var_3593_cast = softmax(axis = var_3110, x = attn_weights_95_cast)[name = tensor<string, []>("op_3593_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, 10, 64, 1024]> attn_47_cast = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_3589_cast, y = var_3593_cast)[name = tensor<string, []>("attn_47_cast")];
            tensor<int32, [4]> var_3597 = const()[name = tensor<string, []>("op_3597"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_411_cast = reshape(shape = var_3597, x = attn_47_cast)[name = tensor<string, []>("input_411_cast")];
            tensor<int32, [2]> var_3602 = const()[name = tensor<string, []>("op_3602"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3604 = const()[name = tensor<string, []>("op_3604"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3606_pad_type_0 = const()[name = tensor<string, []>("op_3606_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3606_pad_0 = const()[name = tensor<string, []>("op_3606_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_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, []>(613192640))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(613499904))), name = tensor<string, []>("up_blocks_2_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_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(613500096)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3606_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3604, groups = var_3126, pad = var_3606_pad_0, pad_type = var_3606_pad_type_0, strides = var_3602, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_411_cast)[name = tensor<string, []>("op_3606_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_71_cast = add(x = var_3606_cast, y = inputs_69_cast)[name = tensor<string, []>("inputs_71_cast")];
            tensor<int32, [1]> var_3610 = const()[name = tensor<string, []>("op_3610"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_71_cast = reduce_mean(axes = var_3610, keep_dims = var_3121, x = inputs_71_cast)[name = tensor<string, []>("channels_mean_71_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3614 = const()[name = tensor<string, []>("op_3614"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3615_cast = reduce_mean(axes = var_3614, keep_dims = var_3121, x = zero_mean_sq_71_cast)[name = tensor<string, []>("op_3615_cast")];
            tensor<fp16, []> var_3616_to_fp16 = const()[name = tensor<string, []>("op_3616_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3617_cast = add(x = var_3615_cast, y = var_3616_to_fp16)[name = tensor<string, []>("op_3617_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_3617_cast)[name = tensor<string, []>("denom_71_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_71_cast = mul(x = zero_mean_71_cast, y = denom_71_cast)[name = tensor<string, []>("out_71_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3621_to_fp16 = const()[name = tensor<string, []>("op_3621_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(613501440)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3622_cast = add(x = out_71_cast, y = var_3621_to_fp16)[name = tensor<string, []>("op_3622_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3624_to_fp16 = const()[name = tensor<string, []>("op_3624_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(613502784)))];
            tensor<fp16, [2, 640, 1, 1024]> input_413_cast = mul(x = var_3622_cast, y = var_3624_to_fp16)[name = tensor<string, []>("input_413_cast")];
            tensor<int32, [2]> var_3632 = const()[name = tensor<string, []>("op_3632"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3634 = const()[name = tensor<string, []>("op_3634"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3636_pad_type_0 = const()[name = tensor<string, []>("op_3636_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3636_pad_0 = const()[name = tensor<string, []>("op_3636_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(613504128))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615961792))), name = tensor<string, []>("up_blocks_2_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_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3840]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615961984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615965888))), name = tensor<string, []>("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [2, 5120, 1, 1024]> var_3636_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3634, groups = var_3126, pad = var_3636_pad_0, pad_type = var_3636_pad_type_0, strides = var_3632, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_413_cast)[name = tensor<string, []>("op_3636_cast")];
            tensor<int32, [2]> var_3637_split_sizes_0 = const()[name = tensor<string, []>("op_3637_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_3637_axis_0 = const()[name = tensor<string, []>("op_3637_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 1024]> var_3637_cast_0, tensor<fp16, [2, 2560, 1, 1024]> var_3637_cast_1 = split(axis = var_3637_axis_0, split_sizes = var_3637_split_sizes_0, x = var_3636_cast)[name = tensor<string, []>("op_3637_cast")];
            tensor<string, []> var_3639_mode_0 = const()[name = tensor<string, []>("op_3639_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 1024]> var_3639_cast = gelu(mode = var_3639_mode_0, x = var_3637_cast_1)[name = tensor<string, []>("op_3639_cast")];
            tensor<fp16, [2, 2560, 1, 1024]> input_415_cast = mul(x = var_3637_cast_0, y = var_3639_cast)[name = tensor<string, []>("input_415_cast")];
            tensor<int32, [2]> var_3643 = const()[name = tensor<string, []>("op_3643"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3645 = const()[name = tensor<string, []>("op_3645"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3647_pad_type_0 = const()[name = tensor<string, []>("op_3647_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3647_pad_0 = const()[name = tensor<string, []>("op_3647_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615966080))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617194944))), name = tensor<string, []>("up_blocks_2_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_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(617195136)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3647_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3645, groups = var_3126, pad = var_3647_pad_0, pad_type = var_3647_pad_type_0, strides = var_3643, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_415_cast)[name = tensor<string, []>("op_3647_cast")];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_253_cast = add(x = var_3647_cast, y = inputs_71_cast)[name = tensor<string, []>("hidden_states_253_cast")];
            tensor<int32, [4]> var_3649 = const()[name = tensor<string, []>("op_3649"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> input_417_cast = reshape(shape = var_3649, x = hidden_states_253_cast)[name = tensor<string, []>("input_417_cast")];
            tensor<int32, [2]> var_3653 = const()[name = tensor<string, []>("op_3653"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3655 = const()[name = tensor<string, []>("op_3655"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_255_pad_type_0 = const()[name = tensor<string, []>("hidden_states_255_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_255_pad_0 = const()[name = tensor<string, []>("hidden_states_255_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617196480))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617503744))), name = tensor<string, []>("up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617503936)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_255_cast = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3655, groups = var_3126, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_3653, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_417_cast)[name = tensor<string, []>("hidden_states_255_cast")];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_257_cast = add(x = hidden_states_255_cast, y = hidden_states_243_cast)[name = tensor<string, []>("hidden_states_257_cast")];
            tensor<bool, []> input_419_interleave_0 = const()[name = tensor<string, []>("input_419_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 960, 32, 32]> input_419_cast = concat(axis = var_3126, interleave = input_419_interleave_0, values = (hidden_states_257_cast, input_63_cast))[name = tensor<string, []>("input_419_cast")];
            tensor<int32, [5]> reshape_192_shape_0 = const()[name = tensor<string, []>("reshape_192_shape_0"), val = tensor<int32, [5]>([2, 32, 30, 32, 32])];
            tensor<fp16, [2, 32, 30, 32, 32]> reshape_192_cast = reshape(shape = reshape_192_shape_0, x = input_419_cast)[name = tensor<string, []>("reshape_192_cast")];
            tensor<int32, [3]> reduce_mean_144_axes_0 = const()[name = tensor<string, []>("reduce_mean_144_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_144_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_144_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_144_cast = reduce_mean(axes = reduce_mean_144_axes_0, keep_dims = reduce_mean_144_keep_dims_0, x = reshape_192_cast)[name = tensor<string, []>("reduce_mean_144_cast")];
            tensor<fp16, [2, 32, 30, 32, 32]> sub_96_cast = sub(x = reshape_192_cast, y = reduce_mean_144_cast)[name = tensor<string, []>("sub_96_cast")];
            tensor<fp16, [2, 32, 30, 32, 32]> square_48_cast = square(x = sub_96_cast)[name = tensor<string, []>("square_48_cast")];
            tensor<int32, [3]> reduce_mean_146_axes_0 = const()[name = tensor<string, []>("reduce_mean_146_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_146_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_146_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_146_cast = reduce_mean(axes = reduce_mean_146_axes_0, keep_dims = reduce_mean_146_keep_dims_0, x = square_48_cast)[name = tensor<string, []>("reduce_mean_146_cast")];
            tensor<fp16, []> add_96_y_0_to_fp16 = const()[name = tensor<string, []>("add_96_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_96_cast = add(x = reduce_mean_146_cast, y = add_96_y_0_to_fp16)[name = tensor<string, []>("add_96_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_48_cast = sqrt(x = add_96_cast)[name = tensor<string, []>("sqrt_48_cast")];
            tensor<fp16, [2, 32, 30, 32, 32]> real_div_48_cast = real_div(x = sub_96_cast, y = sqrt_48_cast)[name = tensor<string, []>("real_div_48_cast")];
            tensor<int32, [4]> reshape_193_shape_0 = const()[name = tensor<string, []>("reshape_193_shape_0"), val = tensor<int32, [4]>([2, 960, 32, 32])];
            tensor<fp16, [2, 960, 32, 32]> reshape_193_cast = reshape(shape = reshape_193_shape_0, x = real_div_48_cast)[name = tensor<string, []>("reshape_193_cast")];
            tensor<fp16, [960]> add_97_mean_0_to_fp16 = const()[name = tensor<string, []>("add_97_mean_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617505280)))];
            tensor<fp16, [960]> add_97_variance_0_to_fp16 = const()[name = tensor<string, []>("add_97_variance_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617507264)))];
            tensor<fp16, [960]> add_97_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_97_gamma_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617509248)))];
            tensor<fp16, [960]> add_97_beta_0_to_fp16 = const()[name = tensor<string, []>("add_97_beta_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617511232)))];
            tensor<fp16, []> add_97_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_97_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 960, 32, 32]> add_97_cast = batch_norm(beta = add_97_beta_0_to_fp16, epsilon = add_97_epsilon_0_to_fp16, gamma = add_97_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_193_cast)[name = tensor<string, []>("add_97_cast")];
            tensor<fp16, [2, 960, 32, 32]> input_423_cast = silu(x = add_97_cast)[name = tensor<string, []>("input_423_cast")];
            tensor<int32, [2]> var_3673 = const()[name = tensor<string, []>("op_3673"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3675 = const()[name = tensor<string, []>("op_3675"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_259_pad_type_0 = const()[name = tensor<string, []>("hidden_states_259_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_259_pad_0 = const()[name = tensor<string, []>("hidden_states_259_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 960, 3, 3]> up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [4147200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617513216))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(621660480))), name = tensor<string, []>("up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 960, 3, 3])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(621660672)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_259_cast = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_3675, groups = var_3126, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_3673, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized, x = input_423_cast)[name = tensor<string, []>("hidden_states_259_cast")];
            tensor<int32, [2]> var_3681 = const()[name = tensor<string, []>("op_3681"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3683 = const()[name = tensor<string, []>("op_3683"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_37_pad_type_0 = const()[name = tensor<string, []>("temb_37_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_37_pad_0 = const()[name = tensor<string, []>("temb_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> up_blocks_2_resnets_2_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, []>(621662016))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622276480))), name = tensor<string, []>("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1280, 1, 1])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622276672)))];
            tensor<fp16, [2, 640, 1, 1]> temb_37_cast = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_3683, groups = var_3126, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_3681, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_37_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_427_cast = add(x = hidden_states_259_cast, y = temb_37_cast)[name = tensor<string, []>("input_427_cast")];
            tensor<int32, [5]> reshape_196_shape_0 = const()[name = tensor<string, []>("reshape_196_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_196_cast = reshape(shape = reshape_196_shape_0, x = input_427_cast)[name = tensor<string, []>("reshape_196_cast")];
            tensor<int32, [3]> reduce_mean_147_axes_0 = const()[name = tensor<string, []>("reduce_mean_147_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_147_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_147_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_147_cast = reduce_mean(axes = reduce_mean_147_axes_0, keep_dims = reduce_mean_147_keep_dims_0, x = reshape_196_cast)[name = tensor<string, []>("reduce_mean_147_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> sub_98_cast = sub(x = reshape_196_cast, y = reduce_mean_147_cast)[name = tensor<string, []>("sub_98_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> square_49_cast = square(x = sub_98_cast)[name = tensor<string, []>("square_49_cast")];
            tensor<int32, [3]> reduce_mean_149_axes_0 = const()[name = tensor<string, []>("reduce_mean_149_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_149_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_149_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_149_cast = reduce_mean(axes = reduce_mean_149_axes_0, keep_dims = reduce_mean_149_keep_dims_0, x = square_49_cast)[name = tensor<string, []>("reduce_mean_149_cast")];
            tensor<fp16, []> add_98_y_0_to_fp16 = const()[name = tensor<string, []>("add_98_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_98_cast = add(x = reduce_mean_149_cast, y = add_98_y_0_to_fp16)[name = tensor<string, []>("add_98_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_49_cast = sqrt(x = add_98_cast)[name = tensor<string, []>("sqrt_49_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_49_cast = real_div(x = sub_98_cast, y = sqrt_49_cast)[name = tensor<string, []>("real_div_49_cast")];
            tensor<int32, [4]> reshape_197_shape_0 = const()[name = tensor<string, []>("reshape_197_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_197_cast = reshape(shape = reshape_197_shape_0, x = real_div_49_cast)[name = tensor<string, []>("reshape_197_cast")];
            tensor<fp16, [640]> add_99_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_99_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622278016)))];
            tensor<fp16, [640]> add_99_beta_0_to_fp16 = const()[name = tensor<string, []>("add_99_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622279360)))];
            tensor<fp16, []> add_99_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_99_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 32, 32]> add_99_cast = batch_norm(beta = add_99_beta_0_to_fp16, epsilon = add_99_epsilon_0_to_fp16, gamma = add_99_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_197_cast)[name = tensor<string, []>("add_99_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_431_cast = silu(x = add_99_cast)[name = tensor<string, []>("input_431_cast")];
            tensor<int32, [2]> var_3693 = const()[name = tensor<string, []>("op_3693"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3695 = const()[name = tensor<string, []>("op_3695"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_261_pad_type_0 = const()[name = tensor<string, []>("hidden_states_261_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_261_pad_0 = const()[name = tensor<string, []>("hidden_states_261_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622280704))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625045568))), name = tensor<string, []>("up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625045760)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_261_cast = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_3695, groups = var_3126, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_3693, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized, x = input_431_cast)[name = tensor<string, []>("hidden_states_261_cast")];
            tensor<int32, [2]> var_3700 = const()[name = tensor<string, []>("op_3700"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3702 = const()[name = tensor<string, []>("op_3702"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_21_pad_type_0 = const()[name = tensor<string, []>("x_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_21_pad_0 = const()[name = tensor<string, []>("x_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 960, 1, 1]> up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [460800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625047104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625507968))), name = tensor<string, []>("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 960, 1, 1])];
            tensor<fp16, [640]> 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, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625508160)))];
            tensor<fp16, [2, 640, 32, 32]> x_21_cast = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_3702, groups = var_3126, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_3700, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_419_cast)[name = tensor<string, []>("x_21_cast")];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_263_cast = add(x = x_21_cast, y = hidden_states_261_cast)[name = tensor<string, []>("hidden_states_263_cast")];
            tensor<int32, [5]> reshape_200_shape_0 = const()[name = tensor<string, []>("reshape_200_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_200_cast = reshape(shape = reshape_200_shape_0, x = hidden_states_263_cast)[name = tensor<string, []>("reshape_200_cast")];
            tensor<int32, [3]> reduce_mean_150_axes_0 = const()[name = tensor<string, []>("reduce_mean_150_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_150_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_150_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_150_cast = reduce_mean(axes = reduce_mean_150_axes_0, keep_dims = reduce_mean_150_keep_dims_0, x = reshape_200_cast)[name = tensor<string, []>("reduce_mean_150_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> sub_100_cast = sub(x = reshape_200_cast, y = reduce_mean_150_cast)[name = tensor<string, []>("sub_100_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> square_50_cast = square(x = sub_100_cast)[name = tensor<string, []>("square_50_cast")];
            tensor<int32, [3]> reduce_mean_152_axes_0 = const()[name = tensor<string, []>("reduce_mean_152_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_152_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_152_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_152_cast = reduce_mean(axes = reduce_mean_152_axes_0, keep_dims = reduce_mean_152_keep_dims_0, x = square_50_cast)[name = tensor<string, []>("reduce_mean_152_cast")];
            tensor<fp16, []> add_100_y_0_to_fp16 = const()[name = tensor<string, []>("add_100_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_100_cast = add(x = reduce_mean_152_cast, y = add_100_y_0_to_fp16)[name = tensor<string, []>("add_100_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_50_cast = sqrt(x = add_100_cast)[name = tensor<string, []>("sqrt_50_cast")];
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_50_cast = real_div(x = sub_100_cast, y = sqrt_50_cast)[name = tensor<string, []>("real_div_50_cast")];
            tensor<int32, [4]> reshape_201_shape_0 = const()[name = tensor<string, []>("reshape_201_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_201_cast = reshape(shape = reshape_201_shape_0, x = real_div_50_cast)[name = tensor<string, []>("reshape_201_cast")];
            tensor<fp16, [640]> add_101_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_101_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625509504)))];
            tensor<fp16, [640]> add_101_beta_0_to_fp16 = const()[name = tensor<string, []>("add_101_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625510848)))];
            tensor<fp16, []> add_101_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_101_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 32, 32]> add_101_cast = batch_norm(beta = add_101_beta_0_to_fp16, epsilon = add_101_epsilon_0_to_fp16, gamma = add_101_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_201_cast)[name = tensor<string, []>("add_101_cast")];
            tensor<int32, [2]> var_3722 = const()[name = tensor<string, []>("op_3722"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3724 = const()[name = tensor<string, []>("op_3724"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_265_pad_type_0 = const()[name = tensor<string, []>("hidden_states_265_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_265_pad_0 = const()[name = tensor<string, []>("hidden_states_265_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625512192))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625819456))), name = tensor<string, []>("up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625819648)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_265_cast = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_3724, groups = var_3126, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_3722, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized, x = add_101_cast)[name = tensor<string, []>("hidden_states_265_cast")];
            tensor<int32, [4]> var_3729 = const()[name = tensor<string, []>("op_3729"), val = tensor<int32, [4]>([2, 640, 1, 1024])];
            tensor<fp16, [2, 640, 1, 1024]> inputs_73_cast = reshape(shape = var_3729, x = hidden_states_265_cast)[name = tensor<string, []>("inputs_73_cast")];
            tensor<int32, [1]> var_3739 = const()[name = tensor<string, []>("op_3739"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_73_cast = reduce_mean(axes = var_3739, keep_dims = var_3121, x = inputs_73_cast)[name = tensor<string, []>("channels_mean_73_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3743 = const()[name = tensor<string, []>("op_3743"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3744_cast = reduce_mean(axes = var_3743, keep_dims = var_3121, x = zero_mean_sq_73_cast)[name = tensor<string, []>("op_3744_cast")];
            tensor<fp16, []> var_3745_to_fp16 = const()[name = tensor<string, []>("op_3745_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3746_cast = add(x = var_3744_cast, y = var_3745_to_fp16)[name = tensor<string, []>("op_3746_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_3746_cast)[name = tensor<string, []>("denom_73_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_73_cast = mul(x = zero_mean_73_cast, y = denom_73_cast)[name = tensor<string, []>("out_73_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3750_to_fp16 = const()[name = tensor<string, []>("op_3750_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625820992)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3751_cast = add(x = out_73_cast, y = var_3750_to_fp16)[name = tensor<string, []>("op_3751_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3753_to_fp16 = const()[name = tensor<string, []>("op_3753_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625822336)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_267_cast = mul(x = var_3751_cast, y = var_3753_to_fp16)[name = tensor<string, []>("hidden_states_267_cast")];
            tensor<int32, [2]> var_3760 = const()[name = tensor<string, []>("op_3760"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3762 = const()[name = tensor<string, []>("op_3762"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(625823680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(626130944))), name = tensor<string, []>("up_blocks_2_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, 1024]> q_49_cast = conv(dilations = var_3762, groups = var_3126, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_3760, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor<string, []>("q_49_cast")];
            tensor<int32, [2]> var_3766 = const()[name = tensor<string, []>("op_3766"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3768 = const()[name = tensor<string, []>("op_3768"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(626131136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(626438400))), name = tensor<string, []>("up_blocks_2_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, 1024]> k_49_cast = conv(dilations = var_3768, groups = var_3126, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_3766, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor<string, []>("k_49_cast")];
            tensor<int32, [2]> var_3772 = const()[name = tensor<string, []>("op_3772"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3774 = const()[name = tensor<string, []>("op_3774"), 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, [640, 640, 1, 1]> up_blocks_2_attentions_2_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, []>(626438592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(626745856))), name = tensor<string, []>("up_blocks_2_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, 1024]> v_49_cast = conv(dilations = var_3774, groups = var_3126, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_3772, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor<string, []>("v_49_cast")];
            tensor<int32, [4]> var_3778 = const()[name = tensor<string, []>("op_3778"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3779_cast = reshape(shape = var_3778, x = q_49_cast)[name = tensor<string, []>("op_3779_cast")];
            tensor<int32, [4]> var_3780 = const()[name = tensor<string, []>("op_3780"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3781_cast = reshape(shape = var_3780, x = k_49_cast)[name = tensor<string, []>("op_3781_cast")];
            tensor<int32, [4]> var_3782 = const()[name = tensor<string, []>("op_3782"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3783_cast = reshape(shape = var_3782, x = v_49_cast)[name = tensor<string, []>("op_3783_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, 10, 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_3779_cast, y = var_3781_cast)[name = tensor<string, []>("attn_weights_97_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> attn_weights_99_cast = mul(x = attn_weights_97_cast, y = var_3117_to_fp16)[name = tensor<string, []>("attn_weights_99_cast")];
            tensor<fp16, [2, 10, 1024, 1024]> var_3787_cast = softmax(axis = var_3110, x = attn_weights_99_cast)[name = tensor<string, []>("op_3787_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, 10, 64, 1024]> attn_49_cast = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_3783_cast, y = var_3787_cast)[name = tensor<string, []>("attn_49_cast")];
            tensor<int32, [4]> var_3791 = const()[name = tensor<string, []>("op_3791"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_435_cast = reshape(shape = var_3791, x = attn_49_cast)[name = tensor<string, []>("input_435_cast")];
            tensor<int32, [2]> var_3796 = const()[name = tensor<string, []>("op_3796"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3798 = const()[name = tensor<string, []>("op_3798"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3800_pad_type_0 = const()[name = tensor<string, []>("op_3800_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3800_pad_0 = const()[name = tensor<string, []>("op_3800_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_2_transformer_blocks_0_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, []>(626746048))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(627053312))), name = tensor<string, []>("up_blocks_2_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_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(627053504)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3800_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3798, groups = var_3126, pad = var_3800_pad_0, pad_type = var_3800_pad_type_0, strides = var_3796, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_435_cast)[name = tensor<string, []>("op_3800_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_75_cast = add(x = var_3800_cast, y = inputs_73_cast)[name = tensor<string, []>("inputs_75_cast")];
            tensor<int32, [1]> var_3804 = const()[name = tensor<string, []>("op_3804"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_75_cast = reduce_mean(axes = var_3804, keep_dims = var_3121, x = inputs_75_cast)[name = tensor<string, []>("channels_mean_75_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3808 = const()[name = tensor<string, []>("op_3808"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3809_cast = reduce_mean(axes = var_3808, keep_dims = var_3121, x = zero_mean_sq_75_cast)[name = tensor<string, []>("op_3809_cast")];
            tensor<fp16, []> var_3810_to_fp16 = const()[name = tensor<string, []>("op_3810_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3811_cast = add(x = var_3809_cast, y = var_3810_to_fp16)[name = tensor<string, []>("op_3811_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_3811_cast)[name = tensor<string, []>("denom_75_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_75_cast = mul(x = zero_mean_75_cast, y = denom_75_cast)[name = tensor<string, []>("out_75_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3815_to_fp16 = const()[name = tensor<string, []>("op_3815_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(627054848)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3816_cast = add(x = out_75_cast, y = var_3815_to_fp16)[name = tensor<string, []>("op_3816_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3818_to_fp16 = const()[name = tensor<string, []>("op_3818_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(627056192)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_269_cast = mul(x = var_3816_cast, y = var_3818_to_fp16)[name = tensor<string, []>("hidden_states_269_cast")];
            tensor<int32, [2]> var_3825 = const()[name = tensor<string, []>("op_3825"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3827 = const()[name = tensor<string, []>("op_3827"), 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, [640, 640, 1, 1]> up_blocks_2_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, []>(627057536))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(627364800))), name = tensor<string, []>("up_blocks_2_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, 1024]> q_51_cast = conv(dilations = var_3827, groups = var_3126, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_3825, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_269_cast)[name = tensor<string, []>("q_51_cast")];
            tensor<int32, [2]> var_3831 = const()[name = tensor<string, []>("op_3831"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3833 = const()[name = tensor<string, []>("op_3833"), 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, [640, 1024, 1, 1]> up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(627364992))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(627856576))), name = tensor<string, []>("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> k_51_cast = conv(dilations = var_3833, groups = var_3126, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_3831, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_51_cast")];
            tensor<int32, [2]> var_3837 = const()[name = tensor<string, []>("op_3837"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3839 = const()[name = tensor<string, []>("op_3839"), 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, [640, 1024, 1, 1]> up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [491520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(627856768))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(628348352))), name = tensor<string, []>("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 1024, 1, 1])];
            tensor<fp16, [2, 640, 1, 77]> v_51_cast = conv(dilations = var_3839, groups = var_3126, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_3837, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_51_cast")];
            tensor<int32, [4]> var_3843 = const()[name = tensor<string, []>("op_3843"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_3844_cast = reshape(shape = var_3843, x = q_51_cast)[name = tensor<string, []>("op_3844_cast")];
            tensor<int32, [4]> var_3845 = const()[name = tensor<string, []>("op_3845"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_3846_cast = reshape(shape = var_3845, x = k_51_cast)[name = tensor<string, []>("op_3846_cast")];
            tensor<int32, [4]> var_3847 = const()[name = tensor<string, []>("op_3847"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_3848_cast = reshape(shape = var_3847, x = v_51_cast)[name = tensor<string, []>("op_3848_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, 10, 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_3844_cast, y = var_3846_cast)[name = tensor<string, []>("attn_weights_101_cast")];
            tensor<fp16, [2, 10, 1024, 77]> attn_weights_103_cast = mul(x = attn_weights_101_cast, y = var_3117_to_fp16)[name = tensor<string, []>("attn_weights_103_cast")];
            tensor<fp16, [2, 10, 1024, 77]> var_3852_cast = softmax(axis = var_3110, x = attn_weights_103_cast)[name = tensor<string, []>("op_3852_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, 10, 64, 1024]> attn_51_cast = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_3848_cast, y = var_3852_cast)[name = tensor<string, []>("attn_51_cast")];
            tensor<int32, [4]> var_3856 = const()[name = tensor<string, []>("op_3856"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_437_cast = reshape(shape = var_3856, x = attn_51_cast)[name = tensor<string, []>("input_437_cast")];
            tensor<int32, [2]> var_3861 = const()[name = tensor<string, []>("op_3861"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3863 = const()[name = tensor<string, []>("op_3863"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3865_pad_type_0 = const()[name = tensor<string, []>("op_3865_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3865_pad_0 = const()[name = tensor<string, []>("op_3865_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_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, []>(628348544))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(628655808))), name = tensor<string, []>("up_blocks_2_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_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(628656000)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3865_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3863, groups = var_3126, pad = var_3865_pad_0, pad_type = var_3865_pad_type_0, strides = var_3861, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_437_cast)[name = tensor<string, []>("op_3865_cast")];
            tensor<fp16, [2, 640, 1, 1024]> inputs_77_cast = add(x = var_3865_cast, y = inputs_75_cast)[name = tensor<string, []>("inputs_77_cast")];
            tensor<int32, [1]> var_3869 = const()[name = tensor<string, []>("op_3869"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_77_cast = reduce_mean(axes = var_3869, keep_dims = var_3121, x = inputs_77_cast)[name = tensor<string, []>("channels_mean_77_cast")];
            tensor<fp16, [2, 640, 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, 640, 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_3873 = const()[name = tensor<string, []>("op_3873"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_3874_cast = reduce_mean(axes = var_3873, keep_dims = var_3121, x = zero_mean_sq_77_cast)[name = tensor<string, []>("op_3874_cast")];
            tensor<fp16, []> var_3875_to_fp16 = const()[name = tensor<string, []>("op_3875_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 1024]> var_3876_cast = add(x = var_3874_cast, y = var_3875_to_fp16)[name = tensor<string, []>("op_3876_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_3876_cast)[name = tensor<string, []>("denom_77_cast")];
            tensor<fp16, [2, 640, 1, 1024]> out_77_cast = mul(x = zero_mean_77_cast, y = denom_77_cast)[name = tensor<string, []>("out_77_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3880_to_fp16 = const()[name = tensor<string, []>("op_3880_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(628657344)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3881_cast = add(x = out_77_cast, y = var_3880_to_fp16)[name = tensor<string, []>("op_3881_cast")];
            tensor<fp16, [1, 640, 1, 1]> var_3883_to_fp16 = const()[name = tensor<string, []>("op_3883_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(628658688)))];
            tensor<fp16, [2, 640, 1, 1024]> input_439_cast = mul(x = var_3881_cast, y = var_3883_to_fp16)[name = tensor<string, []>("input_439_cast")];
            tensor<int32, [2]> var_3891 = const()[name = tensor<string, []>("op_3891"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3893 = const()[name = tensor<string, []>("op_3893"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3895_pad_type_0 = const()[name = tensor<string, []>("op_3895_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3895_pad_0 = const()[name = tensor<string, []>("op_3895_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2457600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(628660032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(631117696))), name = tensor<string, []>("up_blocks_2_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_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3840]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(631117888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(631121792))), name = tensor<string, []>("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([5120])];
            tensor<fp16, [2, 5120, 1, 1024]> var_3895_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3893, groups = var_3126, pad = var_3895_pad_0, pad_type = var_3895_pad_type_0, strides = var_3891, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_439_cast)[name = tensor<string, []>("op_3895_cast")];
            tensor<int32, [2]> var_3896_split_sizes_0 = const()[name = tensor<string, []>("op_3896_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_3896_axis_0 = const()[name = tensor<string, []>("op_3896_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 1024]> var_3896_cast_0, tensor<fp16, [2, 2560, 1, 1024]> var_3896_cast_1 = split(axis = var_3896_axis_0, split_sizes = var_3896_split_sizes_0, x = var_3895_cast)[name = tensor<string, []>("op_3896_cast")];
            tensor<string, []> var_3898_mode_0 = const()[name = tensor<string, []>("op_3898_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 1024]> var_3898_cast = gelu(mode = var_3898_mode_0, x = var_3896_cast_1)[name = tensor<string, []>("op_3898_cast")];
            tensor<fp16, [2, 2560, 1, 1024]> input_441_cast = mul(x = var_3896_cast_0, y = var_3898_cast)[name = tensor<string, []>("input_441_cast")];
            tensor<int32, [2]> var_3902 = const()[name = tensor<string, []>("op_3902"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3904 = const()[name = tensor<string, []>("op_3904"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_3906_pad_type_0 = const()[name = tensor<string, []>("op_3906_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_3906_pad_0 = const()[name = tensor<string, []>("op_3906_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1228800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(631121984))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632350848))), name = tensor<string, []>("up_blocks_2_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_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_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, []>(632351040)))];
            tensor<fp16, [2, 640, 1, 1024]> var_3906_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3904, groups = var_3126, pad = var_3906_pad_0, pad_type = var_3906_pad_type_0, strides = var_3902, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_441_cast)[name = tensor<string, []>("op_3906_cast")];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_273_cast = add(x = var_3906_cast, y = inputs_77_cast)[name = tensor<string, []>("hidden_states_273_cast")];
            tensor<int32, [4]> var_3908 = const()[name = tensor<string, []>("op_3908"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> input_443_cast = reshape(shape = var_3908, x = hidden_states_273_cast)[name = tensor<string, []>("input_443_cast")];
            tensor<int32, [2]> var_3912 = const()[name = tensor<string, []>("op_3912"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3914 = const()[name = tensor<string, []>("op_3914"), 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]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632352384))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632659648))), name = tensor<string, []>("up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 1, 1])];
            tensor<fp16, [640]> up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632659840)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_275_cast = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_3914, groups = var_3126, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_3912, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized, x = input_443_cast)[name = tensor<string, []>("hidden_states_275_cast")];
            tensor<fp16, [2, 640, 32, 32]> input_445_cast = add(x = hidden_states_275_cast, y = hidden_states_263_cast)[name = tensor<string, []>("input_445_cast")];
            tensor<fp32, []> input_447_scale_factor_height_0 = const()[name = tensor<string, []>("input_447_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, []> input_447_scale_factor_width_0 = const()[name = tensor<string, []>("input_447_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp16, [2, 640, 64, 64]> input_447_cast = upsample_nearest_neighbor(scale_factor_height = input_447_scale_factor_height_0, scale_factor_width = input_447_scale_factor_width_0, x = input_445_cast)[name = tensor<string, []>("input_447_cast")];
            tensor<int32, [2]> var_3923 = const()[name = tensor<string, []>("op_3923"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3925 = const()[name = tensor<string, []>("op_3925"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_277_pad_type_0 = const()[name = tensor<string, []>("hidden_states_277_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = tensor<string, []>("hidden_states_277_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2764800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632661184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(635426048))), name = tensor<string, []>("up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([640, 640, 3, 3])];
            tensor<fp16, [640]> up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(635426240)))];
            tensor<fp16, [2, 640, 64, 64]> hidden_states_277_cast = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_3925, groups = var_3126, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = var_3923, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized, x = input_447_cast)[name = tensor<string, []>("hidden_states_277_cast")];
            tensor<int32, []> var_3929 = const()[name = tensor<string, []>("op_3929"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_3940 = const()[name = tensor<string, []>("op_3940"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_3945 = const()[name = tensor<string, []>("op_3945"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_449_interleave_0 = const()[name = tensor<string, []>("input_449_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 960, 64, 64]> input_449_cast = concat(axis = var_3945, interleave = input_449_interleave_0, values = (hidden_states_277_cast, input_61_cast))[name = tensor<string, []>("input_449_cast")];
            tensor<int32, [5]> reshape_204_shape_0 = const()[name = tensor<string, []>("reshape_204_shape_0"), val = tensor<int32, [5]>([2, 32, 30, 64, 64])];
            tensor<fp16, [2, 32, 30, 64, 64]> reshape_204_cast = reshape(shape = reshape_204_shape_0, x = input_449_cast)[name = tensor<string, []>("reshape_204_cast")];
            tensor<int32, [3]> reduce_mean_153_axes_0 = const()[name = tensor<string, []>("reduce_mean_153_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_153_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_153_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_153_cast = reduce_mean(axes = reduce_mean_153_axes_0, keep_dims = reduce_mean_153_keep_dims_0, x = reshape_204_cast)[name = tensor<string, []>("reduce_mean_153_cast")];
            tensor<fp16, [2, 32, 30, 64, 64]> sub_102_cast = sub(x = reshape_204_cast, y = reduce_mean_153_cast)[name = tensor<string, []>("sub_102_cast")];
            tensor<fp16, [2, 32, 30, 64, 64]> square_51_cast = square(x = sub_102_cast)[name = tensor<string, []>("square_51_cast")];
            tensor<int32, [3]> reduce_mean_155_axes_0 = const()[name = tensor<string, []>("reduce_mean_155_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_155_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_155_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_155_cast = reduce_mean(axes = reduce_mean_155_axes_0, keep_dims = reduce_mean_155_keep_dims_0, x = square_51_cast)[name = tensor<string, []>("reduce_mean_155_cast")];
            tensor<fp16, []> add_102_y_0_to_fp16 = const()[name = tensor<string, []>("add_102_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_102_cast = add(x = reduce_mean_155_cast, y = add_102_y_0_to_fp16)[name = tensor<string, []>("add_102_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_51_cast = sqrt(x = add_102_cast)[name = tensor<string, []>("sqrt_51_cast")];
            tensor<fp16, [2, 32, 30, 64, 64]> real_div_51_cast = real_div(x = sub_102_cast, y = sqrt_51_cast)[name = tensor<string, []>("real_div_51_cast")];
            tensor<int32, [4]> reshape_205_shape_0 = const()[name = tensor<string, []>("reshape_205_shape_0"), val = tensor<int32, [4]>([2, 960, 64, 64])];
            tensor<fp16, [2, 960, 64, 64]> reshape_205_cast = reshape(shape = reshape_205_shape_0, x = real_div_51_cast)[name = tensor<string, []>("reshape_205_cast")];
            tensor<fp16, [960]> add_103_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_103_gamma_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(635427584)))];
            tensor<fp16, [960]> add_103_beta_0_to_fp16 = const()[name = tensor<string, []>("add_103_beta_0_to_fp16"), val = tensor<fp16, [960]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(635429568)))];
            tensor<fp16, []> add_103_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_103_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 960, 64, 64]> add_103_cast = batch_norm(beta = add_103_beta_0_to_fp16, epsilon = add_103_epsilon_0_to_fp16, gamma = add_103_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_205_cast)[name = tensor<string, []>("add_103_cast")];
            tensor<fp16, [2, 960, 64, 64]> input_453_cast = silu(x = add_103_cast)[name = tensor<string, []>("input_453_cast")];
            tensor<int32, [2]> var_3972 = const()[name = tensor<string, []>("op_3972"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3974 = const()[name = tensor<string, []>("op_3974"), 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, [320, 960, 3, 3]> up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2073600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(635431552))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637505216))), name = tensor<string, []>("up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 960, 3, 3])];
            tensor<fp16, [320]> up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637505408)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_279_cast = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3974, groups = var_3945, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_3972, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_453_cast)[name = tensor<string, []>("hidden_states_279_cast")];
            tensor<int32, [2]> var_3980 = const()[name = tensor<string, []>("op_3980"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3982 = const()[name = tensor<string, []>("op_3982"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_39_pad_type_0 = const()[name = tensor<string, []>("temb_39_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_39_pad_0 = const()[name = tensor<string, []>("temb_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637506112))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637813376))), name = tensor<string, []>("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637813568)))];
            tensor<fp16, [2, 320, 1, 1]> temb_39_cast = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3982, groups = var_3945, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_3980, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_39_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_457_cast = add(x = hidden_states_279_cast, y = temb_39_cast)[name = tensor<string, []>("input_457_cast")];
            tensor<int32, [5]> reshape_208_shape_0 = const()[name = tensor<string, []>("reshape_208_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_208_cast = reshape(shape = reshape_208_shape_0, x = input_457_cast)[name = tensor<string, []>("reshape_208_cast")];
            tensor<int32, [3]> reduce_mean_156_axes_0 = const()[name = tensor<string, []>("reduce_mean_156_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_156_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_156_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_156_cast = reduce_mean(axes = reduce_mean_156_axes_0, keep_dims = reduce_mean_156_keep_dims_0, x = reshape_208_cast)[name = tensor<string, []>("reduce_mean_156_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_104_cast = sub(x = reshape_208_cast, y = reduce_mean_156_cast)[name = tensor<string, []>("sub_104_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_52_cast = square(x = sub_104_cast)[name = tensor<string, []>("square_52_cast")];
            tensor<int32, [3]> reduce_mean_158_axes_0 = const()[name = tensor<string, []>("reduce_mean_158_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_158_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_158_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_158_cast = reduce_mean(axes = reduce_mean_158_axes_0, keep_dims = reduce_mean_158_keep_dims_0, x = square_52_cast)[name = tensor<string, []>("reduce_mean_158_cast")];
            tensor<fp16, []> add_104_y_0_to_fp16 = const()[name = tensor<string, []>("add_104_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_104_cast = add(x = reduce_mean_158_cast, y = add_104_y_0_to_fp16)[name = tensor<string, []>("add_104_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_52_cast = sqrt(x = add_104_cast)[name = tensor<string, []>("sqrt_52_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_52_cast = real_div(x = sub_104_cast, y = sqrt_52_cast)[name = tensor<string, []>("real_div_52_cast")];
            tensor<int32, [4]> reshape_209_shape_0 = const()[name = tensor<string, []>("reshape_209_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_209_cast = reshape(shape = reshape_209_shape_0, x = real_div_52_cast)[name = tensor<string, []>("reshape_209_cast")];
            tensor<fp16, [320]> add_105_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_105_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637814272)))];
            tensor<fp16, [320]> add_105_beta_0_to_fp16 = const()[name = tensor<string, []>("add_105_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637814976)))];
            tensor<fp16, []> add_105_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_105_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_105_cast = batch_norm(beta = add_105_beta_0_to_fp16, epsilon = add_105_epsilon_0_to_fp16, gamma = add_105_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_209_cast)[name = tensor<string, []>("add_105_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_461_cast = silu(x = add_105_cast)[name = tensor<string, []>("input_461_cast")];
            tensor<int32, [2]> var_3992 = const()[name = tensor<string, []>("op_3992"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_3994 = const()[name = tensor<string, []>("op_3994"), 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, [320, 320, 3, 3]> up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637815680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638506944))), name = tensor<string, []>("up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            tensor<fp16, [320]> up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638507136)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_281_cast = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3994, groups = var_3945, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_3992, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_461_cast)[name = tensor<string, []>("hidden_states_281_cast")];
            tensor<int32, [2]> var_3999 = const()[name = tensor<string, []>("op_3999"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4001 = const()[name = tensor<string, []>("op_4001"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_23_pad_type_0 = const()[name = tensor<string, []>("x_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_23_pad_0 = const()[name = tensor<string, []>("x_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 960, 1, 1]> up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [230400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638507840))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638738304))), name = tensor<string, []>("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 960, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638738496)))];
            tensor<fp16, [2, 320, 64, 64]> x_23_cast = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4001, groups = var_3945, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_3999, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_449_cast)[name = tensor<string, []>("x_23_cast")];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_283_cast = add(x = x_23_cast, y = hidden_states_281_cast)[name = tensor<string, []>("hidden_states_283_cast")];
            tensor<int32, [5]> reshape_212_shape_0 = const()[name = tensor<string, []>("reshape_212_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_212_cast = reshape(shape = reshape_212_shape_0, x = hidden_states_283_cast)[name = tensor<string, []>("reshape_212_cast")];
            tensor<int32, [3]> reduce_mean_159_axes_0 = const()[name = tensor<string, []>("reduce_mean_159_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_159_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_159_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_159_cast = reduce_mean(axes = reduce_mean_159_axes_0, keep_dims = reduce_mean_159_keep_dims_0, x = reshape_212_cast)[name = tensor<string, []>("reduce_mean_159_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_106_cast = sub(x = reshape_212_cast, y = reduce_mean_159_cast)[name = tensor<string, []>("sub_106_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_53_cast = square(x = sub_106_cast)[name = tensor<string, []>("square_53_cast")];
            tensor<int32, [3]> reduce_mean_161_axes_0 = const()[name = tensor<string, []>("reduce_mean_161_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_161_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_161_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_161_cast = reduce_mean(axes = reduce_mean_161_axes_0, keep_dims = reduce_mean_161_keep_dims_0, x = square_53_cast)[name = tensor<string, []>("reduce_mean_161_cast")];
            tensor<fp16, []> add_106_y_0_to_fp16 = const()[name = tensor<string, []>("add_106_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_106_cast = add(x = reduce_mean_161_cast, y = add_106_y_0_to_fp16)[name = tensor<string, []>("add_106_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_53_cast = sqrt(x = add_106_cast)[name = tensor<string, []>("sqrt_53_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_53_cast = real_div(x = sub_106_cast, y = sqrt_53_cast)[name = tensor<string, []>("real_div_53_cast")];
            tensor<int32, [4]> reshape_213_shape_0 = const()[name = tensor<string, []>("reshape_213_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_213_cast = reshape(shape = reshape_213_shape_0, x = real_div_53_cast)[name = tensor<string, []>("reshape_213_cast")];
            tensor<fp16, [320]> add_107_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_107_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638739200)))];
            tensor<fp16, [320]> add_107_beta_0_to_fp16 = const()[name = tensor<string, []>("add_107_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638739904)))];
            tensor<fp16, []> add_107_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_107_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_107_cast = batch_norm(beta = add_107_beta_0_to_fp16, epsilon = add_107_epsilon_0_to_fp16, gamma = add_107_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_213_cast)[name = tensor<string, []>("add_107_cast")];
            tensor<int32, [2]> var_4021 = const()[name = tensor<string, []>("op_4021"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4023 = const()[name = tensor<string, []>("op_4023"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638740608))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638817472))), name = tensor<string, []>("up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638817664)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_285_cast = conv(bias = up_blocks_3_attentions_0_proj_in_bias_to_fp16, dilations = var_4023, groups = var_3945, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_4021, weight = up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized, x = add_107_cast)[name = tensor<string, []>("hidden_states_285_cast")];
            tensor<int32, [4]> var_4028 = const()[name = tensor<string, []>("op_4028"), val = tensor<int32, [4]>([2, 320, 1, 4096])];
            tensor<fp16, [2, 320, 1, 4096]> inputs_79_cast = reshape(shape = var_4028, x = hidden_states_285_cast)[name = tensor<string, []>("inputs_79_cast")];
            tensor<int32, [1]> var_4038 = const()[name = tensor<string, []>("op_4038"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_79_cast = reduce_mean(axes = var_4038, keep_dims = var_3940, x = inputs_79_cast)[name = tensor<string, []>("channels_mean_79_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_79_cast = sub(x = inputs_79_cast, y = channels_mean_79_cast)[name = tensor<string, []>("zero_mean_79_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4042 = const()[name = tensor<string, []>("op_4042"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4043_cast = reduce_mean(axes = var_4042, keep_dims = var_3940, x = zero_mean_sq_79_cast)[name = tensor<string, []>("op_4043_cast")];
            tensor<fp16, []> var_4044_to_fp16 = const()[name = tensor<string, []>("op_4044_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4045_cast = add(x = var_4043_cast, y = var_4044_to_fp16)[name = tensor<string, []>("op_4045_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, 4096]> denom_79_cast = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_4045_cast)[name = tensor<string, []>("denom_79_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_79_cast = mul(x = zero_mean_79_cast, y = denom_79_cast)[name = tensor<string, []>("out_79_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4049_to_fp16 = const()[name = tensor<string, []>("op_4049_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638818368)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4050_cast = add(x = out_79_cast, y = var_4049_to_fp16)[name = tensor<string, []>("op_4050_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4052_to_fp16 = const()[name = tensor<string, []>("op_4052_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638819072)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_287_cast = mul(x = var_4050_cast, y = var_4052_to_fp16)[name = tensor<string, []>("hidden_states_287_cast")];
            tensor<int32, [2]> var_4059 = const()[name = tensor<string, []>("op_4059"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4061 = const()[name = tensor<string, []>("op_4061"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638819776))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638896640))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_53_cast = conv(dilations = var_4061, groups = var_3945, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_4059, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor<string, []>("q_53_cast")];
            tensor<int32, [2]> var_4065 = const()[name = tensor<string, []>("op_4065"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4067 = const()[name = tensor<string, []>("op_4067"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638896832))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638973696))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> k_53_cast = conv(dilations = var_4067, groups = var_3945, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_4065, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor<string, []>("k_53_cast")];
            tensor<int32, [2]> var_4071 = const()[name = tensor<string, []>("op_4071"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4073 = const()[name = tensor<string, []>("op_4073"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(638973888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639050752))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> v_53_cast = conv(dilations = var_4073, groups = var_3945, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_4071, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor<string, []>("v_53_cast")];
            tensor<int32, [4]> var_4077 = const()[name = tensor<string, []>("op_4077"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4078_cast = reshape(shape = var_4077, x = q_53_cast)[name = tensor<string, []>("op_4078_cast")];
            tensor<int32, [4]> var_4079 = const()[name = tensor<string, []>("op_4079"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4080_cast = reshape(shape = var_4079, x = k_53_cast)[name = tensor<string, []>("op_4080_cast")];
            tensor<int32, [4]> var_4081 = const()[name = tensor<string, []>("op_4081"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4082_cast = reshape(shape = var_4081, x = v_53_cast)[name = tensor<string, []>("op_4082_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, 5, 4096, 4096]> attn_weights_105_cast = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_4078_cast, y = var_4080_cast)[name = tensor<string, []>("attn_weights_105_cast")];
            tensor<fp16, []> var_3936_to_fp16 = const()[name = tensor<string, []>("op_3936_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 5, 4096, 4096]> attn_weights_107_cast = mul(x = attn_weights_105_cast, y = var_3936_to_fp16)[name = tensor<string, []>("attn_weights_107_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> var_4086_cast = softmax(axis = var_3929, x = attn_weights_107_cast)[name = tensor<string, []>("op_4086_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, 5, 64, 4096]> attn_53_cast = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_4082_cast, y = var_4086_cast)[name = tensor<string, []>("attn_53_cast")];
            tensor<int32, [4]> var_4090 = const()[name = tensor<string, []>("op_4090"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_465_cast = reshape(shape = var_4090, x = attn_53_cast)[name = tensor<string, []>("input_465_cast")];
            tensor<int32, [2]> var_4095 = const()[name = tensor<string, []>("op_4095"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4097 = const()[name = tensor<string, []>("op_4097"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4099_pad_type_0 = const()[name = tensor<string, []>("op_4099_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4099_pad_0 = const()[name = tensor<string, []>("op_4099_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639050944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639127808))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639128000)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4099_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4097, groups = var_3945, pad = var_4099_pad_0, pad_type = var_4099_pad_type_0, strides = var_4095, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_465_cast)[name = tensor<string, []>("op_4099_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_81_cast = add(x = var_4099_cast, y = inputs_79_cast)[name = tensor<string, []>("inputs_81_cast")];
            tensor<int32, [1]> var_4103 = const()[name = tensor<string, []>("op_4103"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_81_cast = reduce_mean(axes = var_4103, keep_dims = var_3940, x = inputs_81_cast)[name = tensor<string, []>("channels_mean_81_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_81_cast = sub(x = inputs_81_cast, y = channels_mean_81_cast)[name = tensor<string, []>("zero_mean_81_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4107 = const()[name = tensor<string, []>("op_4107"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4108_cast = reduce_mean(axes = var_4107, keep_dims = var_3940, x = zero_mean_sq_81_cast)[name = tensor<string, []>("op_4108_cast")];
            tensor<fp16, []> var_4109_to_fp16 = const()[name = tensor<string, []>("op_4109_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4110_cast = add(x = var_4108_cast, y = var_4109_to_fp16)[name = tensor<string, []>("op_4110_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, 4096]> denom_81_cast = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_4110_cast)[name = tensor<string, []>("denom_81_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_81_cast = mul(x = zero_mean_81_cast, y = denom_81_cast)[name = tensor<string, []>("out_81_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4114_to_fp16 = const()[name = tensor<string, []>("op_4114_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639128704)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4115_cast = add(x = out_81_cast, y = var_4114_to_fp16)[name = tensor<string, []>("op_4115_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4117_to_fp16 = const()[name = tensor<string, []>("op_4117_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639129408)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_289_cast = mul(x = var_4115_cast, y = var_4117_to_fp16)[name = tensor<string, []>("hidden_states_289_cast")];
            tensor<int32, [2]> var_4124 = const()[name = tensor<string, []>("op_4124"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4126 = const()[name = tensor<string, []>("op_4126"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639130112))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639206976))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_55_cast = conv(dilations = var_4126, groups = var_3945, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_4124, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_289_cast)[name = tensor<string, []>("q_55_cast")];
            tensor<int32, [2]> var_4130 = const()[name = tensor<string, []>("op_4130"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4132 = const()[name = tensor<string, []>("op_4132"), 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, [320, 1024, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639207168))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639452992))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> k_55_cast = conv(dilations = var_4132, groups = var_3945, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_4130, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_55_cast")];
            tensor<int32, [2]> var_4136 = const()[name = tensor<string, []>("op_4136"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4138 = const()[name = tensor<string, []>("op_4138"), 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, [320, 1024, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639453184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639699008))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> v_55_cast = conv(dilations = var_4138, groups = var_3945, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_4136, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_55_cast")];
            tensor<int32, [4]> var_4142 = const()[name = tensor<string, []>("op_4142"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4143_cast = reshape(shape = var_4142, x = q_55_cast)[name = tensor<string, []>("op_4143_cast")];
            tensor<int32, [4]> var_4144 = const()[name = tensor<string, []>("op_4144"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_4145_cast = reshape(shape = var_4144, x = k_55_cast)[name = tensor<string, []>("op_4145_cast")];
            tensor<int32, [4]> var_4146 = const()[name = tensor<string, []>("op_4146"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_4147_cast = reshape(shape = var_4146, x = v_55_cast)[name = tensor<string, []>("op_4147_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, 5, 4096, 77]> attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_4143_cast, y = var_4145_cast)[name = tensor<string, []>("attn_weights_109_cast")];
            tensor<fp16, [2, 5, 4096, 77]> attn_weights_111_cast = mul(x = attn_weights_109_cast, y = var_3936_to_fp16)[name = tensor<string, []>("attn_weights_111_cast")];
            tensor<fp16, [2, 5, 4096, 77]> var_4151_cast = softmax(axis = var_3929, x = attn_weights_111_cast)[name = tensor<string, []>("op_4151_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, 5, 64, 4096]> attn_55_cast = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_4147_cast, y = var_4151_cast)[name = tensor<string, []>("attn_55_cast")];
            tensor<int32, [4]> var_4155 = const()[name = tensor<string, []>("op_4155"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_467_cast = reshape(shape = var_4155, x = attn_55_cast)[name = tensor<string, []>("input_467_cast")];
            tensor<int32, [2]> var_4160 = const()[name = tensor<string, []>("op_4160"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4162 = const()[name = tensor<string, []>("op_4162"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4164_pad_type_0 = const()[name = tensor<string, []>("op_4164_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4164_pad_0 = const()[name = tensor<string, []>("op_4164_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639699200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639776064))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639776256)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4164_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4162, groups = var_3945, pad = var_4164_pad_0, pad_type = var_4164_pad_type_0, strides = var_4160, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_467_cast)[name = tensor<string, []>("op_4164_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_83_cast = add(x = var_4164_cast, y = inputs_81_cast)[name = tensor<string, []>("inputs_83_cast")];
            tensor<int32, [1]> var_4168 = const()[name = tensor<string, []>("op_4168"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_83_cast = reduce_mean(axes = var_4168, keep_dims = var_3940, x = inputs_83_cast)[name = tensor<string, []>("channels_mean_83_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_83_cast = sub(x = inputs_83_cast, y = channels_mean_83_cast)[name = tensor<string, []>("zero_mean_83_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4172 = const()[name = tensor<string, []>("op_4172"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4173_cast = reduce_mean(axes = var_4172, keep_dims = var_3940, x = zero_mean_sq_83_cast)[name = tensor<string, []>("op_4173_cast")];
            tensor<fp16, []> var_4174_to_fp16 = const()[name = tensor<string, []>("op_4174_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4175_cast = add(x = var_4173_cast, y = var_4174_to_fp16)[name = tensor<string, []>("op_4175_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, 4096]> denom_83_cast = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_4175_cast)[name = tensor<string, []>("denom_83_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_83_cast = mul(x = zero_mean_83_cast, y = denom_83_cast)[name = tensor<string, []>("out_83_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4179_to_fp16 = const()[name = tensor<string, []>("op_4179_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639776960)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4180_cast = add(x = out_83_cast, y = var_4179_to_fp16)[name = tensor<string, []>("op_4180_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4182_to_fp16 = const()[name = tensor<string, []>("op_4182_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(639777664)))];
            tensor<fp16, [2, 320, 1, 4096]> input_469_cast = mul(x = var_4180_cast, y = var_4182_to_fp16)[name = tensor<string, []>("input_469_cast")];
            tensor<int32, [2]> var_4190 = const()[name = tensor<string, []>("op_4190"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4192 = const()[name = tensor<string, []>("op_4192"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4194_pad_type_0 = const()[name = tensor<string, []>("op_4194_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4194_pad_0 = const()[name = tensor<string, []>("op_4194_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [2560, 320, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_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, []>(639778368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640392832))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([2560, 320, 1, 1])];
            tensor<fp16, [2560]> up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640393024))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640395008))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2, 2560, 1, 4096]> var_4194_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4192, groups = var_3945, pad = var_4194_pad_0, pad_type = var_4194_pad_type_0, strides = var_4190, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_469_cast)[name = tensor<string, []>("op_4194_cast")];
            tensor<int32, [2]> var_4195_split_sizes_0 = const()[name = tensor<string, []>("op_4195_split_sizes_0"), val = tensor<int32, [2]>([1280, 1280])];
            tensor<int32, []> var_4195_axis_0 = const()[name = tensor<string, []>("op_4195_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 1280, 1, 4096]> var_4195_cast_0, tensor<fp16, [2, 1280, 1, 4096]> var_4195_cast_1 = split(axis = var_4195_axis_0, split_sizes = var_4195_split_sizes_0, x = var_4194_cast)[name = tensor<string, []>("op_4195_cast")];
            tensor<string, []> var_4197_mode_0 = const()[name = tensor<string, []>("op_4197_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 4096]> var_4197_cast = gelu(mode = var_4197_mode_0, x = var_4195_cast_1)[name = tensor<string, []>("op_4197_cast")];
            tensor<fp16, [2, 1280, 1, 4096]> input_471_cast = mul(x = var_4195_cast_0, y = var_4197_cast)[name = tensor<string, []>("input_471_cast")];
            tensor<int32, [2]> var_4201 = const()[name = tensor<string, []>("op_4201"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4203 = const()[name = tensor<string, []>("op_4203"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4205_pad_type_0 = const()[name = tensor<string, []>("op_4205_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4205_pad_0 = const()[name = tensor<string, []>("op_4205_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640395200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640702464))), name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640702656)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4205_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4203, groups = var_3945, pad = var_4205_pad_0, pad_type = var_4205_pad_type_0, strides = var_4201, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_471_cast)[name = tensor<string, []>("op_4205_cast")];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_293_cast = add(x = var_4205_cast, y = inputs_83_cast)[name = tensor<string, []>("hidden_states_293_cast")];
            tensor<int32, [4]> var_4207 = const()[name = tensor<string, []>("op_4207"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> input_473_cast = reshape(shape = var_4207, x = hidden_states_293_cast)[name = tensor<string, []>("input_473_cast")];
            tensor<int32, [2]> var_4211 = const()[name = tensor<string, []>("op_4211"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4213 = const()[name = tensor<string, []>("op_4213"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_295_pad_type_0 = const()[name = tensor<string, []>("hidden_states_295_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_295_pad_0 = const()[name = tensor<string, []>("hidden_states_295_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640703360))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640780224))), name = tensor<string, []>("up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640780416)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_295_cast = conv(bias = up_blocks_3_attentions_0_proj_out_bias_to_fp16, dilations = var_4213, groups = var_3945, pad = hidden_states_295_pad_0, pad_type = hidden_states_295_pad_type_0, strides = var_4211, weight = up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized, x = input_473_cast)[name = tensor<string, []>("hidden_states_295_cast")];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_297_cast = add(x = hidden_states_295_cast, y = hidden_states_283_cast)[name = tensor<string, []>("hidden_states_297_cast")];
            tensor<bool, []> input_475_interleave_0 = const()[name = tensor<string, []>("input_475_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 640, 64, 64]> input_475_cast = concat(axis = var_3945, interleave = input_475_interleave_0, values = (hidden_states_297_cast, input_35_cast))[name = tensor<string, []>("input_475_cast")];
            tensor<int32, [5]> reshape_216_shape_0 = const()[name = tensor<string, []>("reshape_216_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_216_cast = reshape(shape = reshape_216_shape_0, x = input_475_cast)[name = tensor<string, []>("reshape_216_cast")];
            tensor<int32, [3]> reduce_mean_162_axes_0 = const()[name = tensor<string, []>("reduce_mean_162_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_162_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_162_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_162_cast = reduce_mean(axes = reduce_mean_162_axes_0, keep_dims = reduce_mean_162_keep_dims_0, x = reshape_216_cast)[name = tensor<string, []>("reduce_mean_162_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_108_cast = sub(x = reshape_216_cast, y = reduce_mean_162_cast)[name = tensor<string, []>("sub_108_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_54_cast = square(x = sub_108_cast)[name = tensor<string, []>("square_54_cast")];
            tensor<int32, [3]> reduce_mean_164_axes_0 = const()[name = tensor<string, []>("reduce_mean_164_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_164_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_164_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_164_cast = reduce_mean(axes = reduce_mean_164_axes_0, keep_dims = reduce_mean_164_keep_dims_0, x = square_54_cast)[name = tensor<string, []>("reduce_mean_164_cast")];
            tensor<fp16, []> add_108_y_0_to_fp16 = const()[name = tensor<string, []>("add_108_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_108_cast = add(x = reduce_mean_164_cast, y = add_108_y_0_to_fp16)[name = tensor<string, []>("add_108_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_54_cast = sqrt(x = add_108_cast)[name = tensor<string, []>("sqrt_54_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_54_cast = real_div(x = sub_108_cast, y = sqrt_54_cast)[name = tensor<string, []>("real_div_54_cast")];
            tensor<int32, [4]> reshape_217_shape_0 = const()[name = tensor<string, []>("reshape_217_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_217_cast = reshape(shape = reshape_217_shape_0, x = real_div_54_cast)[name = tensor<string, []>("reshape_217_cast")];
            tensor<fp16, [640]> add_109_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_109_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640781120)))];
            tensor<fp16, [640]> add_109_beta_0_to_fp16 = const()[name = tensor<string, []>("add_109_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640782464)))];
            tensor<fp16, []> add_109_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_109_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_109_cast = batch_norm(beta = add_109_beta_0_to_fp16, epsilon = add_109_epsilon_0_to_fp16, gamma = add_109_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_217_cast)[name = tensor<string, []>("add_109_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_479_cast = silu(x = add_109_cast)[name = tensor<string, []>("input_479_cast")];
            tensor<int32, [2]> var_4231 = const()[name = tensor<string, []>("op_4231"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4233 = const()[name = tensor<string, []>("op_4233"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_299_pad_type_0 = const()[name = tensor<string, []>("hidden_states_299_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_299_pad_0 = const()[name = tensor<string, []>("hidden_states_299_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 640, 3, 3]> up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1382400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640783808))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642166272))), name = tensor<string, []>("up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 640, 3, 3])];
            tensor<fp16, [320]> up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642166464)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_299_cast = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_4233, groups = var_3945, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_4231, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_479_cast)[name = tensor<string, []>("hidden_states_299_cast")];
            tensor<int32, [2]> var_4239 = const()[name = tensor<string, []>("op_4239"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4241 = const()[name = tensor<string, []>("op_4241"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_41_pad_type_0 = const()[name = tensor<string, []>("temb_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_41_pad_0 = const()[name = tensor<string, []>("temb_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642167168))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642474432))), name = tensor<string, []>("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642474624)))];
            tensor<fp16, [2, 320, 1, 1]> temb_41_cast = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4241, groups = var_3945, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_4239, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_41_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_483_cast = add(x = hidden_states_299_cast, y = temb_41_cast)[name = tensor<string, []>("input_483_cast")];
            tensor<int32, [5]> reshape_220_shape_0 = const()[name = tensor<string, []>("reshape_220_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_220_cast = reshape(shape = reshape_220_shape_0, x = input_483_cast)[name = tensor<string, []>("reshape_220_cast")];
            tensor<int32, [3]> reduce_mean_165_axes_0 = const()[name = tensor<string, []>("reduce_mean_165_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_165_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_165_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_165_cast = reduce_mean(axes = reduce_mean_165_axes_0, keep_dims = reduce_mean_165_keep_dims_0, x = reshape_220_cast)[name = tensor<string, []>("reduce_mean_165_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_110_cast = sub(x = reshape_220_cast, y = reduce_mean_165_cast)[name = tensor<string, []>("sub_110_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_55_cast = square(x = sub_110_cast)[name = tensor<string, []>("square_55_cast")];
            tensor<int32, [3]> reduce_mean_167_axes_0 = const()[name = tensor<string, []>("reduce_mean_167_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_167_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_167_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_167_cast = reduce_mean(axes = reduce_mean_167_axes_0, keep_dims = reduce_mean_167_keep_dims_0, x = square_55_cast)[name = tensor<string, []>("reduce_mean_167_cast")];
            tensor<fp16, []> add_110_y_0_to_fp16 = const()[name = tensor<string, []>("add_110_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_110_cast = add(x = reduce_mean_167_cast, y = add_110_y_0_to_fp16)[name = tensor<string, []>("add_110_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_55_cast = sqrt(x = add_110_cast)[name = tensor<string, []>("sqrt_55_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_55_cast = real_div(x = sub_110_cast, y = sqrt_55_cast)[name = tensor<string, []>("real_div_55_cast")];
            tensor<int32, [4]> reshape_221_shape_0 = const()[name = tensor<string, []>("reshape_221_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_221_cast = reshape(shape = reshape_221_shape_0, x = real_div_55_cast)[name = tensor<string, []>("reshape_221_cast")];
            tensor<fp16, [320]> add_111_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_111_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642475328)))];
            tensor<fp16, [320]> add_111_beta_0_to_fp16 = const()[name = tensor<string, []>("add_111_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642476032)))];
            tensor<fp16, []> add_111_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_111_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_111_cast = batch_norm(beta = add_111_beta_0_to_fp16, epsilon = add_111_epsilon_0_to_fp16, gamma = add_111_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_221_cast)[name = tensor<string, []>("add_111_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_487_cast = silu(x = add_111_cast)[name = tensor<string, []>("input_487_cast")];
            tensor<int32, [2]> var_4251 = const()[name = tensor<string, []>("op_4251"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4253 = const()[name = tensor<string, []>("op_4253"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_301_pad_type_0 = const()[name = tensor<string, []>("hidden_states_301_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_301_pad_0 = const()[name = tensor<string, []>("hidden_states_301_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(642476736))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643168000))), name = tensor<string, []>("up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            tensor<fp16, [320]> up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643168192)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_301_cast = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_4253, groups = var_3945, pad = hidden_states_301_pad_0, pad_type = hidden_states_301_pad_type_0, strides = var_4251, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_487_cast)[name = tensor<string, []>("hidden_states_301_cast")];
            tensor<int32, [2]> var_4258 = const()[name = tensor<string, []>("op_4258"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4260 = const()[name = tensor<string, []>("op_4260"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_25_pad_type_0 = const()[name = tensor<string, []>("x_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_25_pad_0 = const()[name = tensor<string, []>("x_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 640, 1, 1]> up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [153600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643168896))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643322560))), name = tensor<string, []>("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 640, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643322752)))];
            tensor<fp16, [2, 320, 64, 64]> x_25_cast = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4260, groups = var_3945, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_4258, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_475_cast)[name = tensor<string, []>("x_25_cast")];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_303_cast = add(x = x_25_cast, y = hidden_states_301_cast)[name = tensor<string, []>("hidden_states_303_cast")];
            tensor<int32, [5]> reshape_224_shape_0 = const()[name = tensor<string, []>("reshape_224_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_224_cast = reshape(shape = reshape_224_shape_0, x = hidden_states_303_cast)[name = tensor<string, []>("reshape_224_cast")];
            tensor<int32, [3]> reduce_mean_168_axes_0 = const()[name = tensor<string, []>("reduce_mean_168_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_168_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_168_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_168_cast = reduce_mean(axes = reduce_mean_168_axes_0, keep_dims = reduce_mean_168_keep_dims_0, x = reshape_224_cast)[name = tensor<string, []>("reduce_mean_168_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_112_cast = sub(x = reshape_224_cast, y = reduce_mean_168_cast)[name = tensor<string, []>("sub_112_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_56_cast = square(x = sub_112_cast)[name = tensor<string, []>("square_56_cast")];
            tensor<int32, [3]> reduce_mean_170_axes_0 = const()[name = tensor<string, []>("reduce_mean_170_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_170_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_170_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_170_cast = reduce_mean(axes = reduce_mean_170_axes_0, keep_dims = reduce_mean_170_keep_dims_0, x = square_56_cast)[name = tensor<string, []>("reduce_mean_170_cast")];
            tensor<fp16, []> add_112_y_0_to_fp16 = const()[name = tensor<string, []>("add_112_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_112_cast = add(x = reduce_mean_170_cast, y = add_112_y_0_to_fp16)[name = tensor<string, []>("add_112_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_56_cast = sqrt(x = add_112_cast)[name = tensor<string, []>("sqrt_56_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_56_cast = real_div(x = sub_112_cast, y = sqrt_56_cast)[name = tensor<string, []>("real_div_56_cast")];
            tensor<int32, [4]> reshape_225_shape_0 = const()[name = tensor<string, []>("reshape_225_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_225_cast = reshape(shape = reshape_225_shape_0, x = real_div_56_cast)[name = tensor<string, []>("reshape_225_cast")];
            tensor<fp16, [320]> add_113_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_113_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643323456)))];
            tensor<fp16, [320]> add_113_beta_0_to_fp16 = const()[name = tensor<string, []>("add_113_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643324160)))];
            tensor<fp16, []> add_113_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_113_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_113_cast = batch_norm(beta = add_113_beta_0_to_fp16, epsilon = add_113_epsilon_0_to_fp16, gamma = add_113_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_225_cast)[name = tensor<string, []>("add_113_cast")];
            tensor<int32, [2]> var_4280 = const()[name = tensor<string, []>("op_4280"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4282 = const()[name = tensor<string, []>("op_4282"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_305_pad_type_0 = const()[name = tensor<string, []>("hidden_states_305_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_305_pad_0 = const()[name = tensor<string, []>("hidden_states_305_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643324864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643401728))), name = tensor<string, []>("up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643401920)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_305_cast = conv(bias = up_blocks_3_attentions_1_proj_in_bias_to_fp16, dilations = var_4282, groups = var_3945, pad = hidden_states_305_pad_0, pad_type = hidden_states_305_pad_type_0, strides = var_4280, weight = up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized, x = add_113_cast)[name = tensor<string, []>("hidden_states_305_cast")];
            tensor<int32, [4]> var_4287 = const()[name = tensor<string, []>("op_4287"), val = tensor<int32, [4]>([2, 320, 1, 4096])];
            tensor<fp16, [2, 320, 1, 4096]> inputs_85_cast = reshape(shape = var_4287, x = hidden_states_305_cast)[name = tensor<string, []>("inputs_85_cast")];
            tensor<int32, [1]> var_4297 = const()[name = tensor<string, []>("op_4297"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_85_cast = reduce_mean(axes = var_4297, keep_dims = var_3940, x = inputs_85_cast)[name = tensor<string, []>("channels_mean_85_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_85_cast = sub(x = inputs_85_cast, y = channels_mean_85_cast)[name = tensor<string, []>("zero_mean_85_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4301 = const()[name = tensor<string, []>("op_4301"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4302_cast = reduce_mean(axes = var_4301, keep_dims = var_3940, x = zero_mean_sq_85_cast)[name = tensor<string, []>("op_4302_cast")];
            tensor<fp16, []> var_4303_to_fp16 = const()[name = tensor<string, []>("op_4303_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4304_cast = add(x = var_4302_cast, y = var_4303_to_fp16)[name = tensor<string, []>("op_4304_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, 4096]> denom_85_cast = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_4304_cast)[name = tensor<string, []>("denom_85_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_85_cast = mul(x = zero_mean_85_cast, y = denom_85_cast)[name = tensor<string, []>("out_85_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4308_to_fp16 = const()[name = tensor<string, []>("op_4308_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643402624)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4309_cast = add(x = out_85_cast, y = var_4308_to_fp16)[name = tensor<string, []>("op_4309_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4311_to_fp16 = const()[name = tensor<string, []>("op_4311_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643403328)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_307_cast = mul(x = var_4309_cast, y = var_4311_to_fp16)[name = tensor<string, []>("hidden_states_307_cast")];
            tensor<int32, [2]> var_4318 = const()[name = tensor<string, []>("op_4318"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4320 = const()[name = tensor<string, []>("op_4320"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643404032))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643480896))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_57_cast = conv(dilations = var_4320, groups = var_3945, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_4318, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor<string, []>("q_57_cast")];
            tensor<int32, [2]> var_4324 = const()[name = tensor<string, []>("op_4324"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4326 = const()[name = tensor<string, []>("op_4326"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643481088))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643557952))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> k_57_cast = conv(dilations = var_4326, groups = var_3945, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_4324, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor<string, []>("k_57_cast")];
            tensor<int32, [2]> var_4330 = const()[name = tensor<string, []>("op_4330"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4332 = const()[name = tensor<string, []>("op_4332"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643558144))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643635008))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> v_57_cast = conv(dilations = var_4332, groups = var_3945, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_4330, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor<string, []>("v_57_cast")];
            tensor<int32, [4]> var_4336 = const()[name = tensor<string, []>("op_4336"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4337_cast = reshape(shape = var_4336, x = q_57_cast)[name = tensor<string, []>("op_4337_cast")];
            tensor<int32, [4]> var_4338 = const()[name = tensor<string, []>("op_4338"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4339_cast = reshape(shape = var_4338, x = k_57_cast)[name = tensor<string, []>("op_4339_cast")];
            tensor<int32, [4]> var_4340 = const()[name = tensor<string, []>("op_4340"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4341_cast = reshape(shape = var_4340, x = v_57_cast)[name = tensor<string, []>("op_4341_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, 5, 4096, 4096]> attn_weights_113_cast = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_4337_cast, y = var_4339_cast)[name = tensor<string, []>("attn_weights_113_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> attn_weights_115_cast = mul(x = attn_weights_113_cast, y = var_3936_to_fp16)[name = tensor<string, []>("attn_weights_115_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> var_4345_cast = softmax(axis = var_3929, x = attn_weights_115_cast)[name = tensor<string, []>("op_4345_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, 5, 64, 4096]> attn_57_cast = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_4341_cast, y = var_4345_cast)[name = tensor<string, []>("attn_57_cast")];
            tensor<int32, [4]> var_4349 = const()[name = tensor<string, []>("op_4349"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_491_cast = reshape(shape = var_4349, x = attn_57_cast)[name = tensor<string, []>("input_491_cast")];
            tensor<int32, [2]> var_4354 = const()[name = tensor<string, []>("op_4354"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4356 = const()[name = tensor<string, []>("op_4356"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4358_pad_type_0 = const()[name = tensor<string, []>("op_4358_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4358_pad_0 = const()[name = tensor<string, []>("op_4358_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643635200))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643712064))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643712256)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4358_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4356, groups = var_3945, pad = var_4358_pad_0, pad_type = var_4358_pad_type_0, strides = var_4354, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_491_cast)[name = tensor<string, []>("op_4358_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_87_cast = add(x = var_4358_cast, y = inputs_85_cast)[name = tensor<string, []>("inputs_87_cast")];
            tensor<int32, [1]> var_4362 = const()[name = tensor<string, []>("op_4362"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_87_cast = reduce_mean(axes = var_4362, keep_dims = var_3940, x = inputs_87_cast)[name = tensor<string, []>("channels_mean_87_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_87_cast = sub(x = inputs_87_cast, y = channels_mean_87_cast)[name = tensor<string, []>("zero_mean_87_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4366 = const()[name = tensor<string, []>("op_4366"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4367_cast = reduce_mean(axes = var_4366, keep_dims = var_3940, x = zero_mean_sq_87_cast)[name = tensor<string, []>("op_4367_cast")];
            tensor<fp16, []> var_4368_to_fp16 = const()[name = tensor<string, []>("op_4368_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4369_cast = add(x = var_4367_cast, y = var_4368_to_fp16)[name = tensor<string, []>("op_4369_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, 4096]> denom_87_cast = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_4369_cast)[name = tensor<string, []>("denom_87_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_87_cast = mul(x = zero_mean_87_cast, y = denom_87_cast)[name = tensor<string, []>("out_87_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4373_to_fp16 = const()[name = tensor<string, []>("op_4373_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643712960)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4374_cast = add(x = out_87_cast, y = var_4373_to_fp16)[name = tensor<string, []>("op_4374_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4376_to_fp16 = const()[name = tensor<string, []>("op_4376_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643713664)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_309_cast = mul(x = var_4374_cast, y = var_4376_to_fp16)[name = tensor<string, []>("hidden_states_309_cast")];
            tensor<int32, [2]> var_4383 = const()[name = tensor<string, []>("op_4383"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4385 = const()[name = tensor<string, []>("op_4385"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643714368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643791232))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_59_cast = conv(dilations = var_4385, groups = var_3945, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_4383, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_309_cast)[name = tensor<string, []>("q_59_cast")];
            tensor<int32, [2]> var_4389 = const()[name = tensor<string, []>("op_4389"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4391 = const()[name = tensor<string, []>("op_4391"), 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, [320, 1024, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643791424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644037248))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> k_59_cast = conv(dilations = var_4391, groups = var_3945, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_4389, weight = up_blocks_3_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_4395 = const()[name = tensor<string, []>("op_4395"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4397 = const()[name = tensor<string, []>("op_4397"), 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, [320, 1024, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644037440))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644283264))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> v_59_cast = conv(dilations = var_4397, groups = var_3945, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_4395, weight = up_blocks_3_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_4401 = const()[name = tensor<string, []>("op_4401"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4402_cast = reshape(shape = var_4401, x = q_59_cast)[name = tensor<string, []>("op_4402_cast")];
            tensor<int32, [4]> var_4403 = const()[name = tensor<string, []>("op_4403"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_4404_cast = reshape(shape = var_4403, x = k_59_cast)[name = tensor<string, []>("op_4404_cast")];
            tensor<int32, [4]> var_4405 = const()[name = tensor<string, []>("op_4405"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_4406_cast = reshape(shape = var_4405, x = v_59_cast)[name = tensor<string, []>("op_4406_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, 5, 4096, 77]> attn_weights_117_cast = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_4402_cast, y = var_4404_cast)[name = tensor<string, []>("attn_weights_117_cast")];
            tensor<fp16, [2, 5, 4096, 77]> attn_weights_119_cast = mul(x = attn_weights_117_cast, y = var_3936_to_fp16)[name = tensor<string, []>("attn_weights_119_cast")];
            tensor<fp16, [2, 5, 4096, 77]> var_4410_cast = softmax(axis = var_3929, x = attn_weights_119_cast)[name = tensor<string, []>("op_4410_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, 5, 64, 4096]> attn_59_cast = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_4406_cast, y = var_4410_cast)[name = tensor<string, []>("attn_59_cast")];
            tensor<int32, [4]> var_4414 = const()[name = tensor<string, []>("op_4414"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_493_cast = reshape(shape = var_4414, x = attn_59_cast)[name = tensor<string, []>("input_493_cast")];
            tensor<int32, [2]> var_4419 = const()[name = tensor<string, []>("op_4419"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4421 = const()[name = tensor<string, []>("op_4421"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4423_pad_type_0 = const()[name = tensor<string, []>("op_4423_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4423_pad_0 = const()[name = tensor<string, []>("op_4423_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644283456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644360320))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644360512)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4423_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4421, groups = var_3945, pad = var_4423_pad_0, pad_type = var_4423_pad_type_0, strides = var_4419, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_493_cast)[name = tensor<string, []>("op_4423_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_89_cast = add(x = var_4423_cast, y = inputs_87_cast)[name = tensor<string, []>("inputs_89_cast")];
            tensor<int32, [1]> var_4427 = const()[name = tensor<string, []>("op_4427"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_89_cast = reduce_mean(axes = var_4427, keep_dims = var_3940, x = inputs_89_cast)[name = tensor<string, []>("channels_mean_89_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_89_cast = sub(x = inputs_89_cast, y = channels_mean_89_cast)[name = tensor<string, []>("zero_mean_89_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4431 = const()[name = tensor<string, []>("op_4431"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4432_cast = reduce_mean(axes = var_4431, keep_dims = var_3940, x = zero_mean_sq_89_cast)[name = tensor<string, []>("op_4432_cast")];
            tensor<fp16, []> var_4433_to_fp16 = const()[name = tensor<string, []>("op_4433_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4434_cast = add(x = var_4432_cast, y = var_4433_to_fp16)[name = tensor<string, []>("op_4434_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, 4096]> denom_89_cast = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_4434_cast)[name = tensor<string, []>("denom_89_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_89_cast = mul(x = zero_mean_89_cast, y = denom_89_cast)[name = tensor<string, []>("out_89_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4438_to_fp16 = const()[name = tensor<string, []>("op_4438_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644361216)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4439_cast = add(x = out_89_cast, y = var_4438_to_fp16)[name = tensor<string, []>("op_4439_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4441_to_fp16 = const()[name = tensor<string, []>("op_4441_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644361920)))];
            tensor<fp16, [2, 320, 1, 4096]> input_495_cast = mul(x = var_4439_cast, y = var_4441_to_fp16)[name = tensor<string, []>("input_495_cast")];
            tensor<int32, [2]> var_4449 = const()[name = tensor<string, []>("op_4449"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4451 = const()[name = tensor<string, []>("op_4451"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4453_pad_type_0 = const()[name = tensor<string, []>("op_4453_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4453_pad_0 = const()[name = tensor<string, []>("op_4453_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [2560, 320, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_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, []>(644362624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644977088))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([2560, 320, 1, 1])];
            tensor<fp16, [2560]> up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644977280))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644979264))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2, 2560, 1, 4096]> var_4453_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4451, groups = var_3945, pad = var_4453_pad_0, pad_type = var_4453_pad_type_0, strides = var_4449, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_495_cast)[name = tensor<string, []>("op_4453_cast")];
            tensor<int32, [2]> var_4454_split_sizes_0 = const()[name = tensor<string, []>("op_4454_split_sizes_0"), val = tensor<int32, [2]>([1280, 1280])];
            tensor<int32, []> var_4454_axis_0 = const()[name = tensor<string, []>("op_4454_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 1280, 1, 4096]> var_4454_cast_0, tensor<fp16, [2, 1280, 1, 4096]> var_4454_cast_1 = split(axis = var_4454_axis_0, split_sizes = var_4454_split_sizes_0, x = var_4453_cast)[name = tensor<string, []>("op_4454_cast")];
            tensor<string, []> var_4456_mode_0 = const()[name = tensor<string, []>("op_4456_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 4096]> var_4456_cast = gelu(mode = var_4456_mode_0, x = var_4454_cast_1)[name = tensor<string, []>("op_4456_cast")];
            tensor<fp16, [2, 1280, 1, 4096]> input_497_cast = mul(x = var_4454_cast_0, y = var_4456_cast)[name = tensor<string, []>("input_497_cast")];
            tensor<int32, [2]> var_4460 = const()[name = tensor<string, []>("op_4460"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4462 = const()[name = tensor<string, []>("op_4462"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4464_pad_type_0 = const()[name = tensor<string, []>("op_4464_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4464_pad_0 = const()[name = tensor<string, []>("op_4464_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(644979456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645286720))), name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645286912)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4464_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4462, groups = var_3945, pad = var_4464_pad_0, pad_type = var_4464_pad_type_0, strides = var_4460, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_497_cast)[name = tensor<string, []>("op_4464_cast")];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_313_cast = add(x = var_4464_cast, y = inputs_89_cast)[name = tensor<string, []>("hidden_states_313_cast")];
            tensor<int32, [4]> var_4466 = const()[name = tensor<string, []>("op_4466"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> input_499_cast = reshape(shape = var_4466, x = hidden_states_313_cast)[name = tensor<string, []>("input_499_cast")];
            tensor<int32, [2]> var_4470 = const()[name = tensor<string, []>("op_4470"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4472 = const()[name = tensor<string, []>("op_4472"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_315_pad_type_0 = const()[name = tensor<string, []>("hidden_states_315_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_315_pad_0 = const()[name = tensor<string, []>("hidden_states_315_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645287616))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645364480))), name = tensor<string, []>("up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645364672)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_315_cast = conv(bias = up_blocks_3_attentions_1_proj_out_bias_to_fp16, dilations = var_4472, groups = var_3945, pad = hidden_states_315_pad_0, pad_type = hidden_states_315_pad_type_0, strides = var_4470, weight = up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized, x = input_499_cast)[name = tensor<string, []>("hidden_states_315_cast")];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_317_cast = add(x = hidden_states_315_cast, y = hidden_states_303_cast)[name = tensor<string, []>("hidden_states_317_cast")];
            tensor<bool, []> input_501_interleave_0 = const()[name = tensor<string, []>("input_501_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 640, 64, 64]> input_501_cast = concat(axis = var_3945, interleave = input_501_interleave_0, values = (hidden_states_317_cast, input_7_cast))[name = tensor<string, []>("input_501_cast")];
            tensor<int32, [5]> reshape_228_shape_0 = const()[name = tensor<string, []>("reshape_228_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 64, 64])];
            tensor<fp16, [2, 32, 20, 64, 64]> reshape_228_cast = reshape(shape = reshape_228_shape_0, x = input_501_cast)[name = tensor<string, []>("reshape_228_cast")];
            tensor<int32, [3]> reduce_mean_171_axes_0 = const()[name = tensor<string, []>("reduce_mean_171_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_171_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_171_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_171_cast = reduce_mean(axes = reduce_mean_171_axes_0, keep_dims = reduce_mean_171_keep_dims_0, x = reshape_228_cast)[name = tensor<string, []>("reduce_mean_171_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> sub_114_cast = sub(x = reshape_228_cast, y = reduce_mean_171_cast)[name = tensor<string, []>("sub_114_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> square_57_cast = square(x = sub_114_cast)[name = tensor<string, []>("square_57_cast")];
            tensor<int32, [3]> reduce_mean_173_axes_0 = const()[name = tensor<string, []>("reduce_mean_173_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_173_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_173_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_173_cast = reduce_mean(axes = reduce_mean_173_axes_0, keep_dims = reduce_mean_173_keep_dims_0, x = square_57_cast)[name = tensor<string, []>("reduce_mean_173_cast")];
            tensor<fp16, []> add_114_y_0_to_fp16 = const()[name = tensor<string, []>("add_114_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_114_cast = add(x = reduce_mean_173_cast, y = add_114_y_0_to_fp16)[name = tensor<string, []>("add_114_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_57_cast = sqrt(x = add_114_cast)[name = tensor<string, []>("sqrt_57_cast")];
            tensor<fp16, [2, 32, 20, 64, 64]> real_div_57_cast = real_div(x = sub_114_cast, y = sqrt_57_cast)[name = tensor<string, []>("real_div_57_cast")];
            tensor<int32, [4]> reshape_229_shape_0 = const()[name = tensor<string, []>("reshape_229_shape_0"), val = tensor<int32, [4]>([2, 640, 64, 64])];
            tensor<fp16, [2, 640, 64, 64]> reshape_229_cast = reshape(shape = reshape_229_shape_0, x = real_div_57_cast)[name = tensor<string, []>("reshape_229_cast")];
            tensor<fp16, [640]> add_115_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_115_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645365376)))];
            tensor<fp16, [640]> add_115_beta_0_to_fp16 = const()[name = tensor<string, []>("add_115_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645366720)))];
            tensor<fp16, []> add_115_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_115_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 64, 64]> add_115_cast = batch_norm(beta = add_115_beta_0_to_fp16, epsilon = add_115_epsilon_0_to_fp16, gamma = add_115_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_229_cast)[name = tensor<string, []>("add_115_cast")];
            tensor<fp16, [2, 640, 64, 64]> input_505_cast = silu(x = add_115_cast)[name = tensor<string, []>("input_505_cast")];
            tensor<int32, [2]> var_4490 = const()[name = tensor<string, []>("op_4490"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4492 = const()[name = tensor<string, []>("op_4492"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_319_pad_type_0 = const()[name = tensor<string, []>("hidden_states_319_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_319_pad_0 = const()[name = tensor<string, []>("hidden_states_319_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 640, 3, 3]> up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1382400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645368064))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(646750528))), name = tensor<string, []>("up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 640, 3, 3])];
            tensor<fp16, [320]> up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(646750720)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_319_cast = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_4492, groups = var_3945, pad = hidden_states_319_pad_0, pad_type = hidden_states_319_pad_type_0, strides = var_4490, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized, x = input_505_cast)[name = tensor<string, []>("hidden_states_319_cast")];
            tensor<int32, [2]> var_4498 = const()[name = tensor<string, []>("op_4498"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4500 = const()[name = tensor<string, []>("op_4500"), 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_3_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(646751424))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647058688))), name = tensor<string, []>("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647058880)))];
            tensor<fp16, [2, 320, 1, 1]> temb_cast = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4500, groups = var_3945, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_4498, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor<string, []>("temb_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_509_cast = add(x = hidden_states_319_cast, y = temb_cast)[name = tensor<string, []>("input_509_cast")];
            tensor<int32, [5]> reshape_232_shape_0 = const()[name = tensor<string, []>("reshape_232_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_232_cast = reshape(shape = reshape_232_shape_0, x = input_509_cast)[name = tensor<string, []>("reshape_232_cast")];
            tensor<int32, [3]> reduce_mean_174_axes_0 = const()[name = tensor<string, []>("reduce_mean_174_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_174_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_174_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_174_cast = reduce_mean(axes = reduce_mean_174_axes_0, keep_dims = reduce_mean_174_keep_dims_0, x = reshape_232_cast)[name = tensor<string, []>("reduce_mean_174_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_116_cast = sub(x = reshape_232_cast, y = reduce_mean_174_cast)[name = tensor<string, []>("sub_116_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_58_cast = square(x = sub_116_cast)[name = tensor<string, []>("square_58_cast")];
            tensor<int32, [3]> reduce_mean_176_axes_0 = const()[name = tensor<string, []>("reduce_mean_176_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_176_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_176_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_176_cast = reduce_mean(axes = reduce_mean_176_axes_0, keep_dims = reduce_mean_176_keep_dims_0, x = square_58_cast)[name = tensor<string, []>("reduce_mean_176_cast")];
            tensor<fp16, []> add_116_y_0_to_fp16 = const()[name = tensor<string, []>("add_116_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_116_cast = add(x = reduce_mean_176_cast, y = add_116_y_0_to_fp16)[name = tensor<string, []>("add_116_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_58_cast = sqrt(x = add_116_cast)[name = tensor<string, []>("sqrt_58_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_58_cast = real_div(x = sub_116_cast, y = sqrt_58_cast)[name = tensor<string, []>("real_div_58_cast")];
            tensor<int32, [4]> reshape_233_shape_0 = const()[name = tensor<string, []>("reshape_233_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_233_cast = reshape(shape = reshape_233_shape_0, x = real_div_58_cast)[name = tensor<string, []>("reshape_233_cast")];
            tensor<fp16, [320]> add_117_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_117_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647059584)))];
            tensor<fp16, [320]> add_117_beta_0_to_fp16 = const()[name = tensor<string, []>("add_117_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647060288)))];
            tensor<fp16, []> add_117_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_117_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_117_cast = batch_norm(beta = add_117_beta_0_to_fp16, epsilon = add_117_epsilon_0_to_fp16, gamma = add_117_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_233_cast)[name = tensor<string, []>("add_117_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_513_cast = silu(x = add_117_cast)[name = tensor<string, []>("input_513_cast")];
            tensor<int32, [2]> var_4510 = const()[name = tensor<string, []>("op_4510"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4512 = const()[name = tensor<string, []>("op_4512"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_321_pad_type_0 = const()[name = tensor<string, []>("hidden_states_321_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_321_pad_0 = const()[name = tensor<string, []>("hidden_states_321_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [691200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647060992))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647752256))), name = tensor<string, []>("up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 3, 3])];
            tensor<fp16, [320]> up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647752448)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_321_cast = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_4512, groups = var_3945, pad = hidden_states_321_pad_0, pad_type = hidden_states_321_pad_type_0, strides = var_4510, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized, x = input_513_cast)[name = tensor<string, []>("hidden_states_321_cast")];
            tensor<int32, [2]> var_4517 = const()[name = tensor<string, []>("op_4517"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4519 = const()[name = tensor<string, []>("op_4519"), 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_3_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [153600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647753152))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647906816))), name = tensor<string, []>("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 640, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647907008)))];
            tensor<fp16, [2, 320, 64, 64]> x_cast = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4519, groups = var_3945, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_4517, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_501_cast)[name = tensor<string, []>("x_cast")];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_323_cast = add(x = x_cast, y = hidden_states_321_cast)[name = tensor<string, []>("hidden_states_323_cast")];
            tensor<int32, [5]> reshape_236_shape_0 = const()[name = tensor<string, []>("reshape_236_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_236_cast = reshape(shape = reshape_236_shape_0, x = hidden_states_323_cast)[name = tensor<string, []>("reshape_236_cast")];
            tensor<int32, [3]> reduce_mean_177_axes_0 = const()[name = tensor<string, []>("reduce_mean_177_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_177_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_177_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_177_cast = reduce_mean(axes = reduce_mean_177_axes_0, keep_dims = reduce_mean_177_keep_dims_0, x = reshape_236_cast)[name = tensor<string, []>("reduce_mean_177_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_118_cast = sub(x = reshape_236_cast, y = reduce_mean_177_cast)[name = tensor<string, []>("sub_118_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_59_cast = square(x = sub_118_cast)[name = tensor<string, []>("square_59_cast")];
            tensor<int32, [3]> reduce_mean_179_axes_0 = const()[name = tensor<string, []>("reduce_mean_179_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_179_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_179_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_179_cast = reduce_mean(axes = reduce_mean_179_axes_0, keep_dims = reduce_mean_179_keep_dims_0, x = square_59_cast)[name = tensor<string, []>("reduce_mean_179_cast")];
            tensor<fp16, []> add_118_y_0_to_fp16 = const()[name = tensor<string, []>("add_118_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_118_cast = add(x = reduce_mean_179_cast, y = add_118_y_0_to_fp16)[name = tensor<string, []>("add_118_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_59_cast = sqrt(x = add_118_cast)[name = tensor<string, []>("sqrt_59_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_59_cast = real_div(x = sub_118_cast, y = sqrt_59_cast)[name = tensor<string, []>("real_div_59_cast")];
            tensor<int32, [4]> reshape_237_shape_0 = const()[name = tensor<string, []>("reshape_237_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_237_cast = reshape(shape = reshape_237_shape_0, x = real_div_59_cast)[name = tensor<string, []>("reshape_237_cast")];
            tensor<fp16, [320]> add_119_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_119_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647907712)))];
            tensor<fp16, [320]> add_119_beta_0_to_fp16 = const()[name = tensor<string, []>("add_119_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647908416)))];
            tensor<fp16, []> add_119_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_119_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_119_cast = batch_norm(beta = add_119_beta_0_to_fp16, epsilon = add_119_epsilon_0_to_fp16, gamma = add_119_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_237_cast)[name = tensor<string, []>("add_119_cast")];
            tensor<int32, [2]> var_4539 = const()[name = tensor<string, []>("op_4539"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4541 = const()[name = tensor<string, []>("op_4541"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_325_pad_type_0 = const()[name = tensor<string, []>("hidden_states_325_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_325_pad_0 = const()[name = tensor<string, []>("hidden_states_325_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647909120))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647985984))), name = tensor<string, []>("up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_2_proj_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647986176)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_325_cast = conv(bias = up_blocks_3_attentions_2_proj_in_bias_to_fp16, dilations = var_4541, groups = var_3945, pad = hidden_states_325_pad_0, pad_type = hidden_states_325_pad_type_0, strides = var_4539, weight = up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized, x = add_119_cast)[name = tensor<string, []>("hidden_states_325_cast")];
            tensor<int32, [4]> var_4546 = const()[name = tensor<string, []>("op_4546"), val = tensor<int32, [4]>([2, 320, 1, 4096])];
            tensor<fp16, [2, 320, 1, 4096]> inputs_91_cast = reshape(shape = var_4546, x = hidden_states_325_cast)[name = tensor<string, []>("inputs_91_cast")];
            tensor<int32, [1]> var_4556 = const()[name = tensor<string, []>("op_4556"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_91_cast = reduce_mean(axes = var_4556, keep_dims = var_3940, x = inputs_91_cast)[name = tensor<string, []>("channels_mean_91_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_91_cast = sub(x = inputs_91_cast, y = channels_mean_91_cast)[name = tensor<string, []>("zero_mean_91_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4560 = const()[name = tensor<string, []>("op_4560"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4561_cast = reduce_mean(axes = var_4560, keep_dims = var_3940, x = zero_mean_sq_91_cast)[name = tensor<string, []>("op_4561_cast")];
            tensor<fp16, []> var_4562_to_fp16 = const()[name = tensor<string, []>("op_4562_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4563_cast = add(x = var_4561_cast, y = var_4562_to_fp16)[name = tensor<string, []>("op_4563_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, 4096]> denom_91_cast = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_4563_cast)[name = tensor<string, []>("denom_91_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_91_cast = mul(x = zero_mean_91_cast, y = denom_91_cast)[name = tensor<string, []>("out_91_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4567_to_fp16 = const()[name = tensor<string, []>("op_4567_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647986880)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4568_cast = add(x = out_91_cast, y = var_4567_to_fp16)[name = tensor<string, []>("op_4568_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4570_to_fp16 = const()[name = tensor<string, []>("op_4570_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647987584)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_327_cast = mul(x = var_4568_cast, y = var_4570_to_fp16)[name = tensor<string, []>("hidden_states_327_cast")];
            tensor<int32, [2]> var_4577 = const()[name = tensor<string, []>("op_4577"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4579 = const()[name = tensor<string, []>("op_4579"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(647988288))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648065152))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_61_cast = conv(dilations = var_4579, groups = var_3945, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_4577, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor<string, []>("q_61_cast")];
            tensor<int32, [2]> var_4583 = const()[name = tensor<string, []>("op_4583"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4585 = const()[name = tensor<string, []>("op_4585"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648065344))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648142208))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> k_61_cast = conv(dilations = var_4585, groups = var_3945, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_4583, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor<string, []>("k_61_cast")];
            tensor<int32, [2]> var_4589 = const()[name = tensor<string, []>("op_4589"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4591 = const()[name = tensor<string, []>("op_4591"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648142400))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648219264))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> v_61_cast = conv(dilations = var_4591, groups = var_3945, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_4589, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor<string, []>("v_61_cast")];
            tensor<int32, [4]> var_4595 = const()[name = tensor<string, []>("op_4595"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4596_cast = reshape(shape = var_4595, x = q_61_cast)[name = tensor<string, []>("op_4596_cast")];
            tensor<int32, [4]> var_4597 = const()[name = tensor<string, []>("op_4597"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4598_cast = reshape(shape = var_4597, x = k_61_cast)[name = tensor<string, []>("op_4598_cast")];
            tensor<int32, [4]> var_4599 = const()[name = tensor<string, []>("op_4599"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4600_cast = reshape(shape = var_4599, x = v_61_cast)[name = tensor<string, []>("op_4600_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, 5, 4096, 4096]> attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_4596_cast, y = var_4598_cast)[name = tensor<string, []>("attn_weights_121_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> attn_weights_123_cast = mul(x = attn_weights_121_cast, y = var_3936_to_fp16)[name = tensor<string, []>("attn_weights_123_cast")];
            tensor<fp16, [2, 5, 4096, 4096]> var_4604_cast = softmax(axis = var_3929, x = attn_weights_123_cast)[name = tensor<string, []>("op_4604_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, 5, 64, 4096]> attn_61_cast = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_4600_cast, y = var_4604_cast)[name = tensor<string, []>("attn_61_cast")];
            tensor<int32, [4]> var_4608 = const()[name = tensor<string, []>("op_4608"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_517_cast = reshape(shape = var_4608, x = attn_61_cast)[name = tensor<string, []>("input_517_cast")];
            tensor<int32, [2]> var_4613 = const()[name = tensor<string, []>("op_4613"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4615 = const()[name = tensor<string, []>("op_4615"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4617_pad_type_0 = const()[name = tensor<string, []>("op_4617_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4617_pad_0 = const()[name = tensor<string, []>("op_4617_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648219456))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648296320))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648296512)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4617_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4615, groups = var_3945, pad = var_4617_pad_0, pad_type = var_4617_pad_type_0, strides = var_4613, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_517_cast)[name = tensor<string, []>("op_4617_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_93_cast = add(x = var_4617_cast, y = inputs_91_cast)[name = tensor<string, []>("inputs_93_cast")];
            tensor<int32, [1]> var_4621 = const()[name = tensor<string, []>("op_4621"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_93_cast = reduce_mean(axes = var_4621, keep_dims = var_3940, x = inputs_93_cast)[name = tensor<string, []>("channels_mean_93_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_93_cast = sub(x = inputs_93_cast, y = channels_mean_93_cast)[name = tensor<string, []>("zero_mean_93_cast")];
            tensor<fp16, [2, 320, 1, 4096]> 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_4625 = const()[name = tensor<string, []>("op_4625"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4626_cast = reduce_mean(axes = var_4625, keep_dims = var_3940, x = zero_mean_sq_93_cast)[name = tensor<string, []>("op_4626_cast")];
            tensor<fp16, []> var_4627_to_fp16 = const()[name = tensor<string, []>("op_4627_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4628_cast = add(x = var_4626_cast, y = var_4627_to_fp16)[name = tensor<string, []>("op_4628_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, 4096]> denom_93_cast = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_4628_cast)[name = tensor<string, []>("denom_93_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_93_cast = mul(x = zero_mean_93_cast, y = denom_93_cast)[name = tensor<string, []>("out_93_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4632_to_fp16 = const()[name = tensor<string, []>("op_4632_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648297216)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4633_cast = add(x = out_93_cast, y = var_4632_to_fp16)[name = tensor<string, []>("op_4633_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4635_to_fp16 = const()[name = tensor<string, []>("op_4635_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648297920)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_329_cast = mul(x = var_4633_cast, y = var_4635_to_fp16)[name = tensor<string, []>("hidden_states_329_cast")];
            tensor<int32, [2]> var_4642 = const()[name = tensor<string, []>("op_4642"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4644 = const()[name = tensor<string, []>("op_4644"), 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, [320, 320, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648298624))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648375488))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [2, 320, 1, 4096]> q_cast = conv(dilations = var_4644, groups = var_3945, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_4642, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_329_cast)[name = tensor<string, []>("q_cast")];
            tensor<int32, [2]> var_4648 = const()[name = tensor<string, []>("op_4648"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4650 = const()[name = tensor<string, []>("op_4650"), 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, [320, 1024, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648375680))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648621504))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> k_cast = conv(dilations = var_4650, groups = var_3945, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_4648, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("k_cast")];
            tensor<int32, [2]> var_4654 = const()[name = tensor<string, []>("op_4654"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4656 = const()[name = tensor<string, []>("op_4656"), 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, [320, 1024, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [245760]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648621696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648867520))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1024, 1, 1])];
            tensor<fp16, [2, 320, 1, 77]> v_cast = conv(dilations = var_4656, groups = var_3945, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_4654, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor<string, []>("v_cast")];
            tensor<int32, [4]> var_4660 = const()[name = tensor<string, []>("op_4660"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_4661_cast = reshape(shape = var_4660, x = q_cast)[name = tensor<string, []>("op_4661_cast")];
            tensor<int32, [4]> var_4662 = const()[name = tensor<string, []>("op_4662"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_4663_cast = reshape(shape = var_4662, x = k_cast)[name = tensor<string, []>("op_4663_cast")];
            tensor<int32, [4]> var_4664 = const()[name = tensor<string, []>("op_4664"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_4665_cast = reshape(shape = var_4664, x = v_cast)[name = tensor<string, []>("op_4665_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, 5, 4096, 77]> attn_weights_125_cast = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_4661_cast, y = var_4663_cast)[name = tensor<string, []>("attn_weights_125_cast")];
            tensor<fp16, [2, 5, 4096, 77]> attn_weights_cast = mul(x = attn_weights_125_cast, y = var_3936_to_fp16)[name = tensor<string, []>("attn_weights_cast")];
            tensor<fp16, [2, 5, 4096, 77]> var_4669_cast = softmax(axis = var_3929, x = attn_weights_cast)[name = tensor<string, []>("op_4669_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, 5, 64, 4096]> attn_cast = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_4665_cast, y = var_4669_cast)[name = tensor<string, []>("attn_cast")];
            tensor<int32, [4]> var_4673 = const()[name = tensor<string, []>("op_4673"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_519_cast = reshape(shape = var_4673, x = attn_cast)[name = tensor<string, []>("input_519_cast")];
            tensor<int32, [2]> var_4678 = const()[name = tensor<string, []>("op_4678"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4680 = const()[name = tensor<string, []>("op_4680"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4682_pad_type_0 = const()[name = tensor<string, []>("op_4682_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4682_pad_0 = const()[name = tensor<string, []>("op_4682_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648867712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648944576))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648944768)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4682_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4680, groups = var_3945, pad = var_4682_pad_0, pad_type = var_4682_pad_type_0, strides = var_4678, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_519_cast)[name = tensor<string, []>("op_4682_cast")];
            tensor<fp16, [2, 320, 1, 4096]> inputs_cast = add(x = var_4682_cast, y = inputs_93_cast)[name = tensor<string, []>("inputs_cast")];
            tensor<int32, [1]> var_4686 = const()[name = tensor<string, []>("op_4686"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_cast = reduce_mean(axes = var_4686, keep_dims = var_3940, x = inputs_cast)[name = tensor<string, []>("channels_mean_cast")];
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_cast = sub(x = inputs_cast, y = channels_mean_cast)[name = tensor<string, []>("zero_mean_cast")];
            tensor<fp16, [2, 320, 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_4690 = const()[name = tensor<string, []>("op_4690"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_4691_cast = reduce_mean(axes = var_4690, keep_dims = var_3940, x = zero_mean_sq_cast)[name = tensor<string, []>("op_4691_cast")];
            tensor<fp16, []> var_4692_to_fp16 = const()[name = tensor<string, []>("op_4692_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 4096]> var_4693_cast = add(x = var_4691_cast, y = var_4692_to_fp16)[name = tensor<string, []>("op_4693_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_4693_cast)[name = tensor<string, []>("denom_cast")];
            tensor<fp16, [2, 320, 1, 4096]> out_cast = mul(x = zero_mean_cast, y = denom_cast)[name = tensor<string, []>("out_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4697_to_fp16 = const()[name = tensor<string, []>("op_4697_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648945472)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4698_cast = add(x = out_cast, y = var_4697_to_fp16)[name = tensor<string, []>("op_4698_cast")];
            tensor<fp16, [1, 320, 1, 1]> var_4700_to_fp16 = const()[name = tensor<string, []>("op_4700_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(648946176)))];
            tensor<fp16, [2, 320, 1, 4096]> input_521_cast = mul(x = var_4698_cast, y = var_4700_to_fp16)[name = tensor<string, []>("input_521_cast")];
            tensor<int32, [2]> var_4708 = const()[name = tensor<string, []>("op_4708"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4710 = const()[name = tensor<string, []>("op_4710"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4712_pad_type_0 = const()[name = tensor<string, []>("op_4712_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4712_pad_0 = const()[name = tensor<string, []>("op_4712_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [2560, 320, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_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, []>(648946880))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649561344))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([2560, 320, 1, 1])];
            tensor<fp16, [2560]> up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1920]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649561536))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649563520))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([2560])];
            tensor<fp16, [2, 2560, 1, 4096]> var_4712_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4710, groups = var_3945, pad = var_4712_pad_0, pad_type = var_4712_pad_type_0, strides = var_4708, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_521_cast)[name = tensor<string, []>("op_4712_cast")];
            tensor<int32, [2]> var_4713_split_sizes_0 = const()[name = tensor<string, []>("op_4713_split_sizes_0"), val = tensor<int32, [2]>([1280, 1280])];
            tensor<int32, []> var_4713_axis_0 = const()[name = tensor<string, []>("op_4713_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 1280, 1, 4096]> var_4713_cast_0, tensor<fp16, [2, 1280, 1, 4096]> var_4713_cast_1 = split(axis = var_4713_axis_0, split_sizes = var_4713_split_sizes_0, x = var_4712_cast)[name = tensor<string, []>("op_4713_cast")];
            tensor<string, []> var_4715_mode_0 = const()[name = tensor<string, []>("op_4715_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 4096]> var_4715_cast = gelu(mode = var_4715_mode_0, x = var_4713_cast_1)[name = tensor<string, []>("op_4715_cast")];
            tensor<fp16, [2, 1280, 1, 4096]> input_523_cast = mul(x = var_4713_cast_0, y = var_4715_cast)[name = tensor<string, []>("input_523_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, [320, 1280, 1, 1]> up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [307200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649563712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649870976))), name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 1280, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649871168)))];
            tensor<fp16, [2, 320, 1, 4096]> var_4723_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4721, groups = var_3945, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_523_cast)[name = tensor<string, []>("op_4723_cast")];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_333_cast = add(x = var_4723_cast, y = inputs_cast)[name = tensor<string, []>("hidden_states_333_cast")];
            tensor<int32, [4]> var_4725 = const()[name = tensor<string, []>("op_4725"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> input_525_cast = reshape(shape = var_4725, x = hidden_states_333_cast)[name = tensor<string, []>("input_525_cast")];
            tensor<int32, [2]> var_4729 = const()[name = tensor<string, []>("op_4729"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4731 = const()[name = tensor<string, []>("op_4731"), 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]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [76800]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649871872))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649948736))), name = tensor<string, []>("up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([320, 320, 1, 1])];
            tensor<fp16, [320]> up_blocks_3_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_3_attentions_2_proj_out_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649948928)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_cast = conv(bias = up_blocks_3_attentions_2_proj_out_bias_to_fp16, dilations = var_4731, groups = var_3945, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_4729, weight = up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized, x = input_525_cast)[name = tensor<string, []>("hidden_states_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_527_cast = add(x = hidden_states_cast, y = hidden_states_323_cast)[name = tensor<string, []>("input_527_cast")];
            tensor<int32, [5]> reshape_240_shape_0 = const()[name = tensor<string, []>("reshape_240_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_240_cast = reshape(shape = reshape_240_shape_0, x = input_527_cast)[name = tensor<string, []>("reshape_240_cast")];
            tensor<int32, [3]> reduce_mean_180_axes_0 = const()[name = tensor<string, []>("reduce_mean_180_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_180_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_180_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_180_cast = reduce_mean(axes = reduce_mean_180_axes_0, keep_dims = reduce_mean_180_keep_dims_0, x = reshape_240_cast)[name = tensor<string, []>("reduce_mean_180_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> sub_120_cast = sub(x = reshape_240_cast, y = reduce_mean_180_cast)[name = tensor<string, []>("sub_120_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> square_60_cast = square(x = sub_120_cast)[name = tensor<string, []>("square_60_cast")];
            tensor<int32, [3]> reduce_mean_182_axes_0 = const()[name = tensor<string, []>("reduce_mean_182_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_182_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_182_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_182_cast = reduce_mean(axes = reduce_mean_182_axes_0, keep_dims = reduce_mean_182_keep_dims_0, x = square_60_cast)[name = tensor<string, []>("reduce_mean_182_cast")];
            tensor<fp16, []> add_120_y_0_to_fp16 = const()[name = tensor<string, []>("add_120_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_120_cast = add(x = reduce_mean_182_cast, y = add_120_y_0_to_fp16)[name = tensor<string, []>("add_120_cast")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_60_cast = sqrt(x = add_120_cast)[name = tensor<string, []>("sqrt_60_cast")];
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_60_cast = real_div(x = sub_120_cast, y = sqrt_60_cast)[name = tensor<string, []>("real_div_60_cast")];
            tensor<int32, [4]> reshape_241_shape_0 = const()[name = tensor<string, []>("reshape_241_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_241_cast = reshape(shape = reshape_241_shape_0, x = real_div_60_cast)[name = tensor<string, []>("reshape_241_cast")];
            tensor<fp16, [320]> add_121_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_121_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649949632)))];
            tensor<fp16, [320]> add_121_beta_0_to_fp16 = const()[name = tensor<string, []>("add_121_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649950336)))];
            tensor<fp16, []> add_121_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_121_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 64, 64]> add_121_cast = batch_norm(beta = add_121_beta_0_to_fp16, epsilon = add_121_epsilon_0_to_fp16, gamma = add_121_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_241_cast)[name = tensor<string, []>("add_121_cast")];
            tensor<fp16, [2, 320, 64, 64]> input_cast = silu(x = add_121_cast)[name = tensor<string, []>("input_cast")];
            tensor<int32, []> var_4745 = const()[name = tensor<string, []>("op_4745"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_4748 = const()[name = tensor<string, []>("op_4748"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_4750 = const()[name = tensor<string, []>("op_4750"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_4752_pad_type_0 = const()[name = tensor<string, []>("op_4752_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_4752_pad_0 = const()[name = tensor<string, []>("op_4752_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [4, 320, 3, 3]> conv_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [8640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649951040))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649959744))), name = tensor<string, []>("conv_out_weight_to_fp16_palettized"), shape = tensor<uint32, [4]>([4, 320, 3, 3])];
            tensor<fp16, [4]> conv_out_bias_to_fp16 = const()[name = tensor<string, []>("conv_out_bias_to_fp16"), val = tensor<fp16, [4]>([-0x1.39p-9, 0x1.9f8p-9, 0x1.c64p-12, 0x1.d6cp-10])];
            tensor<fp16, [2, 4, 64, 64]> var_4752_cast = conv(bias = conv_out_bias_to_fp16, dilations = var_4750, groups = var_4745, pad = var_4752_pad_0, pad_type = var_4752_pad_type_0, strides = var_4748, weight = conv_out_weight_to_fp16_palettized, x = input_cast)[name = tensor<string, []>("op_4752_cast")];
            tensor<string, []> var_4752_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_4752_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [2, 4, 64, 64]> noise_pred = cast(dtype = var_4752_cast_to_fp32_dtype_0, x = var_4752_cast)[name = tensor<string, []>("cast_0")];
        } -> (noise_pred);
}