diff --git "a/dreamshaper7_lcm/Unet.mlmodelc/model.mil" "b/dreamshaper7_lcm/Unet.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/dreamshaper7_lcm/Unet.mlmodelc/model.mil" @@ -0,0 +1,9220 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}})] +{ + func main(tensor encoder_hidden_states, tensor sample, tensor timestep) { + tensor var_25 = const()[name = tensor("op_25"), val = tensor(-1)]; + tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; + tensor var_42_cast = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor("op_42_cast")]; + tensor var_44_to_fp16 = const()[name = tensor("op_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast = mul(x = var_42_cast, y = var_44_to_fp16)[name = tensor("emb_3_cast")]; + tensor var_49_cast = sin(x = emb_3_cast)[name = tensor("op_49_cast")]; + tensor var_50_cast = cos(x = emb_3_cast)[name = tensor("op_50_cast")]; + tensor emb_interleave_0 = const()[name = tensor("emb_interleave_0"), val = tensor(false)]; + tensor emb_cast = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast, var_50_cast))[name = tensor("emb_cast")]; + tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 160])]; + tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([2, 320])]; + tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, true])]; + tensor 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("op_54_cast")]; + tensor var_56_begin_0 = const()[name = tensor("op_56_begin_0"), val = tensor([0, 0])]; + tensor var_56_end_0 = const()[name = tensor("op_56_end_0"), val = tensor([2, 160])]; + tensor var_56_end_mask_0 = const()[name = tensor("op_56_end_mask_0"), val = tensor([true, false])]; + tensor 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("op_56_cast")]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast, var_56_cast))[name = tensor("sample_cast")]; + tensor var_59 = const()[name = tensor("op_59"), val = tensor(1)]; + tensor var_66_axes_0 = const()[name = tensor("op_66_axes_0"), val = tensor([-1])]; + tensor var_66_cast = expand_dims(axes = var_66_axes_0, x = sample_cast)[name = tensor("op_66_cast")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast = expand_dims(axes = input_1_axes_0, x = var_66_cast)[name = tensor("input_1_cast")]; + tensor var_70 = const()[name = tensor("op_70"), val = tensor([1, 1])]; + tensor var_72 = const()[name = tensor("op_72"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307712))), name = tensor("time_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor([1280, 320, 1, 1])]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307904)))]; + tensor 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("input_3_cast")]; + tensor input_5_cast = silu(x = input_3_cast)[name = tensor("input_5_cast")]; + tensor var_78 = const()[name = tensor("op_78"), val = tensor([1, 1])]; + tensor var_80 = const()[name = tensor("op_80"), val = tensor([1, 1])]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539392))), name = tensor("time_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539584)))]; + tensor 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("input_13_cast")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(1)]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([1, 1])]; + tensor var_91 = const()[name = tensor("op_91"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1542208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550912))), name = tensor("conv_in_weight_to_fp16_palettized"), shape = tensor([320, 4, 3, 3])]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551104)))]; + tensor 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("input_7_cast")]; + tensor var_110 = const()[name = tensor("op_110"), val = tensor(true)]; + tensor var_115 = const()[name = tensor("op_115"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_7_cast)[name = tensor("reshape_0_cast")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_2_cast")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551808)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1552512)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1553216)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1553920)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_1_cast")]; + tensor input_11_cast = silu(x = add_1_cast)[name = tensor("input_11_cast")]; + tensor var_137 = const()[name = tensor("op_137"), val = tensor([1, 1])]; + tensor var_139 = const()[name = tensor("op_139"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1554624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2245888))), name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2246080)))]; + tensor hidden_states_1_cast = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_139, groups = var_115, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_137, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_11_cast)[name = tensor("hidden_states_1_cast")]; + tensor input_15_cast = silu(x = input_13_cast)[name = tensor("input_15_cast")]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 1])]; + tensor var_147 = const()[name = tensor("op_147"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2246784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554048))), name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554240)))]; + tensor temb_1_cast = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_147, groups = var_115, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_145, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_1_cast")]; + tensor input_17_cast = add(x = hidden_states_1_cast, y = temb_1_cast)[name = tensor("input_17_cast")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_17_cast)[name = tensor("reshape_4_cast")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_5_cast")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554944)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2555648)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_3_cast")]; + tensor input_21_cast = silu(x = add_3_cast)[name = tensor("input_21_cast")]; + tensor var_157 = const()[name = tensor("op_157"), val = tensor([1, 1])]; + tensor var_159 = const()[name = tensor("op_159"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2556352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3247616))), name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3247808)))]; + tensor hidden_states_3_cast = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_159, groups = var_115, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_157, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_21_cast)[name = tensor("hidden_states_3_cast")]; + tensor hidden_states_5_cast = add(x = input_7_cast, y = hidden_states_3_cast)[name = tensor("hidden_states_5_cast")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = hidden_states_5_cast)[name = tensor("reshape_8_cast")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_8_cast")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3248512)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3249216)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_5_cast")]; + tensor var_179 = const()[name = tensor("op_179"), val = tensor([1, 1])]; + tensor var_181 = const()[name = tensor("op_181"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3249920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326784))), name = tensor("down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326976)))]; + tensor hidden_states_7_cast = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_181, groups = var_115, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_179, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized, x = add_5_cast)[name = tensor("hidden_states_7_cast")]; + tensor var_186 = const()[name = tensor("op_186"), val = tensor([2, 320, 1, 9216])]; + tensor inputs_1_cast = reshape(shape = var_186, x = hidden_states_7_cast)[name = tensor("inputs_1_cast")]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor([1])]; + tensor channels_mean_1_cast = reduce_mean(axes = var_196, keep_dims = var_110, x = inputs_1_cast)[name = tensor("channels_mean_1_cast")]; + tensor zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast)[name = tensor("zero_mean_1_cast")]; + tensor zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast)[name = tensor("zero_mean_sq_1_cast")]; + tensor var_200 = const()[name = tensor("op_200"), val = tensor([1])]; + tensor var_201_cast = reduce_mean(axes = var_200, keep_dims = var_110, x = zero_mean_sq_1_cast)[name = tensor("op_201_cast")]; + tensor var_202_to_fp16 = const()[name = tensor("op_202_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_203_cast = add(x = var_201_cast, y = var_202_to_fp16)[name = tensor("op_203_cast")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_203_cast)[name = tensor("denom_1_cast")]; + tensor out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast)[name = tensor("out_1_cast")]; + tensor var_207_to_fp16 = const()[name = tensor("op_207_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3327680)))]; + tensor var_208_cast = add(x = out_1_cast, y = var_207_to_fp16)[name = tensor("op_208_cast")]; + tensor var_210_to_fp16 = const()[name = tensor("op_210_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3328384)))]; + tensor hidden_states_9_cast = mul(x = var_208_cast, y = var_210_to_fp16)[name = tensor("hidden_states_9_cast")]; + tensor var_217 = const()[name = tensor("op_217"), val = tensor([1, 1])]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3329088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3405952))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_1_cast = conv(dilations = var_219, groups = var_115, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_217, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor("q_1_cast")]; + tensor var_223 = const()[name = tensor("op_223"), val = tensor([1, 1])]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3406144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3483008))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_1_cast = conv(dilations = var_225, groups = var_115, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_223, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor("k_1_cast")]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3483200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3560064))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_1_cast = conv(dilations = var_231, groups = var_115, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_229, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor("v_1_cast")]; + tensor var_235_begin_0 = const()[name = tensor("op_235_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_235_end_0 = const()[name = tensor("op_235_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_235_end_mask_0 = const()[name = tensor("op_235_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_235_cast = slice_by_index(begin = var_235_begin_0, end = var_235_end_0, end_mask = var_235_end_mask_0, x = q_1_cast)[name = tensor("op_235_cast")]; + tensor var_239_begin_0 = const()[name = tensor("op_239_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_239_end_0 = const()[name = tensor("op_239_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_239_end_mask_0 = const()[name = tensor("op_239_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_239_cast = slice_by_index(begin = var_239_begin_0, end = var_239_end_0, end_mask = var_239_end_mask_0, x = q_1_cast)[name = tensor("op_239_cast")]; + tensor var_243_begin_0 = const()[name = tensor("op_243_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_243_end_0 = const()[name = tensor("op_243_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_243_end_mask_0 = const()[name = tensor("op_243_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_243_cast = slice_by_index(begin = var_243_begin_0, end = var_243_end_0, end_mask = var_243_end_mask_0, x = q_1_cast)[name = tensor("op_243_cast")]; + tensor var_247_begin_0 = const()[name = tensor("op_247_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_247_end_0 = const()[name = tensor("op_247_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_247_end_mask_0 = const()[name = tensor("op_247_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_247_cast = slice_by_index(begin = var_247_begin_0, end = var_247_end_0, end_mask = var_247_end_mask_0, x = q_1_cast)[name = tensor("op_247_cast")]; + tensor var_251_begin_0 = const()[name = tensor("op_251_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_251_end_0 = const()[name = tensor("op_251_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_251_end_mask_0 = const()[name = tensor("op_251_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_251_cast = slice_by_index(begin = var_251_begin_0, end = var_251_end_0, end_mask = var_251_end_mask_0, x = q_1_cast)[name = tensor("op_251_cast")]; + tensor var_255_begin_0 = const()[name = tensor("op_255_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_255_end_0 = const()[name = tensor("op_255_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_255_end_mask_0 = const()[name = tensor("op_255_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_255_cast = slice_by_index(begin = var_255_begin_0, end = var_255_end_0, end_mask = var_255_end_mask_0, x = q_1_cast)[name = tensor("op_255_cast")]; + tensor var_259_begin_0 = const()[name = tensor("op_259_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_259_end_0 = const()[name = tensor("op_259_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_259_end_mask_0 = const()[name = tensor("op_259_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_259_cast = slice_by_index(begin = var_259_begin_0, end = var_259_end_0, end_mask = var_259_end_mask_0, x = q_1_cast)[name = tensor("op_259_cast")]; + tensor var_263_begin_0 = const()[name = tensor("op_263_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_263_end_0 = const()[name = tensor("op_263_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_263_end_mask_0 = const()[name = tensor("op_263_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_263_cast = slice_by_index(begin = var_263_begin_0, end = var_263_end_0, end_mask = var_263_end_mask_0, x = q_1_cast)[name = tensor("op_263_cast")]; + tensor k_3_perm_0 = const()[name = tensor("k_3_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_270_begin_0 = const()[name = tensor("op_270_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_270_end_0 = const()[name = tensor("op_270_end_0"), val = tensor([2, 9216, 1, 40])]; + tensor var_270_end_mask_0 = const()[name = tensor("op_270_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_31 = transpose(perm = k_3_perm_0, x = k_1_cast)[name = tensor("transpose_31")]; + tensor var_270_cast = slice_by_index(begin = var_270_begin_0, end = var_270_end_0, end_mask = var_270_end_mask_0, x = transpose_31)[name = tensor("op_270_cast")]; + tensor var_274_begin_0 = const()[name = tensor("op_274_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_274_end_0 = const()[name = tensor("op_274_end_0"), val = tensor([2, 9216, 1, 80])]; + tensor var_274_end_mask_0 = const()[name = tensor("op_274_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_274_cast = slice_by_index(begin = var_274_begin_0, end = var_274_end_0, end_mask = var_274_end_mask_0, x = transpose_31)[name = tensor("op_274_cast")]; + tensor var_278_begin_0 = const()[name = tensor("op_278_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_278_end_0 = const()[name = tensor("op_278_end_0"), val = tensor([2, 9216, 1, 120])]; + tensor var_278_end_mask_0 = const()[name = tensor("op_278_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_278_cast = slice_by_index(begin = var_278_begin_0, end = var_278_end_0, end_mask = var_278_end_mask_0, x = transpose_31)[name = tensor("op_278_cast")]; + tensor var_282_begin_0 = const()[name = tensor("op_282_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_282_end_0 = const()[name = tensor("op_282_end_0"), val = tensor([2, 9216, 1, 160])]; + tensor var_282_end_mask_0 = const()[name = tensor("op_282_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_282_cast = slice_by_index(begin = var_282_begin_0, end = var_282_end_0, end_mask = var_282_end_mask_0, x = transpose_31)[name = tensor("op_282_cast")]; + tensor var_286_begin_0 = const()[name = tensor("op_286_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_286_end_0 = const()[name = tensor("op_286_end_0"), val = tensor([2, 9216, 1, 200])]; + tensor var_286_end_mask_0 = const()[name = tensor("op_286_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_286_cast = slice_by_index(begin = var_286_begin_0, end = var_286_end_0, end_mask = var_286_end_mask_0, x = transpose_31)[name = tensor("op_286_cast")]; + tensor var_290_begin_0 = const()[name = tensor("op_290_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_290_end_0 = const()[name = tensor("op_290_end_0"), val = tensor([2, 9216, 1, 240])]; + tensor var_290_end_mask_0 = const()[name = tensor("op_290_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_290_cast = slice_by_index(begin = var_290_begin_0, end = var_290_end_0, end_mask = var_290_end_mask_0, x = transpose_31)[name = tensor("op_290_cast")]; + tensor var_294_begin_0 = const()[name = tensor("op_294_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_294_end_0 = const()[name = tensor("op_294_end_0"), val = tensor([2, 9216, 1, 280])]; + tensor var_294_end_mask_0 = const()[name = tensor("op_294_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_294_cast = slice_by_index(begin = var_294_begin_0, end = var_294_end_0, end_mask = var_294_end_mask_0, x = transpose_31)[name = tensor("op_294_cast")]; + tensor var_298_begin_0 = const()[name = tensor("op_298_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_298_end_0 = const()[name = tensor("op_298_end_0"), val = tensor([2, 9216, 1, 320])]; + tensor var_298_end_mask_0 = const()[name = tensor("op_298_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_298_cast = slice_by_index(begin = var_298_begin_0, end = var_298_end_0, end_mask = var_298_end_mask_0, x = transpose_31)[name = tensor("op_298_cast")]; + tensor var_300_begin_0 = const()[name = tensor("op_300_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_300_end_0 = const()[name = tensor("op_300_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_300_end_mask_0 = const()[name = tensor("op_300_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_300_cast = slice_by_index(begin = var_300_begin_0, end = var_300_end_0, end_mask = var_300_end_mask_0, x = v_1_cast)[name = tensor("op_300_cast")]; + tensor var_304_begin_0 = const()[name = tensor("op_304_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_304_end_0 = const()[name = tensor("op_304_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_304_end_mask_0 = const()[name = tensor("op_304_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_304_cast = slice_by_index(begin = var_304_begin_0, end = var_304_end_0, end_mask = var_304_end_mask_0, x = v_1_cast)[name = tensor("op_304_cast")]; + tensor var_308_begin_0 = const()[name = tensor("op_308_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_308_end_0 = const()[name = tensor("op_308_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_308_end_mask_0 = const()[name = tensor("op_308_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_308_cast = slice_by_index(begin = var_308_begin_0, end = var_308_end_0, end_mask = var_308_end_mask_0, x = v_1_cast)[name = tensor("op_308_cast")]; + tensor var_312_begin_0 = const()[name = tensor("op_312_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_312_end_0 = const()[name = tensor("op_312_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_312_end_mask_0 = const()[name = tensor("op_312_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_312_cast = slice_by_index(begin = var_312_begin_0, end = var_312_end_0, end_mask = var_312_end_mask_0, x = v_1_cast)[name = tensor("op_312_cast")]; + tensor var_316_begin_0 = const()[name = tensor("op_316_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_316_end_0 = const()[name = tensor("op_316_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_316_end_mask_0 = const()[name = tensor("op_316_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_316_cast = slice_by_index(begin = var_316_begin_0, end = var_316_end_0, end_mask = var_316_end_mask_0, x = v_1_cast)[name = tensor("op_316_cast")]; + tensor var_320_begin_0 = const()[name = tensor("op_320_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_320_end_0 = const()[name = tensor("op_320_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_320_end_mask_0 = const()[name = tensor("op_320_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_320_cast = slice_by_index(begin = var_320_begin_0, end = var_320_end_0, end_mask = var_320_end_mask_0, x = v_1_cast)[name = tensor("op_320_cast")]; + tensor var_324_begin_0 = const()[name = tensor("op_324_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_324_end_0 = const()[name = tensor("op_324_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_324_end_mask_0 = const()[name = tensor("op_324_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_324_cast = slice_by_index(begin = var_324_begin_0, end = var_324_end_0, end_mask = var_324_end_mask_0, x = v_1_cast)[name = tensor("op_324_cast")]; + tensor var_328_begin_0 = const()[name = tensor("op_328_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_328_end_0 = const()[name = tensor("op_328_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_328_end_mask_0 = const()[name = tensor("op_328_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_328_cast = slice_by_index(begin = var_328_begin_0, end = var_328_end_0, end_mask = var_328_end_mask_0, x = v_1_cast)[name = tensor("op_328_cast")]; + tensor var_332_equation_0 = const()[name = tensor("op_332_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_332_cast = einsum(equation = var_332_equation_0, values = (var_270_cast, var_235_cast))[name = tensor("op_332_cast")]; + tensor var_333_to_fp16 = const()[name = tensor("op_333_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_1_cast = mul(x = var_332_cast, y = var_333_to_fp16)[name = tensor("aw_1_cast")]; + tensor var_336_equation_0 = const()[name = tensor("op_336_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_336_cast = einsum(equation = var_336_equation_0, values = (var_274_cast, var_239_cast))[name = tensor("op_336_cast")]; + tensor var_337_to_fp16 = const()[name = tensor("op_337_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_3_cast = mul(x = var_336_cast, y = var_337_to_fp16)[name = tensor("aw_3_cast")]; + tensor var_340_equation_0 = const()[name = tensor("op_340_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_340_cast = einsum(equation = var_340_equation_0, values = (var_278_cast, var_243_cast))[name = tensor("op_340_cast")]; + tensor var_341_to_fp16 = const()[name = tensor("op_341_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_5_cast = mul(x = var_340_cast, y = var_341_to_fp16)[name = tensor("aw_5_cast")]; + tensor var_344_equation_0 = const()[name = tensor("op_344_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_344_cast = einsum(equation = var_344_equation_0, values = (var_282_cast, var_247_cast))[name = tensor("op_344_cast")]; + tensor var_345_to_fp16 = const()[name = tensor("op_345_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_7_cast = mul(x = var_344_cast, y = var_345_to_fp16)[name = tensor("aw_7_cast")]; + tensor var_348_equation_0 = const()[name = tensor("op_348_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_348_cast = einsum(equation = var_348_equation_0, values = (var_286_cast, var_251_cast))[name = tensor("op_348_cast")]; + tensor var_349_to_fp16 = const()[name = tensor("op_349_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_9_cast = mul(x = var_348_cast, y = var_349_to_fp16)[name = tensor("aw_9_cast")]; + tensor var_352_equation_0 = const()[name = tensor("op_352_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_352_cast = einsum(equation = var_352_equation_0, values = (var_290_cast, var_255_cast))[name = tensor("op_352_cast")]; + tensor var_353_to_fp16 = const()[name = tensor("op_353_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_11_cast = mul(x = var_352_cast, y = var_353_to_fp16)[name = tensor("aw_11_cast")]; + tensor var_356_equation_0 = const()[name = tensor("op_356_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_356_cast = einsum(equation = var_356_equation_0, values = (var_294_cast, var_259_cast))[name = tensor("op_356_cast")]; + tensor var_357_to_fp16 = const()[name = tensor("op_357_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_13_cast = mul(x = var_356_cast, y = var_357_to_fp16)[name = tensor("aw_13_cast")]; + tensor var_360_equation_0 = const()[name = tensor("op_360_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_360_cast = einsum(equation = var_360_equation_0, values = (var_298_cast, var_263_cast))[name = tensor("op_360_cast")]; + tensor var_361_to_fp16 = const()[name = tensor("op_361_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_15_cast = mul(x = var_360_cast, y = var_361_to_fp16)[name = tensor("aw_15_cast")]; + tensor var_363_cast = softmax(axis = var_115, x = aw_1_cast)[name = tensor("op_363_cast")]; + tensor var_364_cast = softmax(axis = var_115, x = aw_3_cast)[name = tensor("op_364_cast")]; + tensor var_365_cast = softmax(axis = var_115, x = aw_5_cast)[name = tensor("op_365_cast")]; + tensor var_366_cast = softmax(axis = var_115, x = aw_7_cast)[name = tensor("op_366_cast")]; + tensor var_367_cast = softmax(axis = var_115, x = aw_9_cast)[name = tensor("op_367_cast")]; + tensor var_368_cast = softmax(axis = var_115, x = aw_11_cast)[name = tensor("op_368_cast")]; + tensor var_369_cast = softmax(axis = var_115, x = aw_13_cast)[name = tensor("op_369_cast")]; + tensor var_370_cast = softmax(axis = var_115, x = aw_15_cast)[name = tensor("op_370_cast")]; + tensor var_372_equation_0 = const()[name = tensor("op_372_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_372_cast = einsum(equation = var_372_equation_0, values = (var_300_cast, var_363_cast))[name = tensor("op_372_cast")]; + tensor var_374_equation_0 = const()[name = tensor("op_374_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_374_cast = einsum(equation = var_374_equation_0, values = (var_304_cast, var_364_cast))[name = tensor("op_374_cast")]; + tensor var_376_equation_0 = const()[name = tensor("op_376_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_376_cast = einsum(equation = var_376_equation_0, values = (var_308_cast, var_365_cast))[name = tensor("op_376_cast")]; + tensor var_378_equation_0 = const()[name = tensor("op_378_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_378_cast = einsum(equation = var_378_equation_0, values = (var_312_cast, var_366_cast))[name = tensor("op_378_cast")]; + tensor var_380_equation_0 = const()[name = tensor("op_380_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_380_cast = einsum(equation = var_380_equation_0, values = (var_316_cast, var_367_cast))[name = tensor("op_380_cast")]; + tensor var_382_equation_0 = const()[name = tensor("op_382_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_382_cast = einsum(equation = var_382_equation_0, values = (var_320_cast, var_368_cast))[name = tensor("op_382_cast")]; + tensor var_384_equation_0 = const()[name = tensor("op_384_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_384_cast = einsum(equation = var_384_equation_0, values = (var_324_cast, var_369_cast))[name = tensor("op_384_cast")]; + tensor var_386_equation_0 = const()[name = tensor("op_386_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_386_cast = einsum(equation = var_386_equation_0, values = (var_328_cast, var_370_cast))[name = tensor("op_386_cast")]; + tensor input_25_interleave_0 = const()[name = tensor("input_25_interleave_0"), val = tensor(false)]; + tensor input_25_cast = concat(axis = var_115, interleave = input_25_interleave_0, values = (var_372_cast, var_374_cast, var_376_cast, var_378_cast, var_380_cast, var_382_cast, var_384_cast, var_386_cast))[name = tensor("input_25_cast")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; + tensor var_396_pad_type_0 = const()[name = tensor("op_396_pad_type_0"), val = tensor("custom")]; + tensor var_396_pad_0 = const()[name = tensor("op_396_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3560256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3637120))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3637312)))]; + tensor var_396_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_394, groups = var_115, pad = var_396_pad_0, pad_type = var_396_pad_type_0, strides = var_392, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_25_cast)[name = tensor("op_396_cast")]; + tensor inputs_3_cast = add(x = var_396_cast, y = inputs_1_cast)[name = tensor("inputs_3_cast")]; + tensor var_400 = const()[name = tensor("op_400"), val = tensor([1])]; + tensor channels_mean_3_cast = reduce_mean(axes = var_400, keep_dims = var_110, x = inputs_3_cast)[name = tensor("channels_mean_3_cast")]; + tensor zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast)[name = tensor("zero_mean_3_cast")]; + tensor zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast)[name = tensor("zero_mean_sq_3_cast")]; + tensor var_404 = const()[name = tensor("op_404"), val = tensor([1])]; + tensor var_405_cast = reduce_mean(axes = var_404, keep_dims = var_110, x = zero_mean_sq_3_cast)[name = tensor("op_405_cast")]; + tensor var_406_to_fp16 = const()[name = tensor("op_406_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_407_cast = add(x = var_405_cast, y = var_406_to_fp16)[name = tensor("op_407_cast")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_407_cast)[name = tensor("denom_3_cast")]; + tensor out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast)[name = tensor("out_3_cast")]; + tensor var_411_to_fp16 = const()[name = tensor("op_411_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3638016)))]; + tensor var_412_cast = add(x = out_3_cast, y = var_411_to_fp16)[name = tensor("op_412_cast")]; + tensor var_414_to_fp16 = const()[name = tensor("op_414_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3638720)))]; + tensor hidden_states_11_cast = mul(x = var_412_cast, y = var_414_to_fp16)[name = tensor("hidden_states_11_cast")]; + tensor var_421 = const()[name = tensor("op_421"), val = tensor([1, 1])]; + tensor var_423 = const()[name = tensor("op_423"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3639424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3716288))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_3_cast = conv(dilations = var_423, groups = var_115, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_421, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_11_cast)[name = tensor("q_3_cast")]; + tensor var_427 = const()[name = tensor("op_427"), val = tensor([1, 1])]; + tensor var_429 = const()[name = tensor("op_429"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3716480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3900864))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_5_cast = conv(dilations = var_429, groups = var_115, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_427, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_5_cast")]; + tensor var_433 = const()[name = tensor("op_433"), val = tensor([1, 1])]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 1])]; + tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3901056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085440))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_3_cast = conv(dilations = var_435, groups = var_115, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_433, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_3_cast")]; + tensor var_439_begin_0 = const()[name = tensor("op_439_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_439_end_0 = const()[name = tensor("op_439_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_439_end_mask_0 = const()[name = tensor("op_439_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_439_cast = slice_by_index(begin = var_439_begin_0, end = var_439_end_0, end_mask = var_439_end_mask_0, x = q_3_cast)[name = tensor("op_439_cast")]; + tensor var_443_begin_0 = const()[name = tensor("op_443_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_443_end_0 = const()[name = tensor("op_443_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_443_end_mask_0 = const()[name = tensor("op_443_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_443_cast = slice_by_index(begin = var_443_begin_0, end = var_443_end_0, end_mask = var_443_end_mask_0, x = q_3_cast)[name = tensor("op_443_cast")]; + tensor var_447_begin_0 = const()[name = tensor("op_447_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_447_end_0 = const()[name = tensor("op_447_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_447_end_mask_0 = const()[name = tensor("op_447_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_447_cast = slice_by_index(begin = var_447_begin_0, end = var_447_end_0, end_mask = var_447_end_mask_0, x = q_3_cast)[name = tensor("op_447_cast")]; + tensor var_451_begin_0 = const()[name = tensor("op_451_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_451_end_0 = const()[name = tensor("op_451_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_451_end_mask_0 = const()[name = tensor("op_451_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_451_cast = slice_by_index(begin = var_451_begin_0, end = var_451_end_0, end_mask = var_451_end_mask_0, x = q_3_cast)[name = tensor("op_451_cast")]; + tensor var_455_begin_0 = const()[name = tensor("op_455_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_455_end_0 = const()[name = tensor("op_455_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_455_end_mask_0 = const()[name = tensor("op_455_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_455_cast = slice_by_index(begin = var_455_begin_0, end = var_455_end_0, end_mask = var_455_end_mask_0, x = q_3_cast)[name = tensor("op_455_cast")]; + tensor var_459_begin_0 = const()[name = tensor("op_459_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_459_end_0 = const()[name = tensor("op_459_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_459_end_mask_0 = const()[name = tensor("op_459_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_459_cast = slice_by_index(begin = var_459_begin_0, end = var_459_end_0, end_mask = var_459_end_mask_0, x = q_3_cast)[name = tensor("op_459_cast")]; + tensor var_463_begin_0 = const()[name = tensor("op_463_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_463_end_0 = const()[name = tensor("op_463_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_463_end_mask_0 = const()[name = tensor("op_463_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_463_cast = slice_by_index(begin = var_463_begin_0, end = var_463_end_0, end_mask = var_463_end_mask_0, x = q_3_cast)[name = tensor("op_463_cast")]; + tensor var_467_begin_0 = const()[name = tensor("op_467_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_467_end_0 = const()[name = tensor("op_467_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_467_end_mask_0 = const()[name = tensor("op_467_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_467_cast = slice_by_index(begin = var_467_begin_0, end = var_467_end_0, end_mask = var_467_end_mask_0, x = q_3_cast)[name = tensor("op_467_cast")]; + tensor k_7_perm_0 = const()[name = tensor("k_7_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_474_begin_0 = const()[name = tensor("op_474_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_474_end_0 = const()[name = tensor("op_474_end_0"), val = tensor([2, 77, 1, 40])]; + tensor var_474_end_mask_0 = const()[name = tensor("op_474_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_30 = transpose(perm = k_7_perm_0, x = k_5_cast)[name = tensor("transpose_30")]; + tensor var_474_cast = slice_by_index(begin = var_474_begin_0, end = var_474_end_0, end_mask = var_474_end_mask_0, x = transpose_30)[name = tensor("op_474_cast")]; + tensor var_478_begin_0 = const()[name = tensor("op_478_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_478_end_0 = const()[name = tensor("op_478_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_478_end_mask_0 = const()[name = tensor("op_478_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_478_cast = slice_by_index(begin = var_478_begin_0, end = var_478_end_0, end_mask = var_478_end_mask_0, x = transpose_30)[name = tensor("op_478_cast")]; + tensor var_482_begin_0 = const()[name = tensor("op_482_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_482_end_0 = const()[name = tensor("op_482_end_0"), val = tensor([2, 77, 1, 120])]; + tensor var_482_end_mask_0 = const()[name = tensor("op_482_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_482_cast = slice_by_index(begin = var_482_begin_0, end = var_482_end_0, end_mask = var_482_end_mask_0, x = transpose_30)[name = tensor("op_482_cast")]; + tensor var_486_begin_0 = const()[name = tensor("op_486_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_486_end_0 = const()[name = tensor("op_486_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_486_end_mask_0 = const()[name = tensor("op_486_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_486_cast = slice_by_index(begin = var_486_begin_0, end = var_486_end_0, end_mask = var_486_end_mask_0, x = transpose_30)[name = tensor("op_486_cast")]; + tensor var_490_begin_0 = const()[name = tensor("op_490_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_490_end_0 = const()[name = tensor("op_490_end_0"), val = tensor([2, 77, 1, 200])]; + tensor var_490_end_mask_0 = const()[name = tensor("op_490_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_490_cast = slice_by_index(begin = var_490_begin_0, end = var_490_end_0, end_mask = var_490_end_mask_0, x = transpose_30)[name = tensor("op_490_cast")]; + tensor var_494_begin_0 = const()[name = tensor("op_494_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_494_end_0 = const()[name = tensor("op_494_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_494_end_mask_0 = const()[name = tensor("op_494_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_494_cast = slice_by_index(begin = var_494_begin_0, end = var_494_end_0, end_mask = var_494_end_mask_0, x = transpose_30)[name = tensor("op_494_cast")]; + tensor var_498_begin_0 = const()[name = tensor("op_498_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_498_end_0 = const()[name = tensor("op_498_end_0"), val = tensor([2, 77, 1, 280])]; + tensor var_498_end_mask_0 = const()[name = tensor("op_498_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_498_cast = slice_by_index(begin = var_498_begin_0, end = var_498_end_0, end_mask = var_498_end_mask_0, x = transpose_30)[name = tensor("op_498_cast")]; + tensor var_502_begin_0 = const()[name = tensor("op_502_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_502_end_0 = const()[name = tensor("op_502_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_502_end_mask_0 = const()[name = tensor("op_502_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_502_cast = slice_by_index(begin = var_502_begin_0, end = var_502_end_0, end_mask = var_502_end_mask_0, x = transpose_30)[name = tensor("op_502_cast")]; + tensor var_504_begin_0 = const()[name = tensor("op_504_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_504_end_0 = const()[name = tensor("op_504_end_0"), val = tensor([2, 40, 1, 77])]; + tensor var_504_end_mask_0 = const()[name = tensor("op_504_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_504_cast = slice_by_index(begin = var_504_begin_0, end = var_504_end_0, end_mask = var_504_end_mask_0, x = v_3_cast)[name = tensor("op_504_cast")]; + tensor var_508_begin_0 = const()[name = tensor("op_508_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_508_end_0 = const()[name = tensor("op_508_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_508_end_mask_0 = const()[name = tensor("op_508_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_508_cast = slice_by_index(begin = var_508_begin_0, end = var_508_end_0, end_mask = var_508_end_mask_0, x = v_3_cast)[name = tensor("op_508_cast")]; + tensor var_512_begin_0 = const()[name = tensor("op_512_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_512_end_0 = const()[name = tensor("op_512_end_0"), val = tensor([2, 120, 1, 77])]; + tensor var_512_end_mask_0 = const()[name = tensor("op_512_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_512_cast = slice_by_index(begin = var_512_begin_0, end = var_512_end_0, end_mask = var_512_end_mask_0, x = v_3_cast)[name = tensor("op_512_cast")]; + tensor var_516_begin_0 = const()[name = tensor("op_516_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_516_end_0 = const()[name = tensor("op_516_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_516_end_mask_0 = const()[name = tensor("op_516_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_516_cast = slice_by_index(begin = var_516_begin_0, end = var_516_end_0, end_mask = var_516_end_mask_0, x = v_3_cast)[name = tensor("op_516_cast")]; + tensor var_520_begin_0 = const()[name = tensor("op_520_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_520_end_0 = const()[name = tensor("op_520_end_0"), val = tensor([2, 200, 1, 77])]; + tensor var_520_end_mask_0 = const()[name = tensor("op_520_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_520_cast = slice_by_index(begin = var_520_begin_0, end = var_520_end_0, end_mask = var_520_end_mask_0, x = v_3_cast)[name = tensor("op_520_cast")]; + tensor var_524_begin_0 = const()[name = tensor("op_524_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_524_end_0 = const()[name = tensor("op_524_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_524_end_mask_0 = const()[name = tensor("op_524_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_524_cast = slice_by_index(begin = var_524_begin_0, end = var_524_end_0, end_mask = var_524_end_mask_0, x = v_3_cast)[name = tensor("op_524_cast")]; + tensor var_528_begin_0 = const()[name = tensor("op_528_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_528_end_0 = const()[name = tensor("op_528_end_0"), val = tensor([2, 280, 1, 77])]; + tensor var_528_end_mask_0 = const()[name = tensor("op_528_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_528_cast = slice_by_index(begin = var_528_begin_0, end = var_528_end_0, end_mask = var_528_end_mask_0, x = v_3_cast)[name = tensor("op_528_cast")]; + tensor var_532_begin_0 = const()[name = tensor("op_532_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_532_end_0 = const()[name = tensor("op_532_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_532_end_mask_0 = const()[name = tensor("op_532_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_532_cast = slice_by_index(begin = var_532_begin_0, end = var_532_end_0, end_mask = var_532_end_mask_0, x = v_3_cast)[name = tensor("op_532_cast")]; + tensor var_536_equation_0 = const()[name = tensor("op_536_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_536_cast = einsum(equation = var_536_equation_0, values = (var_474_cast, var_439_cast))[name = tensor("op_536_cast")]; + tensor var_537_to_fp16 = const()[name = tensor("op_537_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_17_cast = mul(x = var_536_cast, y = var_537_to_fp16)[name = tensor("aw_17_cast")]; + tensor var_540_equation_0 = const()[name = tensor("op_540_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_540_cast = einsum(equation = var_540_equation_0, values = (var_478_cast, var_443_cast))[name = tensor("op_540_cast")]; + tensor var_541_to_fp16 = const()[name = tensor("op_541_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_19_cast = mul(x = var_540_cast, y = var_541_to_fp16)[name = tensor("aw_19_cast")]; + tensor var_544_equation_0 = const()[name = tensor("op_544_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_544_cast = einsum(equation = var_544_equation_0, values = (var_482_cast, var_447_cast))[name = tensor("op_544_cast")]; + tensor var_545_to_fp16 = const()[name = tensor("op_545_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_21_cast = mul(x = var_544_cast, y = var_545_to_fp16)[name = tensor("aw_21_cast")]; + tensor var_548_equation_0 = const()[name = tensor("op_548_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_548_cast = einsum(equation = var_548_equation_0, values = (var_486_cast, var_451_cast))[name = tensor("op_548_cast")]; + tensor var_549_to_fp16 = const()[name = tensor("op_549_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_23_cast = mul(x = var_548_cast, y = var_549_to_fp16)[name = tensor("aw_23_cast")]; + tensor var_552_equation_0 = const()[name = tensor("op_552_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_552_cast = einsum(equation = var_552_equation_0, values = (var_490_cast, var_455_cast))[name = tensor("op_552_cast")]; + tensor var_553_to_fp16 = const()[name = tensor("op_553_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_25_cast = mul(x = var_552_cast, y = var_553_to_fp16)[name = tensor("aw_25_cast")]; + tensor var_556_equation_0 = const()[name = tensor("op_556_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_556_cast = einsum(equation = var_556_equation_0, values = (var_494_cast, var_459_cast))[name = tensor("op_556_cast")]; + tensor var_557_to_fp16 = const()[name = tensor("op_557_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_27_cast = mul(x = var_556_cast, y = var_557_to_fp16)[name = tensor("aw_27_cast")]; + tensor var_560_equation_0 = const()[name = tensor("op_560_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_560_cast = einsum(equation = var_560_equation_0, values = (var_498_cast, var_463_cast))[name = tensor("op_560_cast")]; + tensor var_561_to_fp16 = const()[name = tensor("op_561_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_29_cast = mul(x = var_560_cast, y = var_561_to_fp16)[name = tensor("aw_29_cast")]; + tensor var_564_equation_0 = const()[name = tensor("op_564_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_564_cast = einsum(equation = var_564_equation_0, values = (var_502_cast, var_467_cast))[name = tensor("op_564_cast")]; + tensor var_565_to_fp16 = const()[name = tensor("op_565_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_31_cast = mul(x = var_564_cast, y = var_565_to_fp16)[name = tensor("aw_31_cast")]; + tensor var_567_cast = softmax(axis = var_115, x = aw_17_cast)[name = tensor("op_567_cast")]; + tensor var_568_cast = softmax(axis = var_115, x = aw_19_cast)[name = tensor("op_568_cast")]; + tensor var_569_cast = softmax(axis = var_115, x = aw_21_cast)[name = tensor("op_569_cast")]; + tensor var_570_cast = softmax(axis = var_115, x = aw_23_cast)[name = tensor("op_570_cast")]; + tensor var_571_cast = softmax(axis = var_115, x = aw_25_cast)[name = tensor("op_571_cast")]; + tensor var_572_cast = softmax(axis = var_115, x = aw_27_cast)[name = tensor("op_572_cast")]; + tensor var_573_cast = softmax(axis = var_115, x = aw_29_cast)[name = tensor("op_573_cast")]; + tensor var_574_cast = softmax(axis = var_115, x = aw_31_cast)[name = tensor("op_574_cast")]; + tensor var_576_equation_0 = const()[name = tensor("op_576_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_576_cast = einsum(equation = var_576_equation_0, values = (var_504_cast, var_567_cast))[name = tensor("op_576_cast")]; + tensor var_578_equation_0 = const()[name = tensor("op_578_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_578_cast = einsum(equation = var_578_equation_0, values = (var_508_cast, var_568_cast))[name = tensor("op_578_cast")]; + tensor var_580_equation_0 = const()[name = tensor("op_580_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_580_cast = einsum(equation = var_580_equation_0, values = (var_512_cast, var_569_cast))[name = tensor("op_580_cast")]; + tensor var_582_equation_0 = const()[name = tensor("op_582_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_582_cast = einsum(equation = var_582_equation_0, values = (var_516_cast, var_570_cast))[name = tensor("op_582_cast")]; + tensor var_584_equation_0 = const()[name = tensor("op_584_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_584_cast = einsum(equation = var_584_equation_0, values = (var_520_cast, var_571_cast))[name = tensor("op_584_cast")]; + tensor var_586_equation_0 = const()[name = tensor("op_586_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_586_cast = einsum(equation = var_586_equation_0, values = (var_524_cast, var_572_cast))[name = tensor("op_586_cast")]; + tensor var_588_equation_0 = const()[name = tensor("op_588_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_588_cast = einsum(equation = var_588_equation_0, values = (var_528_cast, var_573_cast))[name = tensor("op_588_cast")]; + tensor var_590_equation_0 = const()[name = tensor("op_590_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_590_cast = einsum(equation = var_590_equation_0, values = (var_532_cast, var_574_cast))[name = tensor("op_590_cast")]; + tensor input_27_interleave_0 = const()[name = tensor("input_27_interleave_0"), val = tensor(false)]; + tensor input_27_cast = concat(axis = var_115, interleave = input_27_interleave_0, values = (var_576_cast, var_578_cast, var_580_cast, var_582_cast, var_584_cast, var_586_cast, var_588_cast, var_590_cast))[name = tensor("input_27_cast")]; + tensor var_596 = const()[name = tensor("op_596"), val = tensor([1, 1])]; + tensor var_598 = const()[name = tensor("op_598"), val = tensor([1, 1])]; + tensor var_600_pad_type_0 = const()[name = tensor("op_600_pad_type_0"), val = tensor("custom")]; + tensor var_600_pad_0 = const()[name = tensor("op_600_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162496))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162688)))]; + tensor var_600_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_598, groups = var_115, pad = var_600_pad_0, pad_type = var_600_pad_type_0, strides = var_596, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_27_cast)[name = tensor("op_600_cast")]; + tensor inputs_5_cast = add(x = var_600_cast, y = inputs_3_cast)[name = tensor("inputs_5_cast")]; + tensor var_604 = const()[name = tensor("op_604"), val = tensor([1])]; + tensor channels_mean_5_cast = reduce_mean(axes = var_604, keep_dims = var_110, x = inputs_5_cast)[name = tensor("channels_mean_5_cast")]; + tensor zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast)[name = tensor("zero_mean_5_cast")]; + tensor zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast)[name = tensor("zero_mean_sq_5_cast")]; + tensor var_608 = const()[name = tensor("op_608"), val = tensor([1])]; + tensor var_609_cast = reduce_mean(axes = var_608, keep_dims = var_110, x = zero_mean_sq_5_cast)[name = tensor("op_609_cast")]; + tensor var_610_to_fp16 = const()[name = tensor("op_610_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_611_cast = add(x = var_609_cast, y = var_610_to_fp16)[name = tensor("op_611_cast")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_611_cast)[name = tensor("denom_5_cast")]; + tensor out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast)[name = tensor("out_5_cast")]; + tensor var_615_to_fp16 = const()[name = tensor("op_615_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4163392)))]; + tensor var_616_cast = add(x = out_5_cast, y = var_615_to_fp16)[name = tensor("op_616_cast")]; + tensor var_618_to_fp16 = const()[name = tensor("op_618_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4164096)))]; + tensor input_29_cast = mul(x = var_616_cast, y = var_618_to_fp16)[name = tensor("input_29_cast")]; + tensor var_626 = const()[name = tensor("op_626"), val = tensor([1, 1])]; + tensor var_628 = const()[name = tensor("op_628"), val = tensor([1, 1])]; + tensor var_630_pad_type_0 = const()[name = tensor("op_630_pad_type_0"), val = tensor("custom")]; + tensor var_630_pad_0 = const()[name = tensor("op_630_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4164800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4779264))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4779456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4781440))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_630_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_628, groups = var_115, pad = var_630_pad_0, pad_type = var_630_pad_type_0, strides = var_626, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_29_cast)[name = tensor("op_630_cast")]; + tensor var_631_split_sizes_0 = const()[name = tensor("op_631_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_631_axis_0 = const()[name = tensor("op_631_axis_0"), val = tensor(1)]; + tensor var_631_cast_0, tensor var_631_cast_1 = split(axis = var_631_axis_0, split_sizes = var_631_split_sizes_0, x = var_630_cast)[name = tensor("op_631_cast")]; + tensor var_633_mode_0 = const()[name = tensor("op_633_mode_0"), val = tensor("EXACT")]; + tensor var_633_cast = gelu(mode = var_633_mode_0, x = var_631_cast_1)[name = tensor("op_633_cast")]; + tensor input_31_cast = mul(x = var_631_cast_0, y = var_633_cast)[name = tensor("input_31_cast")]; + tensor var_637 = const()[name = tensor("op_637"), val = tensor([1, 1])]; + tensor var_639 = const()[name = tensor("op_639"), val = tensor([1, 1])]; + tensor var_641_pad_type_0 = const()[name = tensor("op_641_pad_type_0"), val = tensor("custom")]; + tensor var_641_pad_0 = const()[name = tensor("op_641_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4781632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5088896))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5089088)))]; + tensor var_641_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_639, groups = var_115, pad = var_641_pad_0, pad_type = var_641_pad_type_0, strides = var_637, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_31_cast)[name = tensor("op_641_cast")]; + tensor hidden_states_15_cast = add(x = var_641_cast, y = inputs_5_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_643 = const()[name = tensor("op_643"), val = tensor([2, 320, 96, 96])]; + tensor input_33_cast = reshape(shape = var_643, x = hidden_states_15_cast)[name = tensor("input_33_cast")]; + tensor var_647 = const()[name = tensor("op_647"), val = tensor([1, 1])]; + tensor var_649 = const()[name = tensor("op_649"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5089792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5166656))), name = tensor("down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5166848)))]; + tensor hidden_states_17_cast = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_649, groups = var_115, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_647, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized, x = input_33_cast)[name = tensor("hidden_states_17_cast")]; + tensor input_35_cast = add(x = hidden_states_17_cast, y = hidden_states_5_cast)[name = tensor("input_35_cast")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_35_cast)[name = tensor("reshape_12_cast")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_11_cast")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5167552)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5168256)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_7_cast")]; + tensor input_39_cast = silu(x = add_7_cast)[name = tensor("input_39_cast")]; + tensor var_664 = const()[name = tensor("op_664"), val = tensor([1, 1])]; + tensor var_666 = const()[name = tensor("op_666"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5168960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5860224))), name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5860416)))]; + tensor hidden_states_19_cast = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_666, groups = var_115, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_664, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_39_cast)[name = tensor("hidden_states_19_cast")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; + tensor var_674 = const()[name = tensor("op_674"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5861120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6168384))), name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6168576)))]; + tensor temb_3_cast = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_674, groups = var_115, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_672, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_3_cast")]; + tensor input_43_cast = add(x = hidden_states_19_cast, y = temb_3_cast)[name = tensor("input_43_cast")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_43_cast)[name = tensor("reshape_16_cast")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_14_cast")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6169280)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6169984)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_9_cast")]; + tensor input_47_cast = silu(x = add_9_cast)[name = tensor("input_47_cast")]; + tensor var_684 = const()[name = tensor("op_684"), val = tensor([1, 1])]; + tensor var_686 = const()[name = tensor("op_686"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6170688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6861952))), name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6862144)))]; + tensor hidden_states_21_cast = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_686, groups = var_115, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_684, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_47_cast)[name = tensor("hidden_states_21_cast")]; + tensor hidden_states_23_cast = add(x = input_35_cast, y = hidden_states_21_cast)[name = tensor("hidden_states_23_cast")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = hidden_states_23_cast)[name = tensor("reshape_20_cast")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_17_cast")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6862848)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6863552)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_11_cast")]; + tensor var_706 = const()[name = tensor("op_706"), val = tensor([1, 1])]; + tensor var_708 = const()[name = tensor("op_708"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6864256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6941120))), name = tensor("down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6941312)))]; + tensor hidden_states_25_cast = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_708, groups = var_115, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_706, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized, x = add_11_cast)[name = tensor("hidden_states_25_cast")]; + tensor var_713 = const()[name = tensor("op_713"), val = tensor([2, 320, 1, 9216])]; + tensor inputs_7_cast = reshape(shape = var_713, x = hidden_states_25_cast)[name = tensor("inputs_7_cast")]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1])]; + tensor channels_mean_7_cast = reduce_mean(axes = var_723, keep_dims = var_110, x = inputs_7_cast)[name = tensor("channels_mean_7_cast")]; + tensor zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast)[name = tensor("zero_mean_7_cast")]; + tensor zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast)[name = tensor("zero_mean_sq_7_cast")]; + tensor var_727 = const()[name = tensor("op_727"), val = tensor([1])]; + tensor var_728_cast = reduce_mean(axes = var_727, keep_dims = var_110, x = zero_mean_sq_7_cast)[name = tensor("op_728_cast")]; + tensor var_729_to_fp16 = const()[name = tensor("op_729_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_730_cast = add(x = var_728_cast, y = var_729_to_fp16)[name = tensor("op_730_cast")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_730_cast)[name = tensor("denom_7_cast")]; + tensor out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast)[name = tensor("out_7_cast")]; + tensor var_734_to_fp16 = const()[name = tensor("op_734_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6942016)))]; + tensor var_735_cast = add(x = out_7_cast, y = var_734_to_fp16)[name = tensor("op_735_cast")]; + tensor var_737_to_fp16 = const()[name = tensor("op_737_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6942720)))]; + tensor hidden_states_27_cast = mul(x = var_735_cast, y = var_737_to_fp16)[name = tensor("hidden_states_27_cast")]; + tensor var_744 = const()[name = tensor("op_744"), val = tensor([1, 1])]; + tensor var_746 = const()[name = tensor("op_746"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6943424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7020288))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_5_cast = conv(dilations = var_746, groups = var_115, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_744, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor("q_5_cast")]; + tensor var_750 = const()[name = tensor("op_750"), val = tensor([1, 1])]; + tensor var_752 = const()[name = tensor("op_752"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7020480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7097344))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_9_cast = conv(dilations = var_752, groups = var_115, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_750, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor("k_9_cast")]; + tensor var_756 = const()[name = tensor("op_756"), val = tensor([1, 1])]; + tensor var_758 = const()[name = tensor("op_758"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7097536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7174400))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_5_cast = conv(dilations = var_758, groups = var_115, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_756, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor("v_5_cast")]; + tensor var_762_begin_0 = const()[name = tensor("op_762_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_762_end_0 = const()[name = tensor("op_762_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_762_end_mask_0 = const()[name = tensor("op_762_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_762_cast = slice_by_index(begin = var_762_begin_0, end = var_762_end_0, end_mask = var_762_end_mask_0, x = q_5_cast)[name = tensor("op_762_cast")]; + tensor var_766_begin_0 = const()[name = tensor("op_766_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_766_end_0 = const()[name = tensor("op_766_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_766_end_mask_0 = const()[name = tensor("op_766_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_766_cast = slice_by_index(begin = var_766_begin_0, end = var_766_end_0, end_mask = var_766_end_mask_0, x = q_5_cast)[name = tensor("op_766_cast")]; + tensor var_770_begin_0 = const()[name = tensor("op_770_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_770_end_0 = const()[name = tensor("op_770_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_770_end_mask_0 = const()[name = tensor("op_770_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_770_cast = slice_by_index(begin = var_770_begin_0, end = var_770_end_0, end_mask = var_770_end_mask_0, x = q_5_cast)[name = tensor("op_770_cast")]; + tensor var_774_begin_0 = const()[name = tensor("op_774_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_774_end_0 = const()[name = tensor("op_774_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_774_end_mask_0 = const()[name = tensor("op_774_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_774_cast = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = q_5_cast)[name = tensor("op_774_cast")]; + tensor var_778_begin_0 = const()[name = tensor("op_778_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_778_end_0 = const()[name = tensor("op_778_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_778_end_mask_0 = const()[name = tensor("op_778_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_778_cast = slice_by_index(begin = var_778_begin_0, end = var_778_end_0, end_mask = var_778_end_mask_0, x = q_5_cast)[name = tensor("op_778_cast")]; + tensor var_782_begin_0 = const()[name = tensor("op_782_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_782_end_0 = const()[name = tensor("op_782_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_782_end_mask_0 = const()[name = tensor("op_782_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_782_cast = slice_by_index(begin = var_782_begin_0, end = var_782_end_0, end_mask = var_782_end_mask_0, x = q_5_cast)[name = tensor("op_782_cast")]; + tensor var_786_begin_0 = const()[name = tensor("op_786_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_786_end_0 = const()[name = tensor("op_786_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_786_end_mask_0 = const()[name = tensor("op_786_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_786_cast = slice_by_index(begin = var_786_begin_0, end = var_786_end_0, end_mask = var_786_end_mask_0, x = q_5_cast)[name = tensor("op_786_cast")]; + tensor var_790_begin_0 = const()[name = tensor("op_790_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_790_end_0 = const()[name = tensor("op_790_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_790_end_mask_0 = const()[name = tensor("op_790_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_790_cast = slice_by_index(begin = var_790_begin_0, end = var_790_end_0, end_mask = var_790_end_mask_0, x = q_5_cast)[name = tensor("op_790_cast")]; + tensor k_11_perm_0 = const()[name = tensor("k_11_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_797_begin_0 = const()[name = tensor("op_797_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_797_end_0 = const()[name = tensor("op_797_end_0"), val = tensor([2, 9216, 1, 40])]; + tensor var_797_end_mask_0 = const()[name = tensor("op_797_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_29 = transpose(perm = k_11_perm_0, x = k_9_cast)[name = tensor("transpose_29")]; + tensor var_797_cast = slice_by_index(begin = var_797_begin_0, end = var_797_end_0, end_mask = var_797_end_mask_0, x = transpose_29)[name = tensor("op_797_cast")]; + tensor var_801_begin_0 = const()[name = tensor("op_801_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_801_end_0 = const()[name = tensor("op_801_end_0"), val = tensor([2, 9216, 1, 80])]; + tensor var_801_end_mask_0 = const()[name = tensor("op_801_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_801_cast = slice_by_index(begin = var_801_begin_0, end = var_801_end_0, end_mask = var_801_end_mask_0, x = transpose_29)[name = tensor("op_801_cast")]; + tensor var_805_begin_0 = const()[name = tensor("op_805_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_805_end_0 = const()[name = tensor("op_805_end_0"), val = tensor([2, 9216, 1, 120])]; + tensor var_805_end_mask_0 = const()[name = tensor("op_805_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_805_cast = slice_by_index(begin = var_805_begin_0, end = var_805_end_0, end_mask = var_805_end_mask_0, x = transpose_29)[name = tensor("op_805_cast")]; + tensor var_809_begin_0 = const()[name = tensor("op_809_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_809_end_0 = const()[name = tensor("op_809_end_0"), val = tensor([2, 9216, 1, 160])]; + tensor var_809_end_mask_0 = const()[name = tensor("op_809_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_809_cast = slice_by_index(begin = var_809_begin_0, end = var_809_end_0, end_mask = var_809_end_mask_0, x = transpose_29)[name = tensor("op_809_cast")]; + tensor var_813_begin_0 = const()[name = tensor("op_813_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_813_end_0 = const()[name = tensor("op_813_end_0"), val = tensor([2, 9216, 1, 200])]; + tensor var_813_end_mask_0 = const()[name = tensor("op_813_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_813_cast = slice_by_index(begin = var_813_begin_0, end = var_813_end_0, end_mask = var_813_end_mask_0, x = transpose_29)[name = tensor("op_813_cast")]; + tensor var_817_begin_0 = const()[name = tensor("op_817_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_817_end_0 = const()[name = tensor("op_817_end_0"), val = tensor([2, 9216, 1, 240])]; + tensor var_817_end_mask_0 = const()[name = tensor("op_817_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_817_cast = slice_by_index(begin = var_817_begin_0, end = var_817_end_0, end_mask = var_817_end_mask_0, x = transpose_29)[name = tensor("op_817_cast")]; + tensor var_821_begin_0 = const()[name = tensor("op_821_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_821_end_0 = const()[name = tensor("op_821_end_0"), val = tensor([2, 9216, 1, 280])]; + tensor var_821_end_mask_0 = const()[name = tensor("op_821_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_821_cast = slice_by_index(begin = var_821_begin_0, end = var_821_end_0, end_mask = var_821_end_mask_0, x = transpose_29)[name = tensor("op_821_cast")]; + tensor var_825_begin_0 = const()[name = tensor("op_825_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_825_end_0 = const()[name = tensor("op_825_end_0"), val = tensor([2, 9216, 1, 320])]; + tensor var_825_end_mask_0 = const()[name = tensor("op_825_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_825_cast = slice_by_index(begin = var_825_begin_0, end = var_825_end_0, end_mask = var_825_end_mask_0, x = transpose_29)[name = tensor("op_825_cast")]; + tensor var_827_begin_0 = const()[name = tensor("op_827_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_827_end_0 = const()[name = tensor("op_827_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_827_end_mask_0 = const()[name = tensor("op_827_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_827_cast = slice_by_index(begin = var_827_begin_0, end = var_827_end_0, end_mask = var_827_end_mask_0, x = v_5_cast)[name = tensor("op_827_cast")]; + tensor var_831_begin_0 = const()[name = tensor("op_831_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_831_end_0 = const()[name = tensor("op_831_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_831_end_mask_0 = const()[name = tensor("op_831_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_831_cast = slice_by_index(begin = var_831_begin_0, end = var_831_end_0, end_mask = var_831_end_mask_0, x = v_5_cast)[name = tensor("op_831_cast")]; + tensor var_835_begin_0 = const()[name = tensor("op_835_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_835_end_0 = const()[name = tensor("op_835_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_835_end_mask_0 = const()[name = tensor("op_835_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_835_cast = slice_by_index(begin = var_835_begin_0, end = var_835_end_0, end_mask = var_835_end_mask_0, x = v_5_cast)[name = tensor("op_835_cast")]; + tensor var_839_begin_0 = const()[name = tensor("op_839_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_839_end_0 = const()[name = tensor("op_839_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_839_end_mask_0 = const()[name = tensor("op_839_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_839_cast = slice_by_index(begin = var_839_begin_0, end = var_839_end_0, end_mask = var_839_end_mask_0, x = v_5_cast)[name = tensor("op_839_cast")]; + tensor var_843_begin_0 = const()[name = tensor("op_843_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_843_end_0 = const()[name = tensor("op_843_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_843_end_mask_0 = const()[name = tensor("op_843_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_843_cast = slice_by_index(begin = var_843_begin_0, end = var_843_end_0, end_mask = var_843_end_mask_0, x = v_5_cast)[name = tensor("op_843_cast")]; + tensor var_847_begin_0 = const()[name = tensor("op_847_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_847_end_0 = const()[name = tensor("op_847_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_847_end_mask_0 = const()[name = tensor("op_847_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_847_cast = slice_by_index(begin = var_847_begin_0, end = var_847_end_0, end_mask = var_847_end_mask_0, x = v_5_cast)[name = tensor("op_847_cast")]; + tensor var_851_begin_0 = const()[name = tensor("op_851_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_851_end_0 = const()[name = tensor("op_851_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_851_end_mask_0 = const()[name = tensor("op_851_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_851_cast = slice_by_index(begin = var_851_begin_0, end = var_851_end_0, end_mask = var_851_end_mask_0, x = v_5_cast)[name = tensor("op_851_cast")]; + tensor var_855_begin_0 = const()[name = tensor("op_855_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_855_end_0 = const()[name = tensor("op_855_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_855_end_mask_0 = const()[name = tensor("op_855_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_855_cast = slice_by_index(begin = var_855_begin_0, end = var_855_end_0, end_mask = var_855_end_mask_0, x = v_5_cast)[name = tensor("op_855_cast")]; + tensor var_859_equation_0 = const()[name = tensor("op_859_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_859_cast = einsum(equation = var_859_equation_0, values = (var_797_cast, var_762_cast))[name = tensor("op_859_cast")]; + tensor var_860_to_fp16 = const()[name = tensor("op_860_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_33_cast = mul(x = var_859_cast, y = var_860_to_fp16)[name = tensor("aw_33_cast")]; + tensor var_863_equation_0 = const()[name = tensor("op_863_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_863_cast = einsum(equation = var_863_equation_0, values = (var_801_cast, var_766_cast))[name = tensor("op_863_cast")]; + tensor var_864_to_fp16 = const()[name = tensor("op_864_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_35_cast = mul(x = var_863_cast, y = var_864_to_fp16)[name = tensor("aw_35_cast")]; + tensor var_867_equation_0 = const()[name = tensor("op_867_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_867_cast = einsum(equation = var_867_equation_0, values = (var_805_cast, var_770_cast))[name = tensor("op_867_cast")]; + tensor var_868_to_fp16 = const()[name = tensor("op_868_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_37_cast = mul(x = var_867_cast, y = var_868_to_fp16)[name = tensor("aw_37_cast")]; + tensor var_871_equation_0 = const()[name = tensor("op_871_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_871_cast = einsum(equation = var_871_equation_0, values = (var_809_cast, var_774_cast))[name = tensor("op_871_cast")]; + tensor var_872_to_fp16 = const()[name = tensor("op_872_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_39_cast = mul(x = var_871_cast, y = var_872_to_fp16)[name = tensor("aw_39_cast")]; + tensor var_875_equation_0 = const()[name = tensor("op_875_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_875_cast = einsum(equation = var_875_equation_0, values = (var_813_cast, var_778_cast))[name = tensor("op_875_cast")]; + tensor var_876_to_fp16 = const()[name = tensor("op_876_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_41_cast = mul(x = var_875_cast, y = var_876_to_fp16)[name = tensor("aw_41_cast")]; + tensor var_879_equation_0 = const()[name = tensor("op_879_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_879_cast = einsum(equation = var_879_equation_0, values = (var_817_cast, var_782_cast))[name = tensor("op_879_cast")]; + tensor var_880_to_fp16 = const()[name = tensor("op_880_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_43_cast = mul(x = var_879_cast, y = var_880_to_fp16)[name = tensor("aw_43_cast")]; + tensor var_883_equation_0 = const()[name = tensor("op_883_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_883_cast = einsum(equation = var_883_equation_0, values = (var_821_cast, var_786_cast))[name = tensor("op_883_cast")]; + tensor var_884_to_fp16 = const()[name = tensor("op_884_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_45_cast = mul(x = var_883_cast, y = var_884_to_fp16)[name = tensor("aw_45_cast")]; + tensor var_887_equation_0 = const()[name = tensor("op_887_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_887_cast = einsum(equation = var_887_equation_0, values = (var_825_cast, var_790_cast))[name = tensor("op_887_cast")]; + tensor var_888_to_fp16 = const()[name = tensor("op_888_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_47_cast = mul(x = var_887_cast, y = var_888_to_fp16)[name = tensor("aw_47_cast")]; + tensor var_890_cast = softmax(axis = var_115, x = aw_33_cast)[name = tensor("op_890_cast")]; + tensor var_891_cast = softmax(axis = var_115, x = aw_35_cast)[name = tensor("op_891_cast")]; + tensor var_892_cast = softmax(axis = var_115, x = aw_37_cast)[name = tensor("op_892_cast")]; + tensor var_893_cast = softmax(axis = var_115, x = aw_39_cast)[name = tensor("op_893_cast")]; + tensor var_894_cast = softmax(axis = var_115, x = aw_41_cast)[name = tensor("op_894_cast")]; + tensor var_895_cast = softmax(axis = var_115, x = aw_43_cast)[name = tensor("op_895_cast")]; + tensor var_896_cast = softmax(axis = var_115, x = aw_45_cast)[name = tensor("op_896_cast")]; + tensor var_897_cast = softmax(axis = var_115, x = aw_47_cast)[name = tensor("op_897_cast")]; + tensor var_899_equation_0 = const()[name = tensor("op_899_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_899_cast = einsum(equation = var_899_equation_0, values = (var_827_cast, var_890_cast))[name = tensor("op_899_cast")]; + tensor var_901_equation_0 = const()[name = tensor("op_901_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_901_cast = einsum(equation = var_901_equation_0, values = (var_831_cast, var_891_cast))[name = tensor("op_901_cast")]; + tensor var_903_equation_0 = const()[name = tensor("op_903_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_903_cast = einsum(equation = var_903_equation_0, values = (var_835_cast, var_892_cast))[name = tensor("op_903_cast")]; + tensor var_905_equation_0 = const()[name = tensor("op_905_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_905_cast = einsum(equation = var_905_equation_0, values = (var_839_cast, var_893_cast))[name = tensor("op_905_cast")]; + tensor var_907_equation_0 = const()[name = tensor("op_907_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_907_cast = einsum(equation = var_907_equation_0, values = (var_843_cast, var_894_cast))[name = tensor("op_907_cast")]; + tensor var_909_equation_0 = const()[name = tensor("op_909_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_909_cast = einsum(equation = var_909_equation_0, values = (var_847_cast, var_895_cast))[name = tensor("op_909_cast")]; + tensor var_911_equation_0 = const()[name = tensor("op_911_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_911_cast = einsum(equation = var_911_equation_0, values = (var_851_cast, var_896_cast))[name = tensor("op_911_cast")]; + tensor var_913_equation_0 = const()[name = tensor("op_913_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_913_cast = einsum(equation = var_913_equation_0, values = (var_855_cast, var_897_cast))[name = tensor("op_913_cast")]; + tensor input_51_interleave_0 = const()[name = tensor("input_51_interleave_0"), val = tensor(false)]; + tensor input_51_cast = concat(axis = var_115, interleave = input_51_interleave_0, values = (var_899_cast, var_901_cast, var_903_cast, var_905_cast, var_907_cast, var_909_cast, var_911_cast, var_913_cast))[name = tensor("input_51_cast")]; + tensor var_919 = const()[name = tensor("op_919"), val = tensor([1, 1])]; + tensor var_921 = const()[name = tensor("op_921"), val = tensor([1, 1])]; + tensor var_923_pad_type_0 = const()[name = tensor("op_923_pad_type_0"), val = tensor("custom")]; + tensor var_923_pad_0 = const()[name = tensor("op_923_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7174592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7251456))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7251648)))]; + tensor var_923_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_921, groups = var_115, pad = var_923_pad_0, pad_type = var_923_pad_type_0, strides = var_919, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_51_cast)[name = tensor("op_923_cast")]; + tensor inputs_9_cast = add(x = var_923_cast, y = inputs_7_cast)[name = tensor("inputs_9_cast")]; + tensor var_927 = const()[name = tensor("op_927"), val = tensor([1])]; + tensor channels_mean_9_cast = reduce_mean(axes = var_927, keep_dims = var_110, x = inputs_9_cast)[name = tensor("channels_mean_9_cast")]; + tensor zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast)[name = tensor("zero_mean_9_cast")]; + tensor zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast)[name = tensor("zero_mean_sq_9_cast")]; + tensor var_931 = const()[name = tensor("op_931"), val = tensor([1])]; + tensor var_932_cast = reduce_mean(axes = var_931, keep_dims = var_110, x = zero_mean_sq_9_cast)[name = tensor("op_932_cast")]; + tensor var_933_to_fp16 = const()[name = tensor("op_933_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_934_cast = add(x = var_932_cast, y = var_933_to_fp16)[name = tensor("op_934_cast")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_934_cast)[name = tensor("denom_9_cast")]; + tensor out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast)[name = tensor("out_9_cast")]; + tensor var_938_to_fp16 = const()[name = tensor("op_938_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7252352)))]; + tensor var_939_cast = add(x = out_9_cast, y = var_938_to_fp16)[name = tensor("op_939_cast")]; + tensor var_941_to_fp16 = const()[name = tensor("op_941_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7253056)))]; + tensor hidden_states_29_cast = mul(x = var_939_cast, y = var_941_to_fp16)[name = tensor("hidden_states_29_cast")]; + tensor var_948 = const()[name = tensor("op_948"), val = tensor([1, 1])]; + tensor var_950 = const()[name = tensor("op_950"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7253760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7330624))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_7_cast = conv(dilations = var_950, groups = var_115, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_948, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_29_cast)[name = tensor("q_7_cast")]; + tensor var_954 = const()[name = tensor("op_954"), val = tensor([1, 1])]; + tensor var_956 = const()[name = tensor("op_956"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7330816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7515200))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_13_cast = conv(dilations = var_956, groups = var_115, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_954, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_13_cast")]; + tensor var_960 = const()[name = tensor("op_960"), val = tensor([1, 1])]; + tensor var_962 = const()[name = tensor("op_962"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7515392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7699776))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_7_cast = conv(dilations = var_962, groups = var_115, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_960, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_7_cast")]; + tensor var_966_begin_0 = const()[name = tensor("op_966_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_966_end_0 = const()[name = tensor("op_966_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_966_end_mask_0 = const()[name = tensor("op_966_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_966_cast = slice_by_index(begin = var_966_begin_0, end = var_966_end_0, end_mask = var_966_end_mask_0, x = q_7_cast)[name = tensor("op_966_cast")]; + tensor var_970_begin_0 = const()[name = tensor("op_970_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_970_end_0 = const()[name = tensor("op_970_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_970_end_mask_0 = const()[name = tensor("op_970_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_970_cast = slice_by_index(begin = var_970_begin_0, end = var_970_end_0, end_mask = var_970_end_mask_0, x = q_7_cast)[name = tensor("op_970_cast")]; + tensor var_974_begin_0 = const()[name = tensor("op_974_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_974_end_0 = const()[name = tensor("op_974_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_974_end_mask_0 = const()[name = tensor("op_974_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_974_cast = slice_by_index(begin = var_974_begin_0, end = var_974_end_0, end_mask = var_974_end_mask_0, x = q_7_cast)[name = tensor("op_974_cast")]; + tensor var_978_begin_0 = const()[name = tensor("op_978_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_978_end_0 = const()[name = tensor("op_978_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_978_end_mask_0 = const()[name = tensor("op_978_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_978_cast = slice_by_index(begin = var_978_begin_0, end = var_978_end_0, end_mask = var_978_end_mask_0, x = q_7_cast)[name = tensor("op_978_cast")]; + tensor var_982_begin_0 = const()[name = tensor("op_982_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_982_end_0 = const()[name = tensor("op_982_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_982_end_mask_0 = const()[name = tensor("op_982_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_982_cast = slice_by_index(begin = var_982_begin_0, end = var_982_end_0, end_mask = var_982_end_mask_0, x = q_7_cast)[name = tensor("op_982_cast")]; + tensor var_986_begin_0 = const()[name = tensor("op_986_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_986_end_0 = const()[name = tensor("op_986_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_986_end_mask_0 = const()[name = tensor("op_986_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_986_cast = slice_by_index(begin = var_986_begin_0, end = var_986_end_0, end_mask = var_986_end_mask_0, x = q_7_cast)[name = tensor("op_986_cast")]; + tensor var_990_begin_0 = const()[name = tensor("op_990_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_990_end_0 = const()[name = tensor("op_990_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_990_end_mask_0 = const()[name = tensor("op_990_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_990_cast = slice_by_index(begin = var_990_begin_0, end = var_990_end_0, end_mask = var_990_end_mask_0, x = q_7_cast)[name = tensor("op_990_cast")]; + tensor var_994_begin_0 = const()[name = tensor("op_994_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_994_end_0 = const()[name = tensor("op_994_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_994_end_mask_0 = const()[name = tensor("op_994_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_994_cast = slice_by_index(begin = var_994_begin_0, end = var_994_end_0, end_mask = var_994_end_mask_0, x = q_7_cast)[name = tensor("op_994_cast")]; + tensor k_15_perm_0 = const()[name = tensor("k_15_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_1001_begin_0 = const()[name = tensor("op_1001_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1001_end_0 = const()[name = tensor("op_1001_end_0"), val = tensor([2, 77, 1, 40])]; + tensor var_1001_end_mask_0 = const()[name = tensor("op_1001_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_28 = transpose(perm = k_15_perm_0, x = k_13_cast)[name = tensor("transpose_28")]; + tensor var_1001_cast = slice_by_index(begin = var_1001_begin_0, end = var_1001_end_0, end_mask = var_1001_end_mask_0, x = transpose_28)[name = tensor("op_1001_cast")]; + tensor var_1005_begin_0 = const()[name = tensor("op_1005_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_1005_end_0 = const()[name = tensor("op_1005_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_1005_end_mask_0 = const()[name = tensor("op_1005_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1005_cast = slice_by_index(begin = var_1005_begin_0, end = var_1005_end_0, end_mask = var_1005_end_mask_0, x = transpose_28)[name = tensor("op_1005_cast")]; + tensor var_1009_begin_0 = const()[name = tensor("op_1009_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_1009_end_0 = const()[name = tensor("op_1009_end_0"), val = tensor([2, 77, 1, 120])]; + tensor var_1009_end_mask_0 = const()[name = tensor("op_1009_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1009_cast = slice_by_index(begin = var_1009_begin_0, end = var_1009_end_0, end_mask = var_1009_end_mask_0, x = transpose_28)[name = tensor("op_1009_cast")]; + tensor var_1013_begin_0 = const()[name = tensor("op_1013_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_1013_end_0 = const()[name = tensor("op_1013_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_1013_end_mask_0 = const()[name = tensor("op_1013_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1013_cast = slice_by_index(begin = var_1013_begin_0, end = var_1013_end_0, end_mask = var_1013_end_mask_0, x = transpose_28)[name = tensor("op_1013_cast")]; + tensor var_1017_begin_0 = const()[name = tensor("op_1017_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_1017_end_0 = const()[name = tensor("op_1017_end_0"), val = tensor([2, 77, 1, 200])]; + tensor var_1017_end_mask_0 = const()[name = tensor("op_1017_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1017_cast = slice_by_index(begin = var_1017_begin_0, end = var_1017_end_0, end_mask = var_1017_end_mask_0, x = transpose_28)[name = tensor("op_1017_cast")]; + tensor var_1021_begin_0 = const()[name = tensor("op_1021_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_1021_end_0 = const()[name = tensor("op_1021_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_1021_end_mask_0 = const()[name = tensor("op_1021_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1021_cast = slice_by_index(begin = var_1021_begin_0, end = var_1021_end_0, end_mask = var_1021_end_mask_0, x = transpose_28)[name = tensor("op_1021_cast")]; + tensor var_1025_begin_0 = const()[name = tensor("op_1025_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_1025_end_0 = const()[name = tensor("op_1025_end_0"), val = tensor([2, 77, 1, 280])]; + tensor var_1025_end_mask_0 = const()[name = tensor("op_1025_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1025_cast = slice_by_index(begin = var_1025_begin_0, end = var_1025_end_0, end_mask = var_1025_end_mask_0, x = transpose_28)[name = tensor("op_1025_cast")]; + tensor var_1029_begin_0 = const()[name = tensor("op_1029_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_1029_end_0 = const()[name = tensor("op_1029_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_1029_end_mask_0 = const()[name = tensor("op_1029_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1029_cast = slice_by_index(begin = var_1029_begin_0, end = var_1029_end_0, end_mask = var_1029_end_mask_0, x = transpose_28)[name = tensor("op_1029_cast")]; + tensor var_1031_begin_0 = const()[name = tensor("op_1031_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1031_end_0 = const()[name = tensor("op_1031_end_0"), val = tensor([2, 40, 1, 77])]; + tensor var_1031_end_mask_0 = const()[name = tensor("op_1031_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1031_cast = slice_by_index(begin = var_1031_begin_0, end = var_1031_end_0, end_mask = var_1031_end_mask_0, x = v_7_cast)[name = tensor("op_1031_cast")]; + tensor var_1035_begin_0 = const()[name = tensor("op_1035_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_1035_end_0 = const()[name = tensor("op_1035_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_1035_end_mask_0 = const()[name = tensor("op_1035_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1035_cast = slice_by_index(begin = var_1035_begin_0, end = var_1035_end_0, end_mask = var_1035_end_mask_0, x = v_7_cast)[name = tensor("op_1035_cast")]; + tensor var_1039_begin_0 = const()[name = tensor("op_1039_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_1039_end_0 = const()[name = tensor("op_1039_end_0"), val = tensor([2, 120, 1, 77])]; + tensor var_1039_end_mask_0 = const()[name = tensor("op_1039_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1039_cast = slice_by_index(begin = var_1039_begin_0, end = var_1039_end_0, end_mask = var_1039_end_mask_0, x = v_7_cast)[name = tensor("op_1039_cast")]; + tensor var_1043_begin_0 = const()[name = tensor("op_1043_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_1043_end_0 = const()[name = tensor("op_1043_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_1043_end_mask_0 = const()[name = tensor("op_1043_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1043_cast = slice_by_index(begin = var_1043_begin_0, end = var_1043_end_0, end_mask = var_1043_end_mask_0, x = v_7_cast)[name = tensor("op_1043_cast")]; + tensor var_1047_begin_0 = const()[name = tensor("op_1047_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_1047_end_0 = const()[name = tensor("op_1047_end_0"), val = tensor([2, 200, 1, 77])]; + tensor var_1047_end_mask_0 = const()[name = tensor("op_1047_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1047_cast = slice_by_index(begin = var_1047_begin_0, end = var_1047_end_0, end_mask = var_1047_end_mask_0, x = v_7_cast)[name = tensor("op_1047_cast")]; + tensor var_1051_begin_0 = const()[name = tensor("op_1051_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_1051_end_0 = const()[name = tensor("op_1051_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_1051_end_mask_0 = const()[name = tensor("op_1051_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1051_cast = slice_by_index(begin = var_1051_begin_0, end = var_1051_end_0, end_mask = var_1051_end_mask_0, x = v_7_cast)[name = tensor("op_1051_cast")]; + tensor var_1055_begin_0 = const()[name = tensor("op_1055_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_1055_end_0 = const()[name = tensor("op_1055_end_0"), val = tensor([2, 280, 1, 77])]; + tensor var_1055_end_mask_0 = const()[name = tensor("op_1055_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1055_cast = slice_by_index(begin = var_1055_begin_0, end = var_1055_end_0, end_mask = var_1055_end_mask_0, x = v_7_cast)[name = tensor("op_1055_cast")]; + tensor var_1059_begin_0 = const()[name = tensor("op_1059_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_1059_end_0 = const()[name = tensor("op_1059_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_1059_end_mask_0 = const()[name = tensor("op_1059_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1059_cast = slice_by_index(begin = var_1059_begin_0, end = var_1059_end_0, end_mask = var_1059_end_mask_0, x = v_7_cast)[name = tensor("op_1059_cast")]; + tensor var_1063_equation_0 = const()[name = tensor("op_1063_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1063_cast = einsum(equation = var_1063_equation_0, values = (var_1001_cast, var_966_cast))[name = tensor("op_1063_cast")]; + tensor var_1064_to_fp16 = const()[name = tensor("op_1064_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_49_cast = mul(x = var_1063_cast, y = var_1064_to_fp16)[name = tensor("aw_49_cast")]; + tensor var_1067_equation_0 = const()[name = tensor("op_1067_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1067_cast = einsum(equation = var_1067_equation_0, values = (var_1005_cast, var_970_cast))[name = tensor("op_1067_cast")]; + tensor var_1068_to_fp16 = const()[name = tensor("op_1068_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_51_cast = mul(x = var_1067_cast, y = var_1068_to_fp16)[name = tensor("aw_51_cast")]; + tensor var_1071_equation_0 = const()[name = tensor("op_1071_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1071_cast = einsum(equation = var_1071_equation_0, values = (var_1009_cast, var_974_cast))[name = tensor("op_1071_cast")]; + tensor var_1072_to_fp16 = const()[name = tensor("op_1072_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_53_cast = mul(x = var_1071_cast, y = var_1072_to_fp16)[name = tensor("aw_53_cast")]; + tensor var_1075_equation_0 = const()[name = tensor("op_1075_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1075_cast = einsum(equation = var_1075_equation_0, values = (var_1013_cast, var_978_cast))[name = tensor("op_1075_cast")]; + tensor var_1076_to_fp16 = const()[name = tensor("op_1076_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_55_cast = mul(x = var_1075_cast, y = var_1076_to_fp16)[name = tensor("aw_55_cast")]; + tensor var_1079_equation_0 = const()[name = tensor("op_1079_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1079_cast = einsum(equation = var_1079_equation_0, values = (var_1017_cast, var_982_cast))[name = tensor("op_1079_cast")]; + tensor var_1080_to_fp16 = const()[name = tensor("op_1080_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_57_cast = mul(x = var_1079_cast, y = var_1080_to_fp16)[name = tensor("aw_57_cast")]; + tensor var_1083_equation_0 = const()[name = tensor("op_1083_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1083_cast = einsum(equation = var_1083_equation_0, values = (var_1021_cast, var_986_cast))[name = tensor("op_1083_cast")]; + tensor var_1084_to_fp16 = const()[name = tensor("op_1084_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_59_cast = mul(x = var_1083_cast, y = var_1084_to_fp16)[name = tensor("aw_59_cast")]; + tensor var_1087_equation_0 = const()[name = tensor("op_1087_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1087_cast = einsum(equation = var_1087_equation_0, values = (var_1025_cast, var_990_cast))[name = tensor("op_1087_cast")]; + tensor var_1088_to_fp16 = const()[name = tensor("op_1088_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_61_cast = mul(x = var_1087_cast, y = var_1088_to_fp16)[name = tensor("aw_61_cast")]; + tensor var_1091_equation_0 = const()[name = tensor("op_1091_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1091_cast = einsum(equation = var_1091_equation_0, values = (var_1029_cast, var_994_cast))[name = tensor("op_1091_cast")]; + tensor var_1092_to_fp16 = const()[name = tensor("op_1092_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_63_cast = mul(x = var_1091_cast, y = var_1092_to_fp16)[name = tensor("aw_63_cast")]; + tensor var_1094_cast = softmax(axis = var_115, x = aw_49_cast)[name = tensor("op_1094_cast")]; + tensor var_1095_cast = softmax(axis = var_115, x = aw_51_cast)[name = tensor("op_1095_cast")]; + tensor var_1096_cast = softmax(axis = var_115, x = aw_53_cast)[name = tensor("op_1096_cast")]; + tensor var_1097_cast = softmax(axis = var_115, x = aw_55_cast)[name = tensor("op_1097_cast")]; + tensor var_1098_cast = softmax(axis = var_115, x = aw_57_cast)[name = tensor("op_1098_cast")]; + tensor var_1099_cast = softmax(axis = var_115, x = aw_59_cast)[name = tensor("op_1099_cast")]; + tensor var_1100_cast = softmax(axis = var_115, x = aw_61_cast)[name = tensor("op_1100_cast")]; + tensor var_1101_cast = softmax(axis = var_115, x = aw_63_cast)[name = tensor("op_1101_cast")]; + tensor var_1103_equation_0 = const()[name = tensor("op_1103_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1103_cast = einsum(equation = var_1103_equation_0, values = (var_1031_cast, var_1094_cast))[name = tensor("op_1103_cast")]; + tensor var_1105_equation_0 = const()[name = tensor("op_1105_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1105_cast = einsum(equation = var_1105_equation_0, values = (var_1035_cast, var_1095_cast))[name = tensor("op_1105_cast")]; + tensor var_1107_equation_0 = const()[name = tensor("op_1107_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1107_cast = einsum(equation = var_1107_equation_0, values = (var_1039_cast, var_1096_cast))[name = tensor("op_1107_cast")]; + tensor var_1109_equation_0 = const()[name = tensor("op_1109_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1109_cast = einsum(equation = var_1109_equation_0, values = (var_1043_cast, var_1097_cast))[name = tensor("op_1109_cast")]; + tensor var_1111_equation_0 = const()[name = tensor("op_1111_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1111_cast = einsum(equation = var_1111_equation_0, values = (var_1047_cast, var_1098_cast))[name = tensor("op_1111_cast")]; + tensor var_1113_equation_0 = const()[name = tensor("op_1113_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1113_cast = einsum(equation = var_1113_equation_0, values = (var_1051_cast, var_1099_cast))[name = tensor("op_1113_cast")]; + tensor var_1115_equation_0 = const()[name = tensor("op_1115_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1115_cast = einsum(equation = var_1115_equation_0, values = (var_1055_cast, var_1100_cast))[name = tensor("op_1115_cast")]; + tensor var_1117_equation_0 = const()[name = tensor("op_1117_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1117_cast = einsum(equation = var_1117_equation_0, values = (var_1059_cast, var_1101_cast))[name = tensor("op_1117_cast")]; + tensor input_53_interleave_0 = const()[name = tensor("input_53_interleave_0"), val = tensor(false)]; + tensor input_53_cast = concat(axis = var_115, interleave = input_53_interleave_0, values = (var_1103_cast, var_1105_cast, var_1107_cast, var_1109_cast, var_1111_cast, var_1113_cast, var_1115_cast, var_1117_cast))[name = tensor("input_53_cast")]; + tensor var_1123 = const()[name = tensor("op_1123"), val = tensor([1, 1])]; + tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, 1])]; + tensor var_1127_pad_type_0 = const()[name = tensor("op_1127_pad_type_0"), val = tensor("custom")]; + tensor var_1127_pad_0 = const()[name = tensor("op_1127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7699968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7776832))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7777024)))]; + tensor var_1127_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1125, groups = var_115, pad = var_1127_pad_0, pad_type = var_1127_pad_type_0, strides = var_1123, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_53_cast)[name = tensor("op_1127_cast")]; + tensor inputs_11_cast = add(x = var_1127_cast, y = inputs_9_cast)[name = tensor("inputs_11_cast")]; + tensor var_1131 = const()[name = tensor("op_1131"), val = tensor([1])]; + tensor channels_mean_11_cast = reduce_mean(axes = var_1131, keep_dims = var_110, x = inputs_11_cast)[name = tensor("channels_mean_11_cast")]; + tensor zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast)[name = tensor("zero_mean_11_cast")]; + tensor zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast)[name = tensor("zero_mean_sq_11_cast")]; + tensor var_1135 = const()[name = tensor("op_1135"), val = tensor([1])]; + tensor var_1136_cast = reduce_mean(axes = var_1135, keep_dims = var_110, x = zero_mean_sq_11_cast)[name = tensor("op_1136_cast")]; + tensor var_1137_to_fp16 = const()[name = tensor("op_1137_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1138_cast = add(x = var_1136_cast, y = var_1137_to_fp16)[name = tensor("op_1138_cast")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_1138_cast)[name = tensor("denom_11_cast")]; + tensor out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast)[name = tensor("out_11_cast")]; + tensor var_1142_to_fp16 = const()[name = tensor("op_1142_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7777728)))]; + tensor var_1143_cast = add(x = out_11_cast, y = var_1142_to_fp16)[name = tensor("op_1143_cast")]; + tensor var_1145_to_fp16 = const()[name = tensor("op_1145_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7778432)))]; + tensor input_55_cast = mul(x = var_1143_cast, y = var_1145_to_fp16)[name = tensor("input_55_cast")]; + tensor var_1153 = const()[name = tensor("op_1153"), val = tensor([1, 1])]; + tensor var_1155 = const()[name = tensor("op_1155"), val = tensor([1, 1])]; + tensor var_1157_pad_type_0 = const()[name = tensor("op_1157_pad_type_0"), val = tensor("custom")]; + tensor var_1157_pad_0 = const()[name = tensor("op_1157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7779136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8393600))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8393792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8395776))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_1157_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1155, groups = var_115, pad = var_1157_pad_0, pad_type = var_1157_pad_type_0, strides = var_1153, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_55_cast)[name = tensor("op_1157_cast")]; + tensor var_1158_split_sizes_0 = const()[name = tensor("op_1158_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_1158_axis_0 = const()[name = tensor("op_1158_axis_0"), val = tensor(1)]; + tensor var_1158_cast_0, tensor var_1158_cast_1 = split(axis = var_1158_axis_0, split_sizes = var_1158_split_sizes_0, x = var_1157_cast)[name = tensor("op_1158_cast")]; + tensor var_1160_mode_0 = const()[name = tensor("op_1160_mode_0"), val = tensor("EXACT")]; + tensor var_1160_cast = gelu(mode = var_1160_mode_0, x = var_1158_cast_1)[name = tensor("op_1160_cast")]; + tensor input_57_cast = mul(x = var_1158_cast_0, y = var_1160_cast)[name = tensor("input_57_cast")]; + tensor var_1164 = const()[name = tensor("op_1164"), val = tensor([1, 1])]; + tensor var_1166 = const()[name = tensor("op_1166"), val = tensor([1, 1])]; + tensor var_1168_pad_type_0 = const()[name = tensor("op_1168_pad_type_0"), val = tensor("custom")]; + tensor var_1168_pad_0 = const()[name = tensor("op_1168_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8395968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8703232))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8703424)))]; + tensor var_1168_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1166, groups = var_115, pad = var_1168_pad_0, pad_type = var_1168_pad_type_0, strides = var_1164, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_57_cast)[name = tensor("op_1168_cast")]; + tensor hidden_states_33_cast = add(x = var_1168_cast, y = inputs_11_cast)[name = tensor("hidden_states_33_cast")]; + tensor var_1170 = const()[name = tensor("op_1170"), val = tensor([2, 320, 96, 96])]; + tensor input_59_cast = reshape(shape = var_1170, x = hidden_states_33_cast)[name = tensor("input_59_cast")]; + tensor var_1174 = const()[name = tensor("op_1174"), val = tensor([1, 1])]; + tensor var_1176 = const()[name = tensor("op_1176"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8704128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8780992))), name = tensor("down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8781184)))]; + tensor hidden_states_35_cast = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_1176, groups = var_115, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_1174, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized, x = input_59_cast)[name = tensor("hidden_states_35_cast")]; + tensor input_61_cast = add(x = hidden_states_35_cast, y = hidden_states_23_cast)[name = tensor("input_61_cast")]; + tensor var_1183 = const()[name = tensor("op_1183"), val = tensor([2, 2])]; + tensor var_1185 = const()[name = tensor("op_1185"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8781888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9473152))), name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9473344)))]; + tensor input_63_cast = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_1185, groups = var_115, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_1183, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized, x = input_61_cast)[name = tensor("input_63_cast")]; + tensor var_1208 = const()[name = tensor("op_1208"), val = tensor(true)]; + tensor var_1213 = const()[name = tensor("op_1213"), val = tensor(1)]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 10, 48, 48])]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = input_63_cast)[name = tensor("reshape_24_cast")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_20_cast")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 320, 48, 48])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9474048)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9474752)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_13_cast")]; + tensor input_67_cast = silu(x = add_13_cast)[name = tensor("input_67_cast")]; + tensor var_1236 = const()[name = tensor("op_1236"), val = tensor([1, 1])]; + tensor var_1238 = const()[name = tensor("op_1238"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9475456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10857920))), name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 320, 3, 3])]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10858112)))]; + tensor hidden_states_37_cast = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_1238, groups = var_1213, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_1236, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_67_cast)[name = tensor("hidden_states_37_cast")]; + tensor var_1244 = const()[name = tensor("op_1244"), val = tensor([1, 1])]; + tensor var_1246 = const()[name = tensor("op_1246"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10859456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11473920))), name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11474112)))]; + tensor temb_5_cast = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1246, groups = var_1213, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_1244, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_5_cast")]; + tensor input_71_cast = add(x = hidden_states_37_cast, y = temb_5_cast)[name = tensor("input_71_cast")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_71_cast)[name = tensor("reshape_28_cast")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_23_cast")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_cast")]; + tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11475456)))]; + tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11476800)))]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11478144)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11479488)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_15_cast")]; + tensor input_75_cast = silu(x = add_15_cast)[name = tensor("input_75_cast")]; + tensor var_1256 = const()[name = tensor("op_1256"), val = tensor([1, 1])]; + tensor var_1258 = const()[name = tensor("op_1258"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11480832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14245696))), name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14245888)))]; + tensor hidden_states_39_cast = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_1258, groups = var_1213, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_1256, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_75_cast)[name = tensor("hidden_states_39_cast")]; + tensor var_1263 = const()[name = tensor("op_1263"), val = tensor([1, 1])]; + tensor var_1265 = const()[name = tensor("op_1265"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14247232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14400896))), name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 320, 1, 1])]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14401088)))]; + tensor x_1_cast = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1265, groups = var_1213, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_1263, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_63_cast)[name = tensor("x_1_cast")]; + tensor hidden_states_41_cast = add(x = x_1_cast, y = hidden_states_39_cast)[name = tensor("hidden_states_41_cast")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = hidden_states_41_cast)[name = tensor("reshape_32_cast")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_26_cast")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14402432)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14403776)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_17_cast")]; + tensor var_1285 = const()[name = tensor("op_1285"), val = tensor([1, 1])]; + tensor var_1287 = const()[name = tensor("op_1287"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14405120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14712384))), name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14712576)))]; + tensor hidden_states_43_cast = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_1287, groups = var_1213, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_1285, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_17_cast)[name = tensor("hidden_states_43_cast")]; + tensor var_1292 = const()[name = tensor("op_1292"), val = tensor([2, 640, 1, 2304])]; + tensor inputs_13_cast = reshape(shape = var_1292, x = hidden_states_43_cast)[name = tensor("inputs_13_cast")]; + tensor var_1302 = const()[name = tensor("op_1302"), val = tensor([1])]; + tensor channels_mean_13_cast = reduce_mean(axes = var_1302, keep_dims = var_1208, x = inputs_13_cast)[name = tensor("channels_mean_13_cast")]; + tensor zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast)[name = tensor("zero_mean_13_cast")]; + tensor zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast)[name = tensor("zero_mean_sq_13_cast")]; + tensor var_1306 = const()[name = tensor("op_1306"), val = tensor([1])]; + tensor var_1307_cast = reduce_mean(axes = var_1306, keep_dims = var_1208, x = zero_mean_sq_13_cast)[name = tensor("op_1307_cast")]; + tensor var_1308_to_fp16 = const()[name = tensor("op_1308_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1309_cast = add(x = var_1307_cast, y = var_1308_to_fp16)[name = tensor("op_1309_cast")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_1309_cast)[name = tensor("denom_13_cast")]; + tensor out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_cast)[name = tensor("out_13_cast")]; + tensor var_1313_to_fp16 = const()[name = tensor("op_1313_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14713920)))]; + tensor var_1314_cast = add(x = out_13_cast, y = var_1313_to_fp16)[name = tensor("op_1314_cast")]; + tensor var_1316_to_fp16 = const()[name = tensor("op_1316_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14715264)))]; + tensor hidden_states_45_cast = mul(x = var_1314_cast, y = var_1316_to_fp16)[name = tensor("hidden_states_45_cast")]; + tensor var_1323 = const()[name = tensor("op_1323"), val = tensor([1, 1])]; + tensor var_1325 = const()[name = tensor("op_1325"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14716608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15023872))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_9_cast = conv(dilations = var_1325, groups = var_1213, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_1323, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor("q_9_cast")]; + tensor var_1329 = const()[name = tensor("op_1329"), val = tensor([1, 1])]; + tensor var_1331 = const()[name = tensor("op_1331"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15024064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15331328))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_17_cast = conv(dilations = var_1331, groups = var_1213, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1329, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor("k_17_cast")]; + tensor var_1335 = const()[name = tensor("op_1335"), val = tensor([1, 1])]; + tensor var_1337 = const()[name = tensor("op_1337"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15331520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15638784))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_9_cast = conv(dilations = var_1337, groups = var_1213, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_1335, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor("v_9_cast")]; + tensor var_1341_begin_0 = const()[name = tensor("op_1341_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1341_end_0 = const()[name = tensor("op_1341_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_1341_end_mask_0 = const()[name = tensor("op_1341_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1341_cast = slice_by_index(begin = var_1341_begin_0, end = var_1341_end_0, end_mask = var_1341_end_mask_0, x = q_9_cast)[name = tensor("op_1341_cast")]; + tensor var_1345_begin_0 = const()[name = tensor("op_1345_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_1345_end_0 = const()[name = tensor("op_1345_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_1345_end_mask_0 = const()[name = tensor("op_1345_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1345_cast = slice_by_index(begin = var_1345_begin_0, end = var_1345_end_0, end_mask = var_1345_end_mask_0, x = q_9_cast)[name = tensor("op_1345_cast")]; + tensor var_1349_begin_0 = const()[name = tensor("op_1349_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_1349_end_0 = const()[name = tensor("op_1349_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_1349_end_mask_0 = const()[name = tensor("op_1349_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1349_cast = slice_by_index(begin = var_1349_begin_0, end = var_1349_end_0, end_mask = var_1349_end_mask_0, x = q_9_cast)[name = tensor("op_1349_cast")]; + tensor var_1353_begin_0 = const()[name = tensor("op_1353_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_1353_end_0 = const()[name = tensor("op_1353_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_1353_end_mask_0 = const()[name = tensor("op_1353_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1353_cast = slice_by_index(begin = var_1353_begin_0, end = var_1353_end_0, end_mask = var_1353_end_mask_0, x = q_9_cast)[name = tensor("op_1353_cast")]; + tensor var_1357_begin_0 = const()[name = tensor("op_1357_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_1357_end_0 = const()[name = tensor("op_1357_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_1357_end_mask_0 = const()[name = tensor("op_1357_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1357_cast = slice_by_index(begin = var_1357_begin_0, end = var_1357_end_0, end_mask = var_1357_end_mask_0, x = q_9_cast)[name = tensor("op_1357_cast")]; + tensor var_1361_begin_0 = const()[name = tensor("op_1361_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_1361_end_0 = const()[name = tensor("op_1361_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_1361_end_mask_0 = const()[name = tensor("op_1361_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1361_cast = slice_by_index(begin = var_1361_begin_0, end = var_1361_end_0, end_mask = var_1361_end_mask_0, x = q_9_cast)[name = tensor("op_1361_cast")]; + tensor var_1365_begin_0 = const()[name = tensor("op_1365_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_1365_end_0 = const()[name = tensor("op_1365_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_1365_end_mask_0 = const()[name = tensor("op_1365_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1365_cast = slice_by_index(begin = var_1365_begin_0, end = var_1365_end_0, end_mask = var_1365_end_mask_0, x = q_9_cast)[name = tensor("op_1365_cast")]; + tensor var_1369_begin_0 = const()[name = tensor("op_1369_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_1369_end_0 = const()[name = tensor("op_1369_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_1369_end_mask_0 = const()[name = tensor("op_1369_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1369_cast = slice_by_index(begin = var_1369_begin_0, end = var_1369_end_0, end_mask = var_1369_end_mask_0, x = q_9_cast)[name = tensor("op_1369_cast")]; + tensor k_19_perm_0 = const()[name = tensor("k_19_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_1376_begin_0 = const()[name = tensor("op_1376_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1376_end_0 = const()[name = tensor("op_1376_end_0"), val = tensor([2, 2304, 1, 80])]; + tensor var_1376_end_mask_0 = const()[name = tensor("op_1376_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_27 = transpose(perm = k_19_perm_0, x = k_17_cast)[name = tensor("transpose_27")]; + tensor var_1376_cast = slice_by_index(begin = var_1376_begin_0, end = var_1376_end_0, end_mask = var_1376_end_mask_0, x = transpose_27)[name = tensor("op_1376_cast")]; + tensor var_1380_begin_0 = const()[name = tensor("op_1380_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_1380_end_0 = const()[name = tensor("op_1380_end_0"), val = tensor([2, 2304, 1, 160])]; + tensor var_1380_end_mask_0 = const()[name = tensor("op_1380_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1380_cast = slice_by_index(begin = var_1380_begin_0, end = var_1380_end_0, end_mask = var_1380_end_mask_0, x = transpose_27)[name = tensor("op_1380_cast")]; + tensor var_1384_begin_0 = const()[name = tensor("op_1384_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_1384_end_0 = const()[name = tensor("op_1384_end_0"), val = tensor([2, 2304, 1, 240])]; + tensor var_1384_end_mask_0 = const()[name = tensor("op_1384_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1384_cast = slice_by_index(begin = var_1384_begin_0, end = var_1384_end_0, end_mask = var_1384_end_mask_0, x = transpose_27)[name = tensor("op_1384_cast")]; + tensor var_1388_begin_0 = const()[name = tensor("op_1388_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_1388_end_0 = const()[name = tensor("op_1388_end_0"), val = tensor([2, 2304, 1, 320])]; + tensor var_1388_end_mask_0 = const()[name = tensor("op_1388_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1388_cast = slice_by_index(begin = var_1388_begin_0, end = var_1388_end_0, end_mask = var_1388_end_mask_0, x = transpose_27)[name = tensor("op_1388_cast")]; + tensor var_1392_begin_0 = const()[name = tensor("op_1392_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_1392_end_0 = const()[name = tensor("op_1392_end_0"), val = tensor([2, 2304, 1, 400])]; + tensor var_1392_end_mask_0 = const()[name = tensor("op_1392_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1392_cast = slice_by_index(begin = var_1392_begin_0, end = var_1392_end_0, end_mask = var_1392_end_mask_0, x = transpose_27)[name = tensor("op_1392_cast")]; + tensor var_1396_begin_0 = const()[name = tensor("op_1396_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_1396_end_0 = const()[name = tensor("op_1396_end_0"), val = tensor([2, 2304, 1, 480])]; + tensor var_1396_end_mask_0 = const()[name = tensor("op_1396_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1396_cast = slice_by_index(begin = var_1396_begin_0, end = var_1396_end_0, end_mask = var_1396_end_mask_0, x = transpose_27)[name = tensor("op_1396_cast")]; + tensor var_1400_begin_0 = const()[name = tensor("op_1400_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_1400_end_0 = const()[name = tensor("op_1400_end_0"), val = tensor([2, 2304, 1, 560])]; + tensor var_1400_end_mask_0 = const()[name = tensor("op_1400_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1400_cast = slice_by_index(begin = var_1400_begin_0, end = var_1400_end_0, end_mask = var_1400_end_mask_0, x = transpose_27)[name = tensor("op_1400_cast")]; + tensor var_1404_begin_0 = const()[name = tensor("op_1404_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_1404_end_0 = const()[name = tensor("op_1404_end_0"), val = tensor([2, 2304, 1, 640])]; + tensor var_1404_end_mask_0 = const()[name = tensor("op_1404_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1404_cast = slice_by_index(begin = var_1404_begin_0, end = var_1404_end_0, end_mask = var_1404_end_mask_0, x = transpose_27)[name = tensor("op_1404_cast")]; + tensor var_1406_begin_0 = const()[name = tensor("op_1406_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1406_end_0 = const()[name = tensor("op_1406_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_1406_end_mask_0 = const()[name = tensor("op_1406_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1406_cast = slice_by_index(begin = var_1406_begin_0, end = var_1406_end_0, end_mask = var_1406_end_mask_0, x = v_9_cast)[name = tensor("op_1406_cast")]; + tensor var_1410_begin_0 = const()[name = tensor("op_1410_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_1410_end_0 = const()[name = tensor("op_1410_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_1410_end_mask_0 = const()[name = tensor("op_1410_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1410_cast = slice_by_index(begin = var_1410_begin_0, end = var_1410_end_0, end_mask = var_1410_end_mask_0, x = v_9_cast)[name = tensor("op_1410_cast")]; + tensor var_1414_begin_0 = const()[name = tensor("op_1414_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_1414_end_0 = const()[name = tensor("op_1414_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_1414_end_mask_0 = const()[name = tensor("op_1414_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1414_cast = slice_by_index(begin = var_1414_begin_0, end = var_1414_end_0, end_mask = var_1414_end_mask_0, x = v_9_cast)[name = tensor("op_1414_cast")]; + tensor var_1418_begin_0 = const()[name = tensor("op_1418_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_1418_end_0 = const()[name = tensor("op_1418_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_1418_end_mask_0 = const()[name = tensor("op_1418_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1418_cast = slice_by_index(begin = var_1418_begin_0, end = var_1418_end_0, end_mask = var_1418_end_mask_0, x = v_9_cast)[name = tensor("op_1418_cast")]; + tensor var_1422_begin_0 = const()[name = tensor("op_1422_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_1422_end_0 = const()[name = tensor("op_1422_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_1422_end_mask_0 = const()[name = tensor("op_1422_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1422_cast = slice_by_index(begin = var_1422_begin_0, end = var_1422_end_0, end_mask = var_1422_end_mask_0, x = v_9_cast)[name = tensor("op_1422_cast")]; + tensor var_1426_begin_0 = const()[name = tensor("op_1426_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_1426_end_0 = const()[name = tensor("op_1426_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_1426_end_mask_0 = const()[name = tensor("op_1426_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1426_cast = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = v_9_cast)[name = tensor("op_1426_cast")]; + tensor var_1430_begin_0 = const()[name = tensor("op_1430_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_1430_end_0 = const()[name = tensor("op_1430_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_1430_end_mask_0 = const()[name = tensor("op_1430_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1430_cast = slice_by_index(begin = var_1430_begin_0, end = var_1430_end_0, end_mask = var_1430_end_mask_0, x = v_9_cast)[name = tensor("op_1430_cast")]; + tensor var_1434_begin_0 = const()[name = tensor("op_1434_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_1434_end_0 = const()[name = tensor("op_1434_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_1434_end_mask_0 = const()[name = tensor("op_1434_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1434_cast = slice_by_index(begin = var_1434_begin_0, end = var_1434_end_0, end_mask = var_1434_end_mask_0, x = v_9_cast)[name = tensor("op_1434_cast")]; + tensor var_1438_equation_0 = const()[name = tensor("op_1438_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1438_cast = einsum(equation = var_1438_equation_0, values = (var_1376_cast, var_1341_cast))[name = tensor("op_1438_cast")]; + tensor var_1439_to_fp16 = const()[name = tensor("op_1439_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_65_cast = mul(x = var_1438_cast, y = var_1439_to_fp16)[name = tensor("aw_65_cast")]; + tensor var_1442_equation_0 = const()[name = tensor("op_1442_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1442_cast = einsum(equation = var_1442_equation_0, values = (var_1380_cast, var_1345_cast))[name = tensor("op_1442_cast")]; + tensor var_1443_to_fp16 = const()[name = tensor("op_1443_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_67_cast = mul(x = var_1442_cast, y = var_1443_to_fp16)[name = tensor("aw_67_cast")]; + tensor var_1446_equation_0 = const()[name = tensor("op_1446_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1446_cast = einsum(equation = var_1446_equation_0, values = (var_1384_cast, var_1349_cast))[name = tensor("op_1446_cast")]; + tensor var_1447_to_fp16 = const()[name = tensor("op_1447_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_69_cast = mul(x = var_1446_cast, y = var_1447_to_fp16)[name = tensor("aw_69_cast")]; + tensor var_1450_equation_0 = const()[name = tensor("op_1450_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1450_cast = einsum(equation = var_1450_equation_0, values = (var_1388_cast, var_1353_cast))[name = tensor("op_1450_cast")]; + tensor var_1451_to_fp16 = const()[name = tensor("op_1451_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_71_cast = mul(x = var_1450_cast, y = var_1451_to_fp16)[name = tensor("aw_71_cast")]; + tensor var_1454_equation_0 = const()[name = tensor("op_1454_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1454_cast = einsum(equation = var_1454_equation_0, values = (var_1392_cast, var_1357_cast))[name = tensor("op_1454_cast")]; + tensor var_1455_to_fp16 = const()[name = tensor("op_1455_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_73_cast = mul(x = var_1454_cast, y = var_1455_to_fp16)[name = tensor("aw_73_cast")]; + tensor var_1458_equation_0 = const()[name = tensor("op_1458_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1458_cast = einsum(equation = var_1458_equation_0, values = (var_1396_cast, var_1361_cast))[name = tensor("op_1458_cast")]; + tensor var_1459_to_fp16 = const()[name = tensor("op_1459_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_75_cast = mul(x = var_1458_cast, y = var_1459_to_fp16)[name = tensor("aw_75_cast")]; + tensor var_1462_equation_0 = const()[name = tensor("op_1462_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1462_cast = einsum(equation = var_1462_equation_0, values = (var_1400_cast, var_1365_cast))[name = tensor("op_1462_cast")]; + tensor var_1463_to_fp16 = const()[name = tensor("op_1463_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_77_cast = mul(x = var_1462_cast, y = var_1463_to_fp16)[name = tensor("aw_77_cast")]; + tensor var_1466_equation_0 = const()[name = tensor("op_1466_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1466_cast = einsum(equation = var_1466_equation_0, values = (var_1404_cast, var_1369_cast))[name = tensor("op_1466_cast")]; + tensor var_1467_to_fp16 = const()[name = tensor("op_1467_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_79_cast = mul(x = var_1466_cast, y = var_1467_to_fp16)[name = tensor("aw_79_cast")]; + tensor var_1469_cast = softmax(axis = var_1213, x = aw_65_cast)[name = tensor("op_1469_cast")]; + tensor var_1470_cast = softmax(axis = var_1213, x = aw_67_cast)[name = tensor("op_1470_cast")]; + tensor var_1471_cast = softmax(axis = var_1213, x = aw_69_cast)[name = tensor("op_1471_cast")]; + tensor var_1472_cast = softmax(axis = var_1213, x = aw_71_cast)[name = tensor("op_1472_cast")]; + tensor var_1473_cast = softmax(axis = var_1213, x = aw_73_cast)[name = tensor("op_1473_cast")]; + tensor var_1474_cast = softmax(axis = var_1213, x = aw_75_cast)[name = tensor("op_1474_cast")]; + tensor var_1475_cast = softmax(axis = var_1213, x = aw_77_cast)[name = tensor("op_1475_cast")]; + tensor var_1476_cast = softmax(axis = var_1213, x = aw_79_cast)[name = tensor("op_1476_cast")]; + tensor var_1478_equation_0 = const()[name = tensor("op_1478_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1478_cast = einsum(equation = var_1478_equation_0, values = (var_1406_cast, var_1469_cast))[name = tensor("op_1478_cast")]; + tensor var_1480_equation_0 = const()[name = tensor("op_1480_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1480_cast = einsum(equation = var_1480_equation_0, values = (var_1410_cast, var_1470_cast))[name = tensor("op_1480_cast")]; + tensor var_1482_equation_0 = const()[name = tensor("op_1482_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1482_cast = einsum(equation = var_1482_equation_0, values = (var_1414_cast, var_1471_cast))[name = tensor("op_1482_cast")]; + tensor var_1484_equation_0 = const()[name = tensor("op_1484_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1484_cast = einsum(equation = var_1484_equation_0, values = (var_1418_cast, var_1472_cast))[name = tensor("op_1484_cast")]; + tensor var_1486_equation_0 = const()[name = tensor("op_1486_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1486_cast = einsum(equation = var_1486_equation_0, values = (var_1422_cast, var_1473_cast))[name = tensor("op_1486_cast")]; + tensor var_1488_equation_0 = const()[name = tensor("op_1488_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1488_cast = einsum(equation = var_1488_equation_0, values = (var_1426_cast, var_1474_cast))[name = tensor("op_1488_cast")]; + tensor var_1490_equation_0 = const()[name = tensor("op_1490_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1490_cast = einsum(equation = var_1490_equation_0, values = (var_1430_cast, var_1475_cast))[name = tensor("op_1490_cast")]; + tensor var_1492_equation_0 = const()[name = tensor("op_1492_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1492_cast = einsum(equation = var_1492_equation_0, values = (var_1434_cast, var_1476_cast))[name = tensor("op_1492_cast")]; + tensor input_79_interleave_0 = const()[name = tensor("input_79_interleave_0"), val = tensor(false)]; + tensor input_79_cast = concat(axis = var_1213, interleave = input_79_interleave_0, values = (var_1478_cast, var_1480_cast, var_1482_cast, var_1484_cast, var_1486_cast, var_1488_cast, var_1490_cast, var_1492_cast))[name = tensor("input_79_cast")]; + tensor var_1498 = const()[name = tensor("op_1498"), val = tensor([1, 1])]; + tensor var_1500 = const()[name = tensor("op_1500"), val = tensor([1, 1])]; + tensor var_1502_pad_type_0 = const()[name = tensor("op_1502_pad_type_0"), val = tensor("custom")]; + tensor var_1502_pad_0 = const()[name = tensor("op_1502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15638976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15946240))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15946432)))]; + tensor var_1502_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1500, groups = var_1213, pad = var_1502_pad_0, pad_type = var_1502_pad_type_0, strides = var_1498, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_79_cast)[name = tensor("op_1502_cast")]; + tensor inputs_15_cast = add(x = var_1502_cast, y = inputs_13_cast)[name = tensor("inputs_15_cast")]; + tensor var_1506 = const()[name = tensor("op_1506"), val = tensor([1])]; + tensor channels_mean_15_cast = reduce_mean(axes = var_1506, keep_dims = var_1208, x = inputs_15_cast)[name = tensor("channels_mean_15_cast")]; + tensor zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast)[name = tensor("zero_mean_15_cast")]; + tensor zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast)[name = tensor("zero_mean_sq_15_cast")]; + tensor var_1510 = const()[name = tensor("op_1510"), val = tensor([1])]; + tensor var_1511_cast = reduce_mean(axes = var_1510, keep_dims = var_1208, x = zero_mean_sq_15_cast)[name = tensor("op_1511_cast")]; + tensor var_1512_to_fp16 = const()[name = tensor("op_1512_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1513_cast = add(x = var_1511_cast, y = var_1512_to_fp16)[name = tensor("op_1513_cast")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_1513_cast)[name = tensor("denom_15_cast")]; + tensor out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_cast)[name = tensor("out_15_cast")]; + tensor var_1517_to_fp16 = const()[name = tensor("op_1517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15947776)))]; + tensor var_1518_cast = add(x = out_15_cast, y = var_1517_to_fp16)[name = tensor("op_1518_cast")]; + tensor var_1520_to_fp16 = const()[name = tensor("op_1520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15949120)))]; + tensor hidden_states_47_cast = mul(x = var_1518_cast, y = var_1520_to_fp16)[name = tensor("hidden_states_47_cast")]; + tensor var_1527 = const()[name = tensor("op_1527"), val = tensor([1, 1])]; + tensor var_1529 = const()[name = tensor("op_1529"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15950464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16257728))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_11_cast = conv(dilations = var_1529, groups = var_1213, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_1527, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_47_cast)[name = tensor("q_11_cast")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1, 1])]; + tensor var_1535 = const()[name = tensor("op_1535"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16257920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16626624))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_21_cast = conv(dilations = var_1535, groups = var_1213, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1533, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_21_cast")]; + tensor var_1539 = const()[name = tensor("op_1539"), val = tensor([1, 1])]; + tensor var_1541 = const()[name = tensor("op_1541"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16626816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16995520))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_11_cast = conv(dilations = var_1541, groups = var_1213, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_1539, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_11_cast")]; + tensor var_1545_begin_0 = const()[name = tensor("op_1545_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1545_end_0 = const()[name = tensor("op_1545_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_1545_end_mask_0 = const()[name = tensor("op_1545_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1545_cast = slice_by_index(begin = var_1545_begin_0, end = var_1545_end_0, end_mask = var_1545_end_mask_0, x = q_11_cast)[name = tensor("op_1545_cast")]; + tensor var_1549_begin_0 = const()[name = tensor("op_1549_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_1549_end_0 = const()[name = tensor("op_1549_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_1549_end_mask_0 = const()[name = tensor("op_1549_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1549_cast = slice_by_index(begin = var_1549_begin_0, end = var_1549_end_0, end_mask = var_1549_end_mask_0, x = q_11_cast)[name = tensor("op_1549_cast")]; + tensor var_1553_begin_0 = const()[name = tensor("op_1553_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_1553_end_0 = const()[name = tensor("op_1553_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_1553_end_mask_0 = const()[name = tensor("op_1553_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1553_cast = slice_by_index(begin = var_1553_begin_0, end = var_1553_end_0, end_mask = var_1553_end_mask_0, x = q_11_cast)[name = tensor("op_1553_cast")]; + tensor var_1557_begin_0 = const()[name = tensor("op_1557_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_1557_end_0 = const()[name = tensor("op_1557_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_1557_end_mask_0 = const()[name = tensor("op_1557_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1557_cast = slice_by_index(begin = var_1557_begin_0, end = var_1557_end_0, end_mask = var_1557_end_mask_0, x = q_11_cast)[name = tensor("op_1557_cast")]; + tensor var_1561_begin_0 = const()[name = tensor("op_1561_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_1561_end_0 = const()[name = tensor("op_1561_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_1561_end_mask_0 = const()[name = tensor("op_1561_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1561_cast = slice_by_index(begin = var_1561_begin_0, end = var_1561_end_0, end_mask = var_1561_end_mask_0, x = q_11_cast)[name = tensor("op_1561_cast")]; + tensor var_1565_begin_0 = const()[name = tensor("op_1565_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_1565_end_0 = const()[name = tensor("op_1565_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_1565_end_mask_0 = const()[name = tensor("op_1565_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1565_cast = slice_by_index(begin = var_1565_begin_0, end = var_1565_end_0, end_mask = var_1565_end_mask_0, x = q_11_cast)[name = tensor("op_1565_cast")]; + tensor var_1569_begin_0 = const()[name = tensor("op_1569_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_1569_end_0 = const()[name = tensor("op_1569_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_1569_end_mask_0 = const()[name = tensor("op_1569_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1569_cast = slice_by_index(begin = var_1569_begin_0, end = var_1569_end_0, end_mask = var_1569_end_mask_0, x = q_11_cast)[name = tensor("op_1569_cast")]; + tensor var_1573_begin_0 = const()[name = tensor("op_1573_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_1573_end_0 = const()[name = tensor("op_1573_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_1573_end_mask_0 = const()[name = tensor("op_1573_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1573_cast = slice_by_index(begin = var_1573_begin_0, end = var_1573_end_0, end_mask = var_1573_end_mask_0, x = q_11_cast)[name = tensor("op_1573_cast")]; + tensor k_23_perm_0 = const()[name = tensor("k_23_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_1580_begin_0 = const()[name = tensor("op_1580_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1580_end_0 = const()[name = tensor("op_1580_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_1580_end_mask_0 = const()[name = tensor("op_1580_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_26 = transpose(perm = k_23_perm_0, x = k_21_cast)[name = tensor("transpose_26")]; + tensor var_1580_cast = slice_by_index(begin = var_1580_begin_0, end = var_1580_end_0, end_mask = var_1580_end_mask_0, x = transpose_26)[name = tensor("op_1580_cast")]; + tensor var_1584_begin_0 = const()[name = tensor("op_1584_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_1584_end_0 = const()[name = tensor("op_1584_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_1584_end_mask_0 = const()[name = tensor("op_1584_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1584_cast = slice_by_index(begin = var_1584_begin_0, end = var_1584_end_0, end_mask = var_1584_end_mask_0, x = transpose_26)[name = tensor("op_1584_cast")]; + tensor var_1588_begin_0 = const()[name = tensor("op_1588_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_1588_end_0 = const()[name = tensor("op_1588_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_1588_end_mask_0 = const()[name = tensor("op_1588_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1588_cast = slice_by_index(begin = var_1588_begin_0, end = var_1588_end_0, end_mask = var_1588_end_mask_0, x = transpose_26)[name = tensor("op_1588_cast")]; + tensor var_1592_begin_0 = const()[name = tensor("op_1592_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_1592_end_0 = const()[name = tensor("op_1592_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_1592_end_mask_0 = const()[name = tensor("op_1592_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1592_cast = slice_by_index(begin = var_1592_begin_0, end = var_1592_end_0, end_mask = var_1592_end_mask_0, x = transpose_26)[name = tensor("op_1592_cast")]; + tensor var_1596_begin_0 = const()[name = tensor("op_1596_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_1596_end_0 = const()[name = tensor("op_1596_end_0"), val = tensor([2, 77, 1, 400])]; + tensor var_1596_end_mask_0 = const()[name = tensor("op_1596_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1596_cast = slice_by_index(begin = var_1596_begin_0, end = var_1596_end_0, end_mask = var_1596_end_mask_0, x = transpose_26)[name = tensor("op_1596_cast")]; + tensor var_1600_begin_0 = const()[name = tensor("op_1600_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_1600_end_0 = const()[name = tensor("op_1600_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_1600_end_mask_0 = const()[name = tensor("op_1600_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1600_cast = slice_by_index(begin = var_1600_begin_0, end = var_1600_end_0, end_mask = var_1600_end_mask_0, x = transpose_26)[name = tensor("op_1600_cast")]; + tensor var_1604_begin_0 = const()[name = tensor("op_1604_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_1604_end_0 = const()[name = tensor("op_1604_end_0"), val = tensor([2, 77, 1, 560])]; + tensor var_1604_end_mask_0 = const()[name = tensor("op_1604_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1604_cast = slice_by_index(begin = var_1604_begin_0, end = var_1604_end_0, end_mask = var_1604_end_mask_0, x = transpose_26)[name = tensor("op_1604_cast")]; + tensor var_1608_begin_0 = const()[name = tensor("op_1608_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_1608_end_0 = const()[name = tensor("op_1608_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_1608_end_mask_0 = const()[name = tensor("op_1608_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1608_cast = slice_by_index(begin = var_1608_begin_0, end = var_1608_end_0, end_mask = var_1608_end_mask_0, x = transpose_26)[name = tensor("op_1608_cast")]; + tensor var_1610_begin_0 = const()[name = tensor("op_1610_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1610_end_0 = const()[name = tensor("op_1610_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_1610_end_mask_0 = const()[name = tensor("op_1610_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1610_cast = slice_by_index(begin = var_1610_begin_0, end = var_1610_end_0, end_mask = var_1610_end_mask_0, x = v_11_cast)[name = tensor("op_1610_cast")]; + tensor var_1614_begin_0 = const()[name = tensor("op_1614_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_1614_end_0 = const()[name = tensor("op_1614_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_1614_end_mask_0 = const()[name = tensor("op_1614_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1614_cast = slice_by_index(begin = var_1614_begin_0, end = var_1614_end_0, end_mask = var_1614_end_mask_0, x = v_11_cast)[name = tensor("op_1614_cast")]; + tensor var_1618_begin_0 = const()[name = tensor("op_1618_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_1618_end_0 = const()[name = tensor("op_1618_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_1618_end_mask_0 = const()[name = tensor("op_1618_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1618_cast = slice_by_index(begin = var_1618_begin_0, end = var_1618_end_0, end_mask = var_1618_end_mask_0, x = v_11_cast)[name = tensor("op_1618_cast")]; + tensor var_1622_begin_0 = const()[name = tensor("op_1622_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_1622_end_0 = const()[name = tensor("op_1622_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_1622_end_mask_0 = const()[name = tensor("op_1622_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1622_cast = slice_by_index(begin = var_1622_begin_0, end = var_1622_end_0, end_mask = var_1622_end_mask_0, x = v_11_cast)[name = tensor("op_1622_cast")]; + tensor var_1626_begin_0 = const()[name = tensor("op_1626_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_1626_end_0 = const()[name = tensor("op_1626_end_0"), val = tensor([2, 400, 1, 77])]; + tensor var_1626_end_mask_0 = const()[name = tensor("op_1626_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1626_cast = slice_by_index(begin = var_1626_begin_0, end = var_1626_end_0, end_mask = var_1626_end_mask_0, x = v_11_cast)[name = tensor("op_1626_cast")]; + tensor var_1630_begin_0 = const()[name = tensor("op_1630_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_1630_end_0 = const()[name = tensor("op_1630_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_1630_end_mask_0 = const()[name = tensor("op_1630_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1630_cast = slice_by_index(begin = var_1630_begin_0, end = var_1630_end_0, end_mask = var_1630_end_mask_0, x = v_11_cast)[name = tensor("op_1630_cast")]; + tensor var_1634_begin_0 = const()[name = tensor("op_1634_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_1634_end_0 = const()[name = tensor("op_1634_end_0"), val = tensor([2, 560, 1, 77])]; + tensor var_1634_end_mask_0 = const()[name = tensor("op_1634_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1634_cast = slice_by_index(begin = var_1634_begin_0, end = var_1634_end_0, end_mask = var_1634_end_mask_0, x = v_11_cast)[name = tensor("op_1634_cast")]; + tensor var_1638_begin_0 = const()[name = tensor("op_1638_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_1638_end_0 = const()[name = tensor("op_1638_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_1638_end_mask_0 = const()[name = tensor("op_1638_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1638_cast = slice_by_index(begin = var_1638_begin_0, end = var_1638_end_0, end_mask = var_1638_end_mask_0, x = v_11_cast)[name = tensor("op_1638_cast")]; + tensor var_1642_equation_0 = const()[name = tensor("op_1642_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1642_cast = einsum(equation = var_1642_equation_0, values = (var_1580_cast, var_1545_cast))[name = tensor("op_1642_cast")]; + tensor var_1643_to_fp16 = const()[name = tensor("op_1643_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_81_cast = mul(x = var_1642_cast, y = var_1643_to_fp16)[name = tensor("aw_81_cast")]; + tensor var_1646_equation_0 = const()[name = tensor("op_1646_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1646_cast = einsum(equation = var_1646_equation_0, values = (var_1584_cast, var_1549_cast))[name = tensor("op_1646_cast")]; + tensor var_1647_to_fp16 = const()[name = tensor("op_1647_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_83_cast = mul(x = var_1646_cast, y = var_1647_to_fp16)[name = tensor("aw_83_cast")]; + tensor var_1650_equation_0 = const()[name = tensor("op_1650_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1650_cast = einsum(equation = var_1650_equation_0, values = (var_1588_cast, var_1553_cast))[name = tensor("op_1650_cast")]; + tensor var_1651_to_fp16 = const()[name = tensor("op_1651_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_85_cast = mul(x = var_1650_cast, y = var_1651_to_fp16)[name = tensor("aw_85_cast")]; + tensor var_1654_equation_0 = const()[name = tensor("op_1654_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1654_cast = einsum(equation = var_1654_equation_0, values = (var_1592_cast, var_1557_cast))[name = tensor("op_1654_cast")]; + tensor var_1655_to_fp16 = const()[name = tensor("op_1655_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_87_cast = mul(x = var_1654_cast, y = var_1655_to_fp16)[name = tensor("aw_87_cast")]; + tensor var_1658_equation_0 = const()[name = tensor("op_1658_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1658_cast = einsum(equation = var_1658_equation_0, values = (var_1596_cast, var_1561_cast))[name = tensor("op_1658_cast")]; + tensor var_1659_to_fp16 = const()[name = tensor("op_1659_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_89_cast = mul(x = var_1658_cast, y = var_1659_to_fp16)[name = tensor("aw_89_cast")]; + tensor var_1662_equation_0 = const()[name = tensor("op_1662_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1662_cast = einsum(equation = var_1662_equation_0, values = (var_1600_cast, var_1565_cast))[name = tensor("op_1662_cast")]; + tensor var_1663_to_fp16 = const()[name = tensor("op_1663_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_91_cast = mul(x = var_1662_cast, y = var_1663_to_fp16)[name = tensor("aw_91_cast")]; + tensor var_1666_equation_0 = const()[name = tensor("op_1666_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1666_cast = einsum(equation = var_1666_equation_0, values = (var_1604_cast, var_1569_cast))[name = tensor("op_1666_cast")]; + tensor var_1667_to_fp16 = const()[name = tensor("op_1667_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_93_cast = mul(x = var_1666_cast, y = var_1667_to_fp16)[name = tensor("aw_93_cast")]; + tensor var_1670_equation_0 = const()[name = tensor("op_1670_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1670_cast = einsum(equation = var_1670_equation_0, values = (var_1608_cast, var_1573_cast))[name = tensor("op_1670_cast")]; + tensor var_1671_to_fp16 = const()[name = tensor("op_1671_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_95_cast = mul(x = var_1670_cast, y = var_1671_to_fp16)[name = tensor("aw_95_cast")]; + tensor var_1673_cast = softmax(axis = var_1213, x = aw_81_cast)[name = tensor("op_1673_cast")]; + tensor var_1674_cast = softmax(axis = var_1213, x = aw_83_cast)[name = tensor("op_1674_cast")]; + tensor var_1675_cast = softmax(axis = var_1213, x = aw_85_cast)[name = tensor("op_1675_cast")]; + tensor var_1676_cast = softmax(axis = var_1213, x = aw_87_cast)[name = tensor("op_1676_cast")]; + tensor var_1677_cast = softmax(axis = var_1213, x = aw_89_cast)[name = tensor("op_1677_cast")]; + tensor var_1678_cast = softmax(axis = var_1213, x = aw_91_cast)[name = tensor("op_1678_cast")]; + tensor var_1679_cast = softmax(axis = var_1213, x = aw_93_cast)[name = tensor("op_1679_cast")]; + tensor var_1680_cast = softmax(axis = var_1213, x = aw_95_cast)[name = tensor("op_1680_cast")]; + tensor var_1682_equation_0 = const()[name = tensor("op_1682_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1682_cast = einsum(equation = var_1682_equation_0, values = (var_1610_cast, var_1673_cast))[name = tensor("op_1682_cast")]; + tensor var_1684_equation_0 = const()[name = tensor("op_1684_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1684_cast = einsum(equation = var_1684_equation_0, values = (var_1614_cast, var_1674_cast))[name = tensor("op_1684_cast")]; + tensor var_1686_equation_0 = const()[name = tensor("op_1686_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1686_cast = einsum(equation = var_1686_equation_0, values = (var_1618_cast, var_1675_cast))[name = tensor("op_1686_cast")]; + tensor var_1688_equation_0 = const()[name = tensor("op_1688_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1688_cast = einsum(equation = var_1688_equation_0, values = (var_1622_cast, var_1676_cast))[name = tensor("op_1688_cast")]; + tensor var_1690_equation_0 = const()[name = tensor("op_1690_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1690_cast = einsum(equation = var_1690_equation_0, values = (var_1626_cast, var_1677_cast))[name = tensor("op_1690_cast")]; + tensor var_1692_equation_0 = const()[name = tensor("op_1692_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1692_cast = einsum(equation = var_1692_equation_0, values = (var_1630_cast, var_1678_cast))[name = tensor("op_1692_cast")]; + tensor var_1694_equation_0 = const()[name = tensor("op_1694_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1694_cast = einsum(equation = var_1694_equation_0, values = (var_1634_cast, var_1679_cast))[name = tensor("op_1694_cast")]; + tensor var_1696_equation_0 = const()[name = tensor("op_1696_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_1696_cast = einsum(equation = var_1696_equation_0, values = (var_1638_cast, var_1680_cast))[name = tensor("op_1696_cast")]; + tensor input_81_interleave_0 = const()[name = tensor("input_81_interleave_0"), val = tensor(false)]; + tensor input_81_cast = concat(axis = var_1213, interleave = input_81_interleave_0, values = (var_1682_cast, var_1684_cast, var_1686_cast, var_1688_cast, var_1690_cast, var_1692_cast, var_1694_cast, var_1696_cast))[name = tensor("input_81_cast")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([1, 1])]; + tensor var_1704 = const()[name = tensor("op_1704"), val = tensor([1, 1])]; + tensor var_1706_pad_type_0 = const()[name = tensor("op_1706_pad_type_0"), val = tensor("custom")]; + tensor var_1706_pad_0 = const()[name = tensor("op_1706_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16995712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17302976))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17303168)))]; + tensor var_1706_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1704, groups = var_1213, pad = var_1706_pad_0, pad_type = var_1706_pad_type_0, strides = var_1702, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_81_cast)[name = tensor("op_1706_cast")]; + tensor inputs_17_cast = add(x = var_1706_cast, y = inputs_15_cast)[name = tensor("inputs_17_cast")]; + tensor var_1710 = const()[name = tensor("op_1710"), val = tensor([1])]; + tensor channels_mean_17_cast = reduce_mean(axes = var_1710, keep_dims = var_1208, x = inputs_17_cast)[name = tensor("channels_mean_17_cast")]; + tensor zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast)[name = tensor("zero_mean_17_cast")]; + tensor zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast)[name = tensor("zero_mean_sq_17_cast")]; + tensor var_1714 = const()[name = tensor("op_1714"), val = tensor([1])]; + tensor var_1715_cast = reduce_mean(axes = var_1714, keep_dims = var_1208, x = zero_mean_sq_17_cast)[name = tensor("op_1715_cast")]; + tensor var_1716_to_fp16 = const()[name = tensor("op_1716_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1717_cast = add(x = var_1715_cast, y = var_1716_to_fp16)[name = tensor("op_1717_cast")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_1717_cast)[name = tensor("denom_17_cast")]; + tensor out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_cast)[name = tensor("out_17_cast")]; + tensor var_1721_to_fp16 = const()[name = tensor("op_1721_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17304512)))]; + tensor var_1722_cast = add(x = out_17_cast, y = var_1721_to_fp16)[name = tensor("op_1722_cast")]; + tensor var_1724_to_fp16 = const()[name = tensor("op_1724_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17305856)))]; + tensor input_83_cast = mul(x = var_1722_cast, y = var_1724_to_fp16)[name = tensor("input_83_cast")]; + tensor var_1732 = const()[name = tensor("op_1732"), val = tensor([1, 1])]; + tensor var_1734 = const()[name = tensor("op_1734"), val = tensor([1, 1])]; + tensor var_1736_pad_type_0 = const()[name = tensor("op_1736_pad_type_0"), val = tensor("custom")]; + tensor var_1736_pad_0 = const()[name = tensor("op_1736_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17307200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19764864))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19765056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19768960))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_1736_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1734, groups = var_1213, pad = var_1736_pad_0, pad_type = var_1736_pad_type_0, strides = var_1732, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_83_cast)[name = tensor("op_1736_cast")]; + tensor var_1737_split_sizes_0 = const()[name = tensor("op_1737_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1737_axis_0 = const()[name = tensor("op_1737_axis_0"), val = tensor(1)]; + tensor var_1737_cast_0, tensor var_1737_cast_1 = split(axis = var_1737_axis_0, split_sizes = var_1737_split_sizes_0, x = var_1736_cast)[name = tensor("op_1737_cast")]; + tensor var_1739_mode_0 = const()[name = tensor("op_1739_mode_0"), val = tensor("EXACT")]; + tensor var_1739_cast = gelu(mode = var_1739_mode_0, x = var_1737_cast_1)[name = tensor("op_1739_cast")]; + tensor input_85_cast = mul(x = var_1737_cast_0, y = var_1739_cast)[name = tensor("input_85_cast")]; + tensor var_1743 = const()[name = tensor("op_1743"), val = tensor([1, 1])]; + tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; + tensor var_1747_pad_type_0 = const()[name = tensor("op_1747_pad_type_0"), val = tensor("custom")]; + tensor var_1747_pad_0 = const()[name = tensor("op_1747_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19769152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20998016))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20998208)))]; + tensor var_1747_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1745, groups = var_1213, pad = var_1747_pad_0, pad_type = var_1747_pad_type_0, strides = var_1743, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_85_cast)[name = tensor("op_1747_cast")]; + tensor hidden_states_51_cast = add(x = var_1747_cast, y = inputs_17_cast)[name = tensor("hidden_states_51_cast")]; + tensor var_1749 = const()[name = tensor("op_1749"), val = tensor([2, 640, 48, 48])]; + tensor input_87_cast = reshape(shape = var_1749, x = hidden_states_51_cast)[name = tensor("input_87_cast")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 1])]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20999552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21306816))), name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21307008)))]; + tensor hidden_states_53_cast = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_1755, groups = var_1213, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_1753, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_87_cast)[name = tensor("hidden_states_53_cast")]; + tensor input_89_cast = add(x = hidden_states_53_cast, y = hidden_states_41_cast)[name = tensor("input_89_cast")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_89_cast)[name = tensor("reshape_36_cast")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_29_cast")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21308352)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21309696)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_19_cast")]; + tensor input_93_cast = silu(x = add_19_cast)[name = tensor("input_93_cast")]; + tensor var_1770 = const()[name = tensor("op_1770"), val = tensor([1, 1])]; + tensor var_1772 = const()[name = tensor("op_1772"), val = tensor([1, 1])]; + tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21311040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24075904))), name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24076096)))]; + tensor hidden_states_55_cast = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_1772, groups = var_1213, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_1770, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_93_cast)[name = tensor("hidden_states_55_cast")]; + tensor var_1778 = const()[name = tensor("op_1778"), val = tensor([1, 1])]; + tensor var_1780 = const()[name = tensor("op_1780"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24077440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24691904))), name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24692096)))]; + tensor temb_7_cast = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1780, groups = var_1213, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_1778, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_7_cast")]; + tensor input_97_cast = add(x = hidden_states_55_cast, y = temb_7_cast)[name = tensor("input_97_cast")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_97_cast)[name = tensor("reshape_40_cast")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_32_cast")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24693440)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24694784)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_21_cast")]; + tensor input_101_cast = silu(x = add_21_cast)[name = tensor("input_101_cast")]; + tensor var_1790 = const()[name = tensor("op_1790"), val = tensor([1, 1])]; + tensor var_1792 = const()[name = tensor("op_1792"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24696128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27460992))), name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27461184)))]; + tensor hidden_states_57_cast = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_1792, groups = var_1213, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_1790, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_101_cast)[name = tensor("hidden_states_57_cast")]; + tensor hidden_states_59_cast = add(x = input_89_cast, y = hidden_states_57_cast)[name = tensor("hidden_states_59_cast")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = hidden_states_59_cast)[name = tensor("reshape_44_cast")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_35_cast")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27462528)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27463872)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_23_cast")]; + tensor var_1812 = const()[name = tensor("op_1812"), val = tensor([1, 1])]; + tensor var_1814 = const()[name = tensor("op_1814"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_type_0 = const()[name = tensor("hidden_states_61_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_61_pad_0 = const()[name = tensor("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27465216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27772480))), name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27772672)))]; + tensor hidden_states_61_cast = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_1814, groups = var_1213, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_1812, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_23_cast)[name = tensor("hidden_states_61_cast")]; + tensor var_1819 = const()[name = tensor("op_1819"), val = tensor([2, 640, 1, 2304])]; + tensor inputs_19_cast = reshape(shape = var_1819, x = hidden_states_61_cast)[name = tensor("inputs_19_cast")]; + tensor var_1829 = const()[name = tensor("op_1829"), val = tensor([1])]; + tensor channels_mean_19_cast = reduce_mean(axes = var_1829, keep_dims = var_1208, x = inputs_19_cast)[name = tensor("channels_mean_19_cast")]; + tensor zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast)[name = tensor("zero_mean_19_cast")]; + tensor zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast)[name = tensor("zero_mean_sq_19_cast")]; + tensor var_1833 = const()[name = tensor("op_1833"), val = tensor([1])]; + tensor var_1834_cast = reduce_mean(axes = var_1833, keep_dims = var_1208, x = zero_mean_sq_19_cast)[name = tensor("op_1834_cast")]; + tensor var_1835_to_fp16 = const()[name = tensor("op_1835_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1836_cast = add(x = var_1834_cast, y = var_1835_to_fp16)[name = tensor("op_1836_cast")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_1836_cast)[name = tensor("denom_19_cast")]; + tensor out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast)[name = tensor("out_19_cast")]; + tensor var_1840_to_fp16 = const()[name = tensor("op_1840_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27774016)))]; + tensor var_1841_cast = add(x = out_19_cast, y = var_1840_to_fp16)[name = tensor("op_1841_cast")]; + tensor var_1843_to_fp16 = const()[name = tensor("op_1843_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27775360)))]; + tensor hidden_states_63_cast = mul(x = var_1841_cast, y = var_1843_to_fp16)[name = tensor("hidden_states_63_cast")]; + tensor var_1850 = const()[name = tensor("op_1850"), val = tensor([1, 1])]; + tensor var_1852 = const()[name = tensor("op_1852"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27776704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28083968))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_13_cast = conv(dilations = var_1852, groups = var_1213, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1850, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor("q_13_cast")]; + tensor var_1856 = const()[name = tensor("op_1856"), val = tensor([1, 1])]; + tensor var_1858 = const()[name = tensor("op_1858"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28084160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28391424))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_25_cast = conv(dilations = var_1858, groups = var_1213, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1856, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor("k_25_cast")]; + tensor var_1862 = const()[name = tensor("op_1862"), val = tensor([1, 1])]; + tensor var_1864 = const()[name = tensor("op_1864"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28391616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28698880))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_13_cast = conv(dilations = var_1864, groups = var_1213, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1862, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor("v_13_cast")]; + tensor var_1868_begin_0 = const()[name = tensor("op_1868_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1868_end_0 = const()[name = tensor("op_1868_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_1868_end_mask_0 = const()[name = tensor("op_1868_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1868_cast = slice_by_index(begin = var_1868_begin_0, end = var_1868_end_0, end_mask = var_1868_end_mask_0, x = q_13_cast)[name = tensor("op_1868_cast")]; + tensor var_1872_begin_0 = const()[name = tensor("op_1872_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_1872_end_0 = const()[name = tensor("op_1872_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_1872_end_mask_0 = const()[name = tensor("op_1872_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1872_cast = slice_by_index(begin = var_1872_begin_0, end = var_1872_end_0, end_mask = var_1872_end_mask_0, x = q_13_cast)[name = tensor("op_1872_cast")]; + tensor var_1876_begin_0 = const()[name = tensor("op_1876_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_1876_end_0 = const()[name = tensor("op_1876_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_1876_end_mask_0 = const()[name = tensor("op_1876_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1876_cast = slice_by_index(begin = var_1876_begin_0, end = var_1876_end_0, end_mask = var_1876_end_mask_0, x = q_13_cast)[name = tensor("op_1876_cast")]; + tensor var_1880_begin_0 = const()[name = tensor("op_1880_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_1880_end_0 = const()[name = tensor("op_1880_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_1880_end_mask_0 = const()[name = tensor("op_1880_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1880_cast = slice_by_index(begin = var_1880_begin_0, end = var_1880_end_0, end_mask = var_1880_end_mask_0, x = q_13_cast)[name = tensor("op_1880_cast")]; + tensor var_1884_begin_0 = const()[name = tensor("op_1884_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_1884_end_0 = const()[name = tensor("op_1884_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_1884_end_mask_0 = const()[name = tensor("op_1884_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1884_cast = slice_by_index(begin = var_1884_begin_0, end = var_1884_end_0, end_mask = var_1884_end_mask_0, x = q_13_cast)[name = tensor("op_1884_cast")]; + tensor var_1888_begin_0 = const()[name = tensor("op_1888_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_1888_end_0 = const()[name = tensor("op_1888_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_1888_end_mask_0 = const()[name = tensor("op_1888_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1888_cast = slice_by_index(begin = var_1888_begin_0, end = var_1888_end_0, end_mask = var_1888_end_mask_0, x = q_13_cast)[name = tensor("op_1888_cast")]; + tensor var_1892_begin_0 = const()[name = tensor("op_1892_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_1892_end_0 = const()[name = tensor("op_1892_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_1892_end_mask_0 = const()[name = tensor("op_1892_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1892_cast = slice_by_index(begin = var_1892_begin_0, end = var_1892_end_0, end_mask = var_1892_end_mask_0, x = q_13_cast)[name = tensor("op_1892_cast")]; + tensor var_1896_begin_0 = const()[name = tensor("op_1896_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_1896_end_0 = const()[name = tensor("op_1896_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_1896_end_mask_0 = const()[name = tensor("op_1896_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1896_cast = slice_by_index(begin = var_1896_begin_0, end = var_1896_end_0, end_mask = var_1896_end_mask_0, x = q_13_cast)[name = tensor("op_1896_cast")]; + tensor k_27_perm_0 = const()[name = tensor("k_27_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_1903_begin_0 = const()[name = tensor("op_1903_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1903_end_0 = const()[name = tensor("op_1903_end_0"), val = tensor([2, 2304, 1, 80])]; + tensor var_1903_end_mask_0 = const()[name = tensor("op_1903_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_25 = transpose(perm = k_27_perm_0, x = k_25_cast)[name = tensor("transpose_25")]; + tensor var_1903_cast = slice_by_index(begin = var_1903_begin_0, end = var_1903_end_0, end_mask = var_1903_end_mask_0, x = transpose_25)[name = tensor("op_1903_cast")]; + tensor var_1907_begin_0 = const()[name = tensor("op_1907_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_1907_end_0 = const()[name = tensor("op_1907_end_0"), val = tensor([2, 2304, 1, 160])]; + tensor var_1907_end_mask_0 = const()[name = tensor("op_1907_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1907_cast = slice_by_index(begin = var_1907_begin_0, end = var_1907_end_0, end_mask = var_1907_end_mask_0, x = transpose_25)[name = tensor("op_1907_cast")]; + tensor var_1911_begin_0 = const()[name = tensor("op_1911_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_1911_end_0 = const()[name = tensor("op_1911_end_0"), val = tensor([2, 2304, 1, 240])]; + tensor var_1911_end_mask_0 = const()[name = tensor("op_1911_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1911_cast = slice_by_index(begin = var_1911_begin_0, end = var_1911_end_0, end_mask = var_1911_end_mask_0, x = transpose_25)[name = tensor("op_1911_cast")]; + tensor var_1915_begin_0 = const()[name = tensor("op_1915_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_1915_end_0 = const()[name = tensor("op_1915_end_0"), val = tensor([2, 2304, 1, 320])]; + tensor var_1915_end_mask_0 = const()[name = tensor("op_1915_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1915_cast = slice_by_index(begin = var_1915_begin_0, end = var_1915_end_0, end_mask = var_1915_end_mask_0, x = transpose_25)[name = tensor("op_1915_cast")]; + tensor var_1919_begin_0 = const()[name = tensor("op_1919_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_1919_end_0 = const()[name = tensor("op_1919_end_0"), val = tensor([2, 2304, 1, 400])]; + tensor var_1919_end_mask_0 = const()[name = tensor("op_1919_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1919_cast = slice_by_index(begin = var_1919_begin_0, end = var_1919_end_0, end_mask = var_1919_end_mask_0, x = transpose_25)[name = tensor("op_1919_cast")]; + tensor var_1923_begin_0 = const()[name = tensor("op_1923_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_1923_end_0 = const()[name = tensor("op_1923_end_0"), val = tensor([2, 2304, 1, 480])]; + tensor var_1923_end_mask_0 = const()[name = tensor("op_1923_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1923_cast = slice_by_index(begin = var_1923_begin_0, end = var_1923_end_0, end_mask = var_1923_end_mask_0, x = transpose_25)[name = tensor("op_1923_cast")]; + tensor var_1927_begin_0 = const()[name = tensor("op_1927_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_1927_end_0 = const()[name = tensor("op_1927_end_0"), val = tensor([2, 2304, 1, 560])]; + tensor var_1927_end_mask_0 = const()[name = tensor("op_1927_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1927_cast = slice_by_index(begin = var_1927_begin_0, end = var_1927_end_0, end_mask = var_1927_end_mask_0, x = transpose_25)[name = tensor("op_1927_cast")]; + tensor var_1931_begin_0 = const()[name = tensor("op_1931_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_1931_end_0 = const()[name = tensor("op_1931_end_0"), val = tensor([2, 2304, 1, 640])]; + tensor var_1931_end_mask_0 = const()[name = tensor("op_1931_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_1931_cast = slice_by_index(begin = var_1931_begin_0, end = var_1931_end_0, end_mask = var_1931_end_mask_0, x = transpose_25)[name = tensor("op_1931_cast")]; + tensor var_1933_begin_0 = const()[name = tensor("op_1933_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1933_end_0 = const()[name = tensor("op_1933_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_1933_end_mask_0 = const()[name = tensor("op_1933_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1933_cast = slice_by_index(begin = var_1933_begin_0, end = var_1933_end_0, end_mask = var_1933_end_mask_0, x = v_13_cast)[name = tensor("op_1933_cast")]; + tensor var_1937_begin_0 = const()[name = tensor("op_1937_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_1937_end_0 = const()[name = tensor("op_1937_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_1937_end_mask_0 = const()[name = tensor("op_1937_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1937_cast = slice_by_index(begin = var_1937_begin_0, end = var_1937_end_0, end_mask = var_1937_end_mask_0, x = v_13_cast)[name = tensor("op_1937_cast")]; + tensor var_1941_begin_0 = const()[name = tensor("op_1941_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_1941_end_0 = const()[name = tensor("op_1941_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_1941_end_mask_0 = const()[name = tensor("op_1941_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1941_cast = slice_by_index(begin = var_1941_begin_0, end = var_1941_end_0, end_mask = var_1941_end_mask_0, x = v_13_cast)[name = tensor("op_1941_cast")]; + tensor var_1945_begin_0 = const()[name = tensor("op_1945_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_1945_end_0 = const()[name = tensor("op_1945_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_1945_end_mask_0 = const()[name = tensor("op_1945_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1945_cast = slice_by_index(begin = var_1945_begin_0, end = var_1945_end_0, end_mask = var_1945_end_mask_0, x = v_13_cast)[name = tensor("op_1945_cast")]; + tensor var_1949_begin_0 = const()[name = tensor("op_1949_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_1949_end_0 = const()[name = tensor("op_1949_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_1949_end_mask_0 = const()[name = tensor("op_1949_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1949_cast = slice_by_index(begin = var_1949_begin_0, end = var_1949_end_0, end_mask = var_1949_end_mask_0, x = v_13_cast)[name = tensor("op_1949_cast")]; + tensor var_1953_begin_0 = const()[name = tensor("op_1953_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_1953_end_0 = const()[name = tensor("op_1953_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_1953_end_mask_0 = const()[name = tensor("op_1953_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1953_cast = slice_by_index(begin = var_1953_begin_0, end = var_1953_end_0, end_mask = var_1953_end_mask_0, x = v_13_cast)[name = tensor("op_1953_cast")]; + tensor var_1957_begin_0 = const()[name = tensor("op_1957_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_1957_end_0 = const()[name = tensor("op_1957_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_1957_end_mask_0 = const()[name = tensor("op_1957_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1957_cast = slice_by_index(begin = var_1957_begin_0, end = var_1957_end_0, end_mask = var_1957_end_mask_0, x = v_13_cast)[name = tensor("op_1957_cast")]; + tensor var_1961_begin_0 = const()[name = tensor("op_1961_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_1961_end_0 = const()[name = tensor("op_1961_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_1961_end_mask_0 = const()[name = tensor("op_1961_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1961_cast = slice_by_index(begin = var_1961_begin_0, end = var_1961_end_0, end_mask = var_1961_end_mask_0, x = v_13_cast)[name = tensor("op_1961_cast")]; + tensor var_1965_equation_0 = const()[name = tensor("op_1965_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1965_cast = einsum(equation = var_1965_equation_0, values = (var_1903_cast, var_1868_cast))[name = tensor("op_1965_cast")]; + tensor var_1966_to_fp16 = const()[name = tensor("op_1966_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_97_cast = mul(x = var_1965_cast, y = var_1966_to_fp16)[name = tensor("aw_97_cast")]; + tensor var_1969_equation_0 = const()[name = tensor("op_1969_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1969_cast = einsum(equation = var_1969_equation_0, values = (var_1907_cast, var_1872_cast))[name = tensor("op_1969_cast")]; + tensor var_1970_to_fp16 = const()[name = tensor("op_1970_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_99_cast = mul(x = var_1969_cast, y = var_1970_to_fp16)[name = tensor("aw_99_cast")]; + tensor var_1973_equation_0 = const()[name = tensor("op_1973_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1973_cast = einsum(equation = var_1973_equation_0, values = (var_1911_cast, var_1876_cast))[name = tensor("op_1973_cast")]; + tensor var_1974_to_fp16 = const()[name = tensor("op_1974_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_101_cast = mul(x = var_1973_cast, y = var_1974_to_fp16)[name = tensor("aw_101_cast")]; + tensor var_1977_equation_0 = const()[name = tensor("op_1977_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1977_cast = einsum(equation = var_1977_equation_0, values = (var_1915_cast, var_1880_cast))[name = tensor("op_1977_cast")]; + tensor var_1978_to_fp16 = const()[name = tensor("op_1978_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_103_cast = mul(x = var_1977_cast, y = var_1978_to_fp16)[name = tensor("aw_103_cast")]; + tensor var_1981_equation_0 = const()[name = tensor("op_1981_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1981_cast = einsum(equation = var_1981_equation_0, values = (var_1919_cast, var_1884_cast))[name = tensor("op_1981_cast")]; + tensor var_1982_to_fp16 = const()[name = tensor("op_1982_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_105_cast = mul(x = var_1981_cast, y = var_1982_to_fp16)[name = tensor("aw_105_cast")]; + tensor var_1985_equation_0 = const()[name = tensor("op_1985_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1985_cast = einsum(equation = var_1985_equation_0, values = (var_1923_cast, var_1888_cast))[name = tensor("op_1985_cast")]; + tensor var_1986_to_fp16 = const()[name = tensor("op_1986_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_107_cast = mul(x = var_1985_cast, y = var_1986_to_fp16)[name = tensor("aw_107_cast")]; + tensor var_1989_equation_0 = const()[name = tensor("op_1989_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1989_cast = einsum(equation = var_1989_equation_0, values = (var_1927_cast, var_1892_cast))[name = tensor("op_1989_cast")]; + tensor var_1990_to_fp16 = const()[name = tensor("op_1990_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_109_cast = mul(x = var_1989_cast, y = var_1990_to_fp16)[name = tensor("aw_109_cast")]; + tensor var_1993_equation_0 = const()[name = tensor("op_1993_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_1993_cast = einsum(equation = var_1993_equation_0, values = (var_1931_cast, var_1896_cast))[name = tensor("op_1993_cast")]; + tensor var_1994_to_fp16 = const()[name = tensor("op_1994_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_111_cast = mul(x = var_1993_cast, y = var_1994_to_fp16)[name = tensor("aw_111_cast")]; + tensor var_1996_cast = softmax(axis = var_1213, x = aw_97_cast)[name = tensor("op_1996_cast")]; + tensor var_1997_cast = softmax(axis = var_1213, x = aw_99_cast)[name = tensor("op_1997_cast")]; + tensor var_1998_cast = softmax(axis = var_1213, x = aw_101_cast)[name = tensor("op_1998_cast")]; + tensor var_1999_cast = softmax(axis = var_1213, x = aw_103_cast)[name = tensor("op_1999_cast")]; + tensor var_2000_cast = softmax(axis = var_1213, x = aw_105_cast)[name = tensor("op_2000_cast")]; + tensor var_2001_cast = softmax(axis = var_1213, x = aw_107_cast)[name = tensor("op_2001_cast")]; + tensor var_2002_cast = softmax(axis = var_1213, x = aw_109_cast)[name = tensor("op_2002_cast")]; + tensor var_2003_cast = softmax(axis = var_1213, x = aw_111_cast)[name = tensor("op_2003_cast")]; + tensor var_2005_equation_0 = const()[name = tensor("op_2005_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2005_cast = einsum(equation = var_2005_equation_0, values = (var_1933_cast, var_1996_cast))[name = tensor("op_2005_cast")]; + tensor var_2007_equation_0 = const()[name = tensor("op_2007_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2007_cast = einsum(equation = var_2007_equation_0, values = (var_1937_cast, var_1997_cast))[name = tensor("op_2007_cast")]; + tensor var_2009_equation_0 = const()[name = tensor("op_2009_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2009_cast = einsum(equation = var_2009_equation_0, values = (var_1941_cast, var_1998_cast))[name = tensor("op_2009_cast")]; + tensor var_2011_equation_0 = const()[name = tensor("op_2011_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2011_cast = einsum(equation = var_2011_equation_0, values = (var_1945_cast, var_1999_cast))[name = tensor("op_2011_cast")]; + tensor var_2013_equation_0 = const()[name = tensor("op_2013_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2013_cast = einsum(equation = var_2013_equation_0, values = (var_1949_cast, var_2000_cast))[name = tensor("op_2013_cast")]; + tensor var_2015_equation_0 = const()[name = tensor("op_2015_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2015_cast = einsum(equation = var_2015_equation_0, values = (var_1953_cast, var_2001_cast))[name = tensor("op_2015_cast")]; + tensor var_2017_equation_0 = const()[name = tensor("op_2017_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2017_cast = einsum(equation = var_2017_equation_0, values = (var_1957_cast, var_2002_cast))[name = tensor("op_2017_cast")]; + tensor var_2019_equation_0 = const()[name = tensor("op_2019_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2019_cast = einsum(equation = var_2019_equation_0, values = (var_1961_cast, var_2003_cast))[name = tensor("op_2019_cast")]; + tensor input_105_interleave_0 = const()[name = tensor("input_105_interleave_0"), val = tensor(false)]; + tensor input_105_cast = concat(axis = var_1213, interleave = input_105_interleave_0, values = (var_2005_cast, var_2007_cast, var_2009_cast, var_2011_cast, var_2013_cast, var_2015_cast, var_2017_cast, var_2019_cast))[name = tensor("input_105_cast")]; + tensor var_2025 = const()[name = tensor("op_2025"), val = tensor([1, 1])]; + tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1])]; + tensor var_2029_pad_type_0 = const()[name = tensor("op_2029_pad_type_0"), val = tensor("custom")]; + tensor var_2029_pad_0 = const()[name = tensor("op_2029_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28699072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29006336))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29006528)))]; + tensor var_2029_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2027, groups = var_1213, pad = var_2029_pad_0, pad_type = var_2029_pad_type_0, strides = var_2025, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_105_cast)[name = tensor("op_2029_cast")]; + tensor inputs_21_cast = add(x = var_2029_cast, y = inputs_19_cast)[name = tensor("inputs_21_cast")]; + tensor var_2033 = const()[name = tensor("op_2033"), val = tensor([1])]; + tensor channels_mean_21_cast = reduce_mean(axes = var_2033, keep_dims = var_1208, x = inputs_21_cast)[name = tensor("channels_mean_21_cast")]; + tensor zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast)[name = tensor("zero_mean_21_cast")]; + tensor zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast)[name = tensor("zero_mean_sq_21_cast")]; + tensor var_2037 = const()[name = tensor("op_2037"), val = tensor([1])]; + tensor var_2038_cast = reduce_mean(axes = var_2037, keep_dims = var_1208, x = zero_mean_sq_21_cast)[name = tensor("op_2038_cast")]; + tensor var_2039_to_fp16 = const()[name = tensor("op_2039_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2040_cast = add(x = var_2038_cast, y = var_2039_to_fp16)[name = tensor("op_2040_cast")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_2040_cast)[name = tensor("denom_21_cast")]; + tensor out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast)[name = tensor("out_21_cast")]; + tensor var_2044_to_fp16 = const()[name = tensor("op_2044_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29007872)))]; + tensor var_2045_cast = add(x = out_21_cast, y = var_2044_to_fp16)[name = tensor("op_2045_cast")]; + tensor var_2047_to_fp16 = const()[name = tensor("op_2047_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29009216)))]; + tensor hidden_states_65_cast = mul(x = var_2045_cast, y = var_2047_to_fp16)[name = tensor("hidden_states_65_cast")]; + tensor var_2054 = const()[name = tensor("op_2054"), val = tensor([1, 1])]; + tensor var_2056 = const()[name = tensor("op_2056"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29010560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29317824))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_15_cast = conv(dilations = var_2056, groups = var_1213, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_2054, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_65_cast)[name = tensor("q_15_cast")]; + tensor var_2060 = const()[name = tensor("op_2060"), val = tensor([1, 1])]; + tensor var_2062 = const()[name = tensor("op_2062"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29318016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29686720))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_29_cast = conv(dilations = var_2062, groups = var_1213, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_2060, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_29_cast")]; + tensor var_2066 = const()[name = tensor("op_2066"), val = tensor([1, 1])]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29686912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30055616))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_15_cast = conv(dilations = var_2068, groups = var_1213, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_2066, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_15_cast")]; + tensor var_2072_begin_0 = const()[name = tensor("op_2072_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2072_end_0 = const()[name = tensor("op_2072_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_2072_end_mask_0 = const()[name = tensor("op_2072_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2072_cast = slice_by_index(begin = var_2072_begin_0, end = var_2072_end_0, end_mask = var_2072_end_mask_0, x = q_15_cast)[name = tensor("op_2072_cast")]; + tensor var_2076_begin_0 = const()[name = tensor("op_2076_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_2076_end_0 = const()[name = tensor("op_2076_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_2076_end_mask_0 = const()[name = tensor("op_2076_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2076_cast = slice_by_index(begin = var_2076_begin_0, end = var_2076_end_0, end_mask = var_2076_end_mask_0, x = q_15_cast)[name = tensor("op_2076_cast")]; + tensor var_2080_begin_0 = const()[name = tensor("op_2080_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_2080_end_0 = const()[name = tensor("op_2080_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_2080_end_mask_0 = const()[name = tensor("op_2080_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2080_cast = slice_by_index(begin = var_2080_begin_0, end = var_2080_end_0, end_mask = var_2080_end_mask_0, x = q_15_cast)[name = tensor("op_2080_cast")]; + tensor var_2084_begin_0 = const()[name = tensor("op_2084_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_2084_end_0 = const()[name = tensor("op_2084_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_2084_end_mask_0 = const()[name = tensor("op_2084_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2084_cast = slice_by_index(begin = var_2084_begin_0, end = var_2084_end_0, end_mask = var_2084_end_mask_0, x = q_15_cast)[name = tensor("op_2084_cast")]; + tensor var_2088_begin_0 = const()[name = tensor("op_2088_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_2088_end_0 = const()[name = tensor("op_2088_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_2088_end_mask_0 = const()[name = tensor("op_2088_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2088_cast = slice_by_index(begin = var_2088_begin_0, end = var_2088_end_0, end_mask = var_2088_end_mask_0, x = q_15_cast)[name = tensor("op_2088_cast")]; + tensor var_2092_begin_0 = const()[name = tensor("op_2092_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_2092_end_0 = const()[name = tensor("op_2092_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_2092_end_mask_0 = const()[name = tensor("op_2092_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2092_cast = slice_by_index(begin = var_2092_begin_0, end = var_2092_end_0, end_mask = var_2092_end_mask_0, x = q_15_cast)[name = tensor("op_2092_cast")]; + tensor var_2096_begin_0 = const()[name = tensor("op_2096_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_2096_end_0 = const()[name = tensor("op_2096_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_2096_end_mask_0 = const()[name = tensor("op_2096_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2096_cast = slice_by_index(begin = var_2096_begin_0, end = var_2096_end_0, end_mask = var_2096_end_mask_0, x = q_15_cast)[name = tensor("op_2096_cast")]; + tensor var_2100_begin_0 = const()[name = tensor("op_2100_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_2100_end_0 = const()[name = tensor("op_2100_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_2100_end_mask_0 = const()[name = tensor("op_2100_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2100_cast = slice_by_index(begin = var_2100_begin_0, end = var_2100_end_0, end_mask = var_2100_end_mask_0, x = q_15_cast)[name = tensor("op_2100_cast")]; + tensor k_31_perm_0 = const()[name = tensor("k_31_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_2107_begin_0 = const()[name = tensor("op_2107_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2107_end_0 = const()[name = tensor("op_2107_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_2107_end_mask_0 = const()[name = tensor("op_2107_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_24 = transpose(perm = k_31_perm_0, x = k_29_cast)[name = tensor("transpose_24")]; + tensor var_2107_cast = slice_by_index(begin = var_2107_begin_0, end = var_2107_end_0, end_mask = var_2107_end_mask_0, x = transpose_24)[name = tensor("op_2107_cast")]; + tensor var_2111_begin_0 = const()[name = tensor("op_2111_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_2111_end_0 = const()[name = tensor("op_2111_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_2111_end_mask_0 = const()[name = tensor("op_2111_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2111_cast = slice_by_index(begin = var_2111_begin_0, end = var_2111_end_0, end_mask = var_2111_end_mask_0, x = transpose_24)[name = tensor("op_2111_cast")]; + tensor var_2115_begin_0 = const()[name = tensor("op_2115_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_2115_end_0 = const()[name = tensor("op_2115_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_2115_end_mask_0 = const()[name = tensor("op_2115_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2115_cast = slice_by_index(begin = var_2115_begin_0, end = var_2115_end_0, end_mask = var_2115_end_mask_0, x = transpose_24)[name = tensor("op_2115_cast")]; + tensor var_2119_begin_0 = const()[name = tensor("op_2119_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_2119_end_0 = const()[name = tensor("op_2119_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_2119_end_mask_0 = const()[name = tensor("op_2119_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2119_cast = slice_by_index(begin = var_2119_begin_0, end = var_2119_end_0, end_mask = var_2119_end_mask_0, x = transpose_24)[name = tensor("op_2119_cast")]; + tensor var_2123_begin_0 = const()[name = tensor("op_2123_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_2123_end_0 = const()[name = tensor("op_2123_end_0"), val = tensor([2, 77, 1, 400])]; + tensor var_2123_end_mask_0 = const()[name = tensor("op_2123_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2123_cast = slice_by_index(begin = var_2123_begin_0, end = var_2123_end_0, end_mask = var_2123_end_mask_0, x = transpose_24)[name = tensor("op_2123_cast")]; + tensor var_2127_begin_0 = const()[name = tensor("op_2127_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_2127_end_0 = const()[name = tensor("op_2127_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_2127_end_mask_0 = const()[name = tensor("op_2127_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2127_cast = slice_by_index(begin = var_2127_begin_0, end = var_2127_end_0, end_mask = var_2127_end_mask_0, x = transpose_24)[name = tensor("op_2127_cast")]; + tensor var_2131_begin_0 = const()[name = tensor("op_2131_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_2131_end_0 = const()[name = tensor("op_2131_end_0"), val = tensor([2, 77, 1, 560])]; + tensor var_2131_end_mask_0 = const()[name = tensor("op_2131_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2131_cast = slice_by_index(begin = var_2131_begin_0, end = var_2131_end_0, end_mask = var_2131_end_mask_0, x = transpose_24)[name = tensor("op_2131_cast")]; + tensor var_2135_begin_0 = const()[name = tensor("op_2135_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_2135_end_0 = const()[name = tensor("op_2135_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_2135_end_mask_0 = const()[name = tensor("op_2135_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2135_cast = slice_by_index(begin = var_2135_begin_0, end = var_2135_end_0, end_mask = var_2135_end_mask_0, x = transpose_24)[name = tensor("op_2135_cast")]; + tensor var_2137_begin_0 = const()[name = tensor("op_2137_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2137_end_0 = const()[name = tensor("op_2137_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_2137_end_mask_0 = const()[name = tensor("op_2137_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2137_cast = slice_by_index(begin = var_2137_begin_0, end = var_2137_end_0, end_mask = var_2137_end_mask_0, x = v_15_cast)[name = tensor("op_2137_cast")]; + tensor var_2141_begin_0 = const()[name = tensor("op_2141_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_2141_end_0 = const()[name = tensor("op_2141_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_2141_end_mask_0 = const()[name = tensor("op_2141_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2141_cast = slice_by_index(begin = var_2141_begin_0, end = var_2141_end_0, end_mask = var_2141_end_mask_0, x = v_15_cast)[name = tensor("op_2141_cast")]; + tensor var_2145_begin_0 = const()[name = tensor("op_2145_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_2145_end_0 = const()[name = tensor("op_2145_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_2145_end_mask_0 = const()[name = tensor("op_2145_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2145_cast = slice_by_index(begin = var_2145_begin_0, end = var_2145_end_0, end_mask = var_2145_end_mask_0, x = v_15_cast)[name = tensor("op_2145_cast")]; + tensor var_2149_begin_0 = const()[name = tensor("op_2149_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_2149_end_0 = const()[name = tensor("op_2149_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_2149_end_mask_0 = const()[name = tensor("op_2149_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2149_cast = slice_by_index(begin = var_2149_begin_0, end = var_2149_end_0, end_mask = var_2149_end_mask_0, x = v_15_cast)[name = tensor("op_2149_cast")]; + tensor var_2153_begin_0 = const()[name = tensor("op_2153_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_2153_end_0 = const()[name = tensor("op_2153_end_0"), val = tensor([2, 400, 1, 77])]; + tensor var_2153_end_mask_0 = const()[name = tensor("op_2153_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2153_cast = slice_by_index(begin = var_2153_begin_0, end = var_2153_end_0, end_mask = var_2153_end_mask_0, x = v_15_cast)[name = tensor("op_2153_cast")]; + tensor var_2157_begin_0 = const()[name = tensor("op_2157_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_2157_end_0 = const()[name = tensor("op_2157_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_2157_end_mask_0 = const()[name = tensor("op_2157_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2157_cast = slice_by_index(begin = var_2157_begin_0, end = var_2157_end_0, end_mask = var_2157_end_mask_0, x = v_15_cast)[name = tensor("op_2157_cast")]; + tensor var_2161_begin_0 = const()[name = tensor("op_2161_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_2161_end_0 = const()[name = tensor("op_2161_end_0"), val = tensor([2, 560, 1, 77])]; + tensor var_2161_end_mask_0 = const()[name = tensor("op_2161_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2161_cast = slice_by_index(begin = var_2161_begin_0, end = var_2161_end_0, end_mask = var_2161_end_mask_0, x = v_15_cast)[name = tensor("op_2161_cast")]; + tensor var_2165_begin_0 = const()[name = tensor("op_2165_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_2165_end_0 = const()[name = tensor("op_2165_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_2165_end_mask_0 = const()[name = tensor("op_2165_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2165_cast = slice_by_index(begin = var_2165_begin_0, end = var_2165_end_0, end_mask = var_2165_end_mask_0, x = v_15_cast)[name = tensor("op_2165_cast")]; + tensor var_2169_equation_0 = const()[name = tensor("op_2169_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2169_cast = einsum(equation = var_2169_equation_0, values = (var_2107_cast, var_2072_cast))[name = tensor("op_2169_cast")]; + tensor var_2170_to_fp16 = const()[name = tensor("op_2170_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_113_cast = mul(x = var_2169_cast, y = var_2170_to_fp16)[name = tensor("aw_113_cast")]; + tensor var_2173_equation_0 = const()[name = tensor("op_2173_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2173_cast = einsum(equation = var_2173_equation_0, values = (var_2111_cast, var_2076_cast))[name = tensor("op_2173_cast")]; + tensor var_2174_to_fp16 = const()[name = tensor("op_2174_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_115_cast = mul(x = var_2173_cast, y = var_2174_to_fp16)[name = tensor("aw_115_cast")]; + tensor var_2177_equation_0 = const()[name = tensor("op_2177_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2177_cast = einsum(equation = var_2177_equation_0, values = (var_2115_cast, var_2080_cast))[name = tensor("op_2177_cast")]; + tensor var_2178_to_fp16 = const()[name = tensor("op_2178_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_117_cast = mul(x = var_2177_cast, y = var_2178_to_fp16)[name = tensor("aw_117_cast")]; + tensor var_2181_equation_0 = const()[name = tensor("op_2181_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2181_cast = einsum(equation = var_2181_equation_0, values = (var_2119_cast, var_2084_cast))[name = tensor("op_2181_cast")]; + tensor var_2182_to_fp16 = const()[name = tensor("op_2182_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_119_cast = mul(x = var_2181_cast, y = var_2182_to_fp16)[name = tensor("aw_119_cast")]; + tensor var_2185_equation_0 = const()[name = tensor("op_2185_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2185_cast = einsum(equation = var_2185_equation_0, values = (var_2123_cast, var_2088_cast))[name = tensor("op_2185_cast")]; + tensor var_2186_to_fp16 = const()[name = tensor("op_2186_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_121_cast = mul(x = var_2185_cast, y = var_2186_to_fp16)[name = tensor("aw_121_cast")]; + tensor var_2189_equation_0 = const()[name = tensor("op_2189_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2189_cast = einsum(equation = var_2189_equation_0, values = (var_2127_cast, var_2092_cast))[name = tensor("op_2189_cast")]; + tensor var_2190_to_fp16 = const()[name = tensor("op_2190_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_123_cast = mul(x = var_2189_cast, y = var_2190_to_fp16)[name = tensor("aw_123_cast")]; + tensor var_2193_equation_0 = const()[name = tensor("op_2193_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2193_cast = einsum(equation = var_2193_equation_0, values = (var_2131_cast, var_2096_cast))[name = tensor("op_2193_cast")]; + tensor var_2194_to_fp16 = const()[name = tensor("op_2194_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_125_cast = mul(x = var_2193_cast, y = var_2194_to_fp16)[name = tensor("aw_125_cast")]; + tensor var_2197_equation_0 = const()[name = tensor("op_2197_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2197_cast = einsum(equation = var_2197_equation_0, values = (var_2135_cast, var_2100_cast))[name = tensor("op_2197_cast")]; + tensor var_2198_to_fp16 = const()[name = tensor("op_2198_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_127_cast = mul(x = var_2197_cast, y = var_2198_to_fp16)[name = tensor("aw_127_cast")]; + tensor var_2200_cast = softmax(axis = var_1213, x = aw_113_cast)[name = tensor("op_2200_cast")]; + tensor var_2201_cast = softmax(axis = var_1213, x = aw_115_cast)[name = tensor("op_2201_cast")]; + tensor var_2202_cast = softmax(axis = var_1213, x = aw_117_cast)[name = tensor("op_2202_cast")]; + tensor var_2203_cast = softmax(axis = var_1213, x = aw_119_cast)[name = tensor("op_2203_cast")]; + tensor var_2204_cast = softmax(axis = var_1213, x = aw_121_cast)[name = tensor("op_2204_cast")]; + tensor var_2205_cast = softmax(axis = var_1213, x = aw_123_cast)[name = tensor("op_2205_cast")]; + tensor var_2206_cast = softmax(axis = var_1213, x = aw_125_cast)[name = tensor("op_2206_cast")]; + tensor var_2207_cast = softmax(axis = var_1213, x = aw_127_cast)[name = tensor("op_2207_cast")]; + tensor var_2209_equation_0 = const()[name = tensor("op_2209_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2209_cast = einsum(equation = var_2209_equation_0, values = (var_2137_cast, var_2200_cast))[name = tensor("op_2209_cast")]; + tensor var_2211_equation_0 = const()[name = tensor("op_2211_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2211_cast = einsum(equation = var_2211_equation_0, values = (var_2141_cast, var_2201_cast))[name = tensor("op_2211_cast")]; + tensor var_2213_equation_0 = const()[name = tensor("op_2213_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2213_cast = einsum(equation = var_2213_equation_0, values = (var_2145_cast, var_2202_cast))[name = tensor("op_2213_cast")]; + tensor var_2215_equation_0 = const()[name = tensor("op_2215_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2215_cast = einsum(equation = var_2215_equation_0, values = (var_2149_cast, var_2203_cast))[name = tensor("op_2215_cast")]; + tensor var_2217_equation_0 = const()[name = tensor("op_2217_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2217_cast = einsum(equation = var_2217_equation_0, values = (var_2153_cast, var_2204_cast))[name = tensor("op_2217_cast")]; + tensor var_2219_equation_0 = const()[name = tensor("op_2219_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2219_cast = einsum(equation = var_2219_equation_0, values = (var_2157_cast, var_2205_cast))[name = tensor("op_2219_cast")]; + tensor var_2221_equation_0 = const()[name = tensor("op_2221_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2221_cast = einsum(equation = var_2221_equation_0, values = (var_2161_cast, var_2206_cast))[name = tensor("op_2221_cast")]; + tensor var_2223_equation_0 = const()[name = tensor("op_2223_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2223_cast = einsum(equation = var_2223_equation_0, values = (var_2165_cast, var_2207_cast))[name = tensor("op_2223_cast")]; + tensor input_107_interleave_0 = const()[name = tensor("input_107_interleave_0"), val = tensor(false)]; + tensor input_107_cast = concat(axis = var_1213, interleave = input_107_interleave_0, values = (var_2209_cast, var_2211_cast, var_2213_cast, var_2215_cast, var_2217_cast, var_2219_cast, var_2221_cast, var_2223_cast))[name = tensor("input_107_cast")]; + tensor var_2229 = const()[name = tensor("op_2229"), val = tensor([1, 1])]; + tensor var_2231 = const()[name = tensor("op_2231"), val = tensor([1, 1])]; + tensor var_2233_pad_type_0 = const()[name = tensor("op_2233_pad_type_0"), val = tensor("custom")]; + tensor var_2233_pad_0 = const()[name = tensor("op_2233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30055808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30363072))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30363264)))]; + tensor var_2233_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2231, groups = var_1213, pad = var_2233_pad_0, pad_type = var_2233_pad_type_0, strides = var_2229, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_107_cast)[name = tensor("op_2233_cast")]; + tensor inputs_23_cast = add(x = var_2233_cast, y = inputs_21_cast)[name = tensor("inputs_23_cast")]; + tensor var_2237 = const()[name = tensor("op_2237"), val = tensor([1])]; + tensor channels_mean_23_cast = reduce_mean(axes = var_2237, keep_dims = var_1208, x = inputs_23_cast)[name = tensor("channels_mean_23_cast")]; + tensor zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast)[name = tensor("zero_mean_23_cast")]; + tensor zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast)[name = tensor("zero_mean_sq_23_cast")]; + tensor var_2241 = const()[name = tensor("op_2241"), val = tensor([1])]; + tensor var_2242_cast = reduce_mean(axes = var_2241, keep_dims = var_1208, x = zero_mean_sq_23_cast)[name = tensor("op_2242_cast")]; + tensor var_2243_to_fp16 = const()[name = tensor("op_2243_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2244_cast = add(x = var_2242_cast, y = var_2243_to_fp16)[name = tensor("op_2244_cast")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_2244_cast)[name = tensor("denom_23_cast")]; + tensor out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast)[name = tensor("out_23_cast")]; + tensor var_2248_to_fp16 = const()[name = tensor("op_2248_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30364608)))]; + tensor var_2249_cast = add(x = out_23_cast, y = var_2248_to_fp16)[name = tensor("op_2249_cast")]; + tensor var_2251_to_fp16 = const()[name = tensor("op_2251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30365952)))]; + tensor input_109_cast = mul(x = var_2249_cast, y = var_2251_to_fp16)[name = tensor("input_109_cast")]; + tensor var_2259 = const()[name = tensor("op_2259"), val = tensor([1, 1])]; + tensor var_2261 = const()[name = tensor("op_2261"), val = tensor([1, 1])]; + tensor var_2263_pad_type_0 = const()[name = tensor("op_2263_pad_type_0"), val = tensor("custom")]; + tensor var_2263_pad_0 = const()[name = tensor("op_2263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30367296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32824960))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32825152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32829056))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_2263_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2261, groups = var_1213, pad = var_2263_pad_0, pad_type = var_2263_pad_type_0, strides = var_2259, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_109_cast)[name = tensor("op_2263_cast")]; + tensor var_2264_split_sizes_0 = const()[name = tensor("op_2264_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_2264_axis_0 = const()[name = tensor("op_2264_axis_0"), val = tensor(1)]; + tensor var_2264_cast_0, tensor var_2264_cast_1 = split(axis = var_2264_axis_0, split_sizes = var_2264_split_sizes_0, x = var_2263_cast)[name = tensor("op_2264_cast")]; + tensor var_2266_mode_0 = const()[name = tensor("op_2266_mode_0"), val = tensor("EXACT")]; + tensor var_2266_cast = gelu(mode = var_2266_mode_0, x = var_2264_cast_1)[name = tensor("op_2266_cast")]; + tensor input_111_cast = mul(x = var_2264_cast_0, y = var_2266_cast)[name = tensor("input_111_cast")]; + tensor var_2270 = const()[name = tensor("op_2270"), val = tensor([1, 1])]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor var_2274_pad_type_0 = const()[name = tensor("op_2274_pad_type_0"), val = tensor("custom")]; + tensor var_2274_pad_0 = const()[name = tensor("op_2274_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32829248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34058112))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34058304)))]; + tensor var_2274_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2272, groups = var_1213, pad = var_2274_pad_0, pad_type = var_2274_pad_type_0, strides = var_2270, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_111_cast)[name = tensor("op_2274_cast")]; + tensor hidden_states_69_cast = add(x = var_2274_cast, y = inputs_23_cast)[name = tensor("hidden_states_69_cast")]; + tensor var_2276 = const()[name = tensor("op_2276"), val = tensor([2, 640, 48, 48])]; + tensor input_113_cast = reshape(shape = var_2276, x = hidden_states_69_cast)[name = tensor("input_113_cast")]; + tensor var_2280 = const()[name = tensor("op_2280"), val = tensor([1, 1])]; + tensor var_2282 = const()[name = tensor("op_2282"), val = tensor([1, 1])]; + tensor hidden_states_71_pad_type_0 = const()[name = tensor("hidden_states_71_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_71_pad_0 = const()[name = tensor("hidden_states_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34059648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34366912))), name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34367104)))]; + tensor hidden_states_71_cast = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_2282, groups = var_1213, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_2280, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_113_cast)[name = tensor("hidden_states_71_cast")]; + tensor input_115_cast = add(x = hidden_states_71_cast, y = hidden_states_59_cast)[name = tensor("input_115_cast")]; + tensor var_2289 = const()[name = tensor("op_2289"), val = tensor([2, 2])]; + tensor var_2291 = const()[name = tensor("op_2291"), val = tensor([1, 1])]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34368448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37133312))), name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37133504)))]; + tensor input_117_cast = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_2291, groups = var_1213, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_2289, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized, x = input_115_cast)[name = tensor("input_117_cast")]; + tensor var_2314 = const()[name = tensor("op_2314"), val = tensor(true)]; + tensor var_2319 = const()[name = tensor("op_2319"), val = tensor(1)]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 20, 24, 24])]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_117_cast)[name = tensor("reshape_48_cast")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_38_cast")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 640, 24, 24])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37134848)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37136192)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_25_cast")]; + tensor input_121_cast = silu(x = add_25_cast)[name = tensor("input_121_cast")]; + tensor var_2342 = const()[name = tensor("op_2342"), val = tensor([1, 1])]; + tensor var_2344 = const()[name = tensor("op_2344"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_type_0 = const()[name = tensor("hidden_states_73_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_73_pad_0 = const()[name = tensor("hidden_states_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37137536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42667200))), name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 640, 3, 3])]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42667392)))]; + tensor hidden_states_73_cast = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_2344, groups = var_2319, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_2342, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_121_cast)[name = tensor("hidden_states_73_cast")]; + tensor var_2350 = const()[name = tensor("op_2350"), val = tensor([1, 1])]; + tensor var_2352 = const()[name = tensor("op_2352"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42670016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43898880))), name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43899072)))]; + tensor temb_9_cast = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2352, groups = var_2319, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_2350, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_9_cast")]; + tensor input_125_cast = add(x = hidden_states_73_cast, y = temb_9_cast)[name = tensor("input_125_cast")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_125_cast)[name = tensor("reshape_52_cast")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_41_cast")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_cast")]; + tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43901696)))]; + tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43904320)))]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43906944)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43909568)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_27_cast")]; + tensor input_129_cast = silu(x = add_27_cast)[name = tensor("input_129_cast")]; + tensor var_2362 = const()[name = tensor("op_2362"), val = tensor([1, 1])]; + tensor var_2364 = const()[name = tensor("op_2364"), val = tensor([1, 1])]; + tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43912192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54971456))), name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54971648)))]; + tensor hidden_states_75_cast = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_2364, groups = var_2319, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_2362, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_129_cast)[name = tensor("hidden_states_75_cast")]; + tensor var_2369 = const()[name = tensor("op_2369"), val = tensor([1, 1])]; + tensor var_2371 = const()[name = tensor("op_2371"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54974272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55588736))), name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 640, 1, 1])]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55588928)))]; + tensor x_3_cast = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2371, groups = var_2319, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_2369, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_117_cast)[name = tensor("x_3_cast")]; + tensor hidden_states_77_cast = add(x = x_3_cast, y = hidden_states_75_cast)[name = tensor("hidden_states_77_cast")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = hidden_states_77_cast)[name = tensor("reshape_56_cast")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_44_cast")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55591552)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55594176)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_29_cast")]; + tensor var_2391 = const()[name = tensor("op_2391"), val = tensor([1, 1])]; + tensor var_2393 = const()[name = tensor("op_2393"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_type_0 = const()[name = tensor("hidden_states_79_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_79_pad_0 = const()[name = tensor("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55596800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56825664))), name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56825856)))]; + tensor hidden_states_79_cast = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_2393, groups = var_2319, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_2391, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_29_cast)[name = tensor("hidden_states_79_cast")]; + tensor var_2398 = const()[name = tensor("op_2398"), val = tensor([2, 1280, 1, 576])]; + tensor inputs_25_cast = reshape(shape = var_2398, x = hidden_states_79_cast)[name = tensor("inputs_25_cast")]; + tensor var_2408 = const()[name = tensor("op_2408"), val = tensor([1])]; + tensor channels_mean_25_cast = reduce_mean(axes = var_2408, keep_dims = var_2314, x = inputs_25_cast)[name = tensor("channels_mean_25_cast")]; + tensor zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast)[name = tensor("zero_mean_25_cast")]; + tensor zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast)[name = tensor("zero_mean_sq_25_cast")]; + tensor var_2412 = const()[name = tensor("op_2412"), val = tensor([1])]; + tensor var_2413_cast = reduce_mean(axes = var_2412, keep_dims = var_2314, x = zero_mean_sq_25_cast)[name = tensor("op_2413_cast")]; + tensor var_2414_to_fp16 = const()[name = tensor("op_2414_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2415_cast = add(x = var_2413_cast, y = var_2414_to_fp16)[name = tensor("op_2415_cast")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_2415_cast)[name = tensor("denom_25_cast")]; + tensor out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast)[name = tensor("out_25_cast")]; + tensor var_2419_to_fp16 = const()[name = tensor("op_2419_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56828480)))]; + tensor var_2420_cast = add(x = out_25_cast, y = var_2419_to_fp16)[name = tensor("op_2420_cast")]; + tensor var_2422_to_fp16 = const()[name = tensor("op_2422_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56831104)))]; + tensor hidden_states_81_cast = mul(x = var_2420_cast, y = var_2422_to_fp16)[name = tensor("hidden_states_81_cast")]; + tensor var_2429 = const()[name = tensor("op_2429"), val = tensor([1, 1])]; + tensor var_2431 = const()[name = tensor("op_2431"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56833728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58062592))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_17_cast = conv(dilations = var_2431, groups = var_2319, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_2429, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor("q_17_cast")]; + tensor var_2435 = const()[name = tensor("op_2435"), val = tensor([1, 1])]; + tensor var_2437 = const()[name = tensor("op_2437"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58062784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59291648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_33_cast = conv(dilations = var_2437, groups = var_2319, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2435, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor("k_33_cast")]; + tensor var_2441 = const()[name = tensor("op_2441"), val = tensor([1, 1])]; + tensor var_2443 = const()[name = tensor("op_2443"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59291840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60520704))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_17_cast = conv(dilations = var_2443, groups = var_2319, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_2441, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor("v_17_cast")]; + tensor var_2447_begin_0 = const()[name = tensor("op_2447_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2447_end_0 = const()[name = tensor("op_2447_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_2447_end_mask_0 = const()[name = tensor("op_2447_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2447_cast = slice_by_index(begin = var_2447_begin_0, end = var_2447_end_0, end_mask = var_2447_end_mask_0, x = q_17_cast)[name = tensor("op_2447_cast")]; + tensor var_2451_begin_0 = const()[name = tensor("op_2451_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_2451_end_0 = const()[name = tensor("op_2451_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_2451_end_mask_0 = const()[name = tensor("op_2451_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2451_cast = slice_by_index(begin = var_2451_begin_0, end = var_2451_end_0, end_mask = var_2451_end_mask_0, x = q_17_cast)[name = tensor("op_2451_cast")]; + tensor var_2455_begin_0 = const()[name = tensor("op_2455_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_2455_end_0 = const()[name = tensor("op_2455_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_2455_end_mask_0 = const()[name = tensor("op_2455_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2455_cast = slice_by_index(begin = var_2455_begin_0, end = var_2455_end_0, end_mask = var_2455_end_mask_0, x = q_17_cast)[name = tensor("op_2455_cast")]; + tensor var_2459_begin_0 = const()[name = tensor("op_2459_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_2459_end_0 = const()[name = tensor("op_2459_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_2459_end_mask_0 = const()[name = tensor("op_2459_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2459_cast = slice_by_index(begin = var_2459_begin_0, end = var_2459_end_0, end_mask = var_2459_end_mask_0, x = q_17_cast)[name = tensor("op_2459_cast")]; + tensor var_2463_begin_0 = const()[name = tensor("op_2463_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_2463_end_0 = const()[name = tensor("op_2463_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_2463_end_mask_0 = const()[name = tensor("op_2463_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2463_cast = slice_by_index(begin = var_2463_begin_0, end = var_2463_end_0, end_mask = var_2463_end_mask_0, x = q_17_cast)[name = tensor("op_2463_cast")]; + tensor var_2467_begin_0 = const()[name = tensor("op_2467_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_2467_end_0 = const()[name = tensor("op_2467_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_2467_end_mask_0 = const()[name = tensor("op_2467_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2467_cast = slice_by_index(begin = var_2467_begin_0, end = var_2467_end_0, end_mask = var_2467_end_mask_0, x = q_17_cast)[name = tensor("op_2467_cast")]; + tensor var_2471_begin_0 = const()[name = tensor("op_2471_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_2471_end_0 = const()[name = tensor("op_2471_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_2471_end_mask_0 = const()[name = tensor("op_2471_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2471_cast = slice_by_index(begin = var_2471_begin_0, end = var_2471_end_0, end_mask = var_2471_end_mask_0, x = q_17_cast)[name = tensor("op_2471_cast")]; + tensor var_2475_begin_0 = const()[name = tensor("op_2475_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_2475_end_0 = const()[name = tensor("op_2475_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_2475_end_mask_0 = const()[name = tensor("op_2475_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2475_cast = slice_by_index(begin = var_2475_begin_0, end = var_2475_end_0, end_mask = var_2475_end_mask_0, x = q_17_cast)[name = tensor("op_2475_cast")]; + tensor k_35_perm_0 = const()[name = tensor("k_35_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_2482_begin_0 = const()[name = tensor("op_2482_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2482_end_0 = const()[name = tensor("op_2482_end_0"), val = tensor([2, 576, 1, 160])]; + tensor var_2482_end_mask_0 = const()[name = tensor("op_2482_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_23 = transpose(perm = k_35_perm_0, x = k_33_cast)[name = tensor("transpose_23")]; + tensor var_2482_cast = slice_by_index(begin = var_2482_begin_0, end = var_2482_end_0, end_mask = var_2482_end_mask_0, x = transpose_23)[name = tensor("op_2482_cast")]; + tensor var_2486_begin_0 = const()[name = tensor("op_2486_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_2486_end_0 = const()[name = tensor("op_2486_end_0"), val = tensor([2, 576, 1, 320])]; + tensor var_2486_end_mask_0 = const()[name = tensor("op_2486_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2486_cast = slice_by_index(begin = var_2486_begin_0, end = var_2486_end_0, end_mask = var_2486_end_mask_0, x = transpose_23)[name = tensor("op_2486_cast")]; + tensor var_2490_begin_0 = const()[name = tensor("op_2490_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_2490_end_0 = const()[name = tensor("op_2490_end_0"), val = tensor([2, 576, 1, 480])]; + tensor var_2490_end_mask_0 = const()[name = tensor("op_2490_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2490_cast = slice_by_index(begin = var_2490_begin_0, end = var_2490_end_0, end_mask = var_2490_end_mask_0, x = transpose_23)[name = tensor("op_2490_cast")]; + tensor var_2494_begin_0 = const()[name = tensor("op_2494_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_2494_end_0 = const()[name = tensor("op_2494_end_0"), val = tensor([2, 576, 1, 640])]; + tensor var_2494_end_mask_0 = const()[name = tensor("op_2494_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2494_cast = slice_by_index(begin = var_2494_begin_0, end = var_2494_end_0, end_mask = var_2494_end_mask_0, x = transpose_23)[name = tensor("op_2494_cast")]; + tensor var_2498_begin_0 = const()[name = tensor("op_2498_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_2498_end_0 = const()[name = tensor("op_2498_end_0"), val = tensor([2, 576, 1, 800])]; + tensor var_2498_end_mask_0 = const()[name = tensor("op_2498_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2498_cast = slice_by_index(begin = var_2498_begin_0, end = var_2498_end_0, end_mask = var_2498_end_mask_0, x = transpose_23)[name = tensor("op_2498_cast")]; + tensor var_2502_begin_0 = const()[name = tensor("op_2502_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_2502_end_0 = const()[name = tensor("op_2502_end_0"), val = tensor([2, 576, 1, 960])]; + tensor var_2502_end_mask_0 = const()[name = tensor("op_2502_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2502_cast = slice_by_index(begin = var_2502_begin_0, end = var_2502_end_0, end_mask = var_2502_end_mask_0, x = transpose_23)[name = tensor("op_2502_cast")]; + tensor var_2506_begin_0 = const()[name = tensor("op_2506_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_2506_end_0 = const()[name = tensor("op_2506_end_0"), val = tensor([2, 576, 1, 1120])]; + tensor var_2506_end_mask_0 = const()[name = tensor("op_2506_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2506_cast = slice_by_index(begin = var_2506_begin_0, end = var_2506_end_0, end_mask = var_2506_end_mask_0, x = transpose_23)[name = tensor("op_2506_cast")]; + tensor var_2510_begin_0 = const()[name = tensor("op_2510_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_2510_end_0 = const()[name = tensor("op_2510_end_0"), val = tensor([2, 576, 1, 1280])]; + tensor var_2510_end_mask_0 = const()[name = tensor("op_2510_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2510_cast = slice_by_index(begin = var_2510_begin_0, end = var_2510_end_0, end_mask = var_2510_end_mask_0, x = transpose_23)[name = tensor("op_2510_cast")]; + tensor var_2512_begin_0 = const()[name = tensor("op_2512_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2512_end_0 = const()[name = tensor("op_2512_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_2512_end_mask_0 = const()[name = tensor("op_2512_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2512_cast = slice_by_index(begin = var_2512_begin_0, end = var_2512_end_0, end_mask = var_2512_end_mask_0, x = v_17_cast)[name = tensor("op_2512_cast")]; + tensor var_2516_begin_0 = const()[name = tensor("op_2516_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_2516_end_0 = const()[name = tensor("op_2516_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_2516_end_mask_0 = const()[name = tensor("op_2516_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2516_cast = slice_by_index(begin = var_2516_begin_0, end = var_2516_end_0, end_mask = var_2516_end_mask_0, x = v_17_cast)[name = tensor("op_2516_cast")]; + tensor var_2520_begin_0 = const()[name = tensor("op_2520_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_2520_end_0 = const()[name = tensor("op_2520_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_2520_end_mask_0 = const()[name = tensor("op_2520_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2520_cast = slice_by_index(begin = var_2520_begin_0, end = var_2520_end_0, end_mask = var_2520_end_mask_0, x = v_17_cast)[name = tensor("op_2520_cast")]; + tensor var_2524_begin_0 = const()[name = tensor("op_2524_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_2524_end_0 = const()[name = tensor("op_2524_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_2524_end_mask_0 = const()[name = tensor("op_2524_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2524_cast = slice_by_index(begin = var_2524_begin_0, end = var_2524_end_0, end_mask = var_2524_end_mask_0, x = v_17_cast)[name = tensor("op_2524_cast")]; + tensor var_2528_begin_0 = const()[name = tensor("op_2528_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_2528_end_0 = const()[name = tensor("op_2528_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_2528_end_mask_0 = const()[name = tensor("op_2528_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2528_cast = slice_by_index(begin = var_2528_begin_0, end = var_2528_end_0, end_mask = var_2528_end_mask_0, x = v_17_cast)[name = tensor("op_2528_cast")]; + tensor var_2532_begin_0 = const()[name = tensor("op_2532_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_2532_end_0 = const()[name = tensor("op_2532_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_2532_end_mask_0 = const()[name = tensor("op_2532_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2532_cast = slice_by_index(begin = var_2532_begin_0, end = var_2532_end_0, end_mask = var_2532_end_mask_0, x = v_17_cast)[name = tensor("op_2532_cast")]; + tensor var_2536_begin_0 = const()[name = tensor("op_2536_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_2536_end_0 = const()[name = tensor("op_2536_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_2536_end_mask_0 = const()[name = tensor("op_2536_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2536_cast = slice_by_index(begin = var_2536_begin_0, end = var_2536_end_0, end_mask = var_2536_end_mask_0, x = v_17_cast)[name = tensor("op_2536_cast")]; + tensor var_2540_begin_0 = const()[name = tensor("op_2540_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_2540_end_0 = const()[name = tensor("op_2540_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_2540_end_mask_0 = const()[name = tensor("op_2540_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2540_cast = slice_by_index(begin = var_2540_begin_0, end = var_2540_end_0, end_mask = var_2540_end_mask_0, x = v_17_cast)[name = tensor("op_2540_cast")]; + tensor var_2544_equation_0 = const()[name = tensor("op_2544_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2544_cast = einsum(equation = var_2544_equation_0, values = (var_2482_cast, var_2447_cast))[name = tensor("op_2544_cast")]; + tensor var_2545_to_fp16 = const()[name = tensor("op_2545_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_129_cast = mul(x = var_2544_cast, y = var_2545_to_fp16)[name = tensor("aw_129_cast")]; + tensor var_2548_equation_0 = const()[name = tensor("op_2548_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2548_cast = einsum(equation = var_2548_equation_0, values = (var_2486_cast, var_2451_cast))[name = tensor("op_2548_cast")]; + tensor var_2549_to_fp16 = const()[name = tensor("op_2549_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_131_cast = mul(x = var_2548_cast, y = var_2549_to_fp16)[name = tensor("aw_131_cast")]; + tensor var_2552_equation_0 = const()[name = tensor("op_2552_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2552_cast = einsum(equation = var_2552_equation_0, values = (var_2490_cast, var_2455_cast))[name = tensor("op_2552_cast")]; + tensor var_2553_to_fp16 = const()[name = tensor("op_2553_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_133_cast = mul(x = var_2552_cast, y = var_2553_to_fp16)[name = tensor("aw_133_cast")]; + tensor var_2556_equation_0 = const()[name = tensor("op_2556_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2556_cast = einsum(equation = var_2556_equation_0, values = (var_2494_cast, var_2459_cast))[name = tensor("op_2556_cast")]; + tensor var_2557_to_fp16 = const()[name = tensor("op_2557_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_135_cast = mul(x = var_2556_cast, y = var_2557_to_fp16)[name = tensor("aw_135_cast")]; + tensor var_2560_equation_0 = const()[name = tensor("op_2560_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2560_cast = einsum(equation = var_2560_equation_0, values = (var_2498_cast, var_2463_cast))[name = tensor("op_2560_cast")]; + tensor var_2561_to_fp16 = const()[name = tensor("op_2561_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_137_cast = mul(x = var_2560_cast, y = var_2561_to_fp16)[name = tensor("aw_137_cast")]; + tensor var_2564_equation_0 = const()[name = tensor("op_2564_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2564_cast = einsum(equation = var_2564_equation_0, values = (var_2502_cast, var_2467_cast))[name = tensor("op_2564_cast")]; + tensor var_2565_to_fp16 = const()[name = tensor("op_2565_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_139_cast = mul(x = var_2564_cast, y = var_2565_to_fp16)[name = tensor("aw_139_cast")]; + tensor var_2568_equation_0 = const()[name = tensor("op_2568_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2568_cast = einsum(equation = var_2568_equation_0, values = (var_2506_cast, var_2471_cast))[name = tensor("op_2568_cast")]; + tensor var_2569_to_fp16 = const()[name = tensor("op_2569_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_141_cast = mul(x = var_2568_cast, y = var_2569_to_fp16)[name = tensor("aw_141_cast")]; + tensor var_2572_equation_0 = const()[name = tensor("op_2572_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2572_cast = einsum(equation = var_2572_equation_0, values = (var_2510_cast, var_2475_cast))[name = tensor("op_2572_cast")]; + tensor var_2573_to_fp16 = const()[name = tensor("op_2573_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_143_cast = mul(x = var_2572_cast, y = var_2573_to_fp16)[name = tensor("aw_143_cast")]; + tensor var_2575_cast = softmax(axis = var_2319, x = aw_129_cast)[name = tensor("op_2575_cast")]; + tensor var_2576_cast = softmax(axis = var_2319, x = aw_131_cast)[name = tensor("op_2576_cast")]; + tensor var_2577_cast = softmax(axis = var_2319, x = aw_133_cast)[name = tensor("op_2577_cast")]; + tensor var_2578_cast = softmax(axis = var_2319, x = aw_135_cast)[name = tensor("op_2578_cast")]; + tensor var_2579_cast = softmax(axis = var_2319, x = aw_137_cast)[name = tensor("op_2579_cast")]; + tensor var_2580_cast = softmax(axis = var_2319, x = aw_139_cast)[name = tensor("op_2580_cast")]; + tensor var_2581_cast = softmax(axis = var_2319, x = aw_141_cast)[name = tensor("op_2581_cast")]; + tensor var_2582_cast = softmax(axis = var_2319, x = aw_143_cast)[name = tensor("op_2582_cast")]; + tensor var_2584_equation_0 = const()[name = tensor("op_2584_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2584_cast = einsum(equation = var_2584_equation_0, values = (var_2512_cast, var_2575_cast))[name = tensor("op_2584_cast")]; + tensor var_2586_equation_0 = const()[name = tensor("op_2586_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2586_cast = einsum(equation = var_2586_equation_0, values = (var_2516_cast, var_2576_cast))[name = tensor("op_2586_cast")]; + tensor var_2588_equation_0 = const()[name = tensor("op_2588_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2588_cast = einsum(equation = var_2588_equation_0, values = (var_2520_cast, var_2577_cast))[name = tensor("op_2588_cast")]; + tensor var_2590_equation_0 = const()[name = tensor("op_2590_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2590_cast = einsum(equation = var_2590_equation_0, values = (var_2524_cast, var_2578_cast))[name = tensor("op_2590_cast")]; + tensor var_2592_equation_0 = const()[name = tensor("op_2592_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2592_cast = einsum(equation = var_2592_equation_0, values = (var_2528_cast, var_2579_cast))[name = tensor("op_2592_cast")]; + tensor var_2594_equation_0 = const()[name = tensor("op_2594_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2594_cast = einsum(equation = var_2594_equation_0, values = (var_2532_cast, var_2580_cast))[name = tensor("op_2594_cast")]; + tensor var_2596_equation_0 = const()[name = tensor("op_2596_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2596_cast = einsum(equation = var_2596_equation_0, values = (var_2536_cast, var_2581_cast))[name = tensor("op_2596_cast")]; + tensor var_2598_equation_0 = const()[name = tensor("op_2598_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2598_cast = einsum(equation = var_2598_equation_0, values = (var_2540_cast, var_2582_cast))[name = tensor("op_2598_cast")]; + tensor input_133_interleave_0 = const()[name = tensor("input_133_interleave_0"), val = tensor(false)]; + tensor input_133_cast = concat(axis = var_2319, interleave = input_133_interleave_0, values = (var_2584_cast, var_2586_cast, var_2588_cast, var_2590_cast, var_2592_cast, var_2594_cast, var_2596_cast, var_2598_cast))[name = tensor("input_133_cast")]; + tensor var_2604 = const()[name = tensor("op_2604"), val = tensor([1, 1])]; + tensor var_2606 = const()[name = tensor("op_2606"), val = tensor([1, 1])]; + tensor var_2608_pad_type_0 = const()[name = tensor("op_2608_pad_type_0"), val = tensor("custom")]; + tensor var_2608_pad_0 = const()[name = tensor("op_2608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60520896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61749760))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61749952)))]; + tensor var_2608_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2606, groups = var_2319, pad = var_2608_pad_0, pad_type = var_2608_pad_type_0, strides = var_2604, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_133_cast)[name = tensor("op_2608_cast")]; + tensor inputs_27_cast = add(x = var_2608_cast, y = inputs_25_cast)[name = tensor("inputs_27_cast")]; + tensor var_2612 = const()[name = tensor("op_2612"), val = tensor([1])]; + tensor channels_mean_27_cast = reduce_mean(axes = var_2612, keep_dims = var_2314, x = inputs_27_cast)[name = tensor("channels_mean_27_cast")]; + tensor zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast)[name = tensor("zero_mean_27_cast")]; + tensor zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast)[name = tensor("zero_mean_sq_27_cast")]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1])]; + tensor var_2617_cast = reduce_mean(axes = var_2616, keep_dims = var_2314, x = zero_mean_sq_27_cast)[name = tensor("op_2617_cast")]; + tensor var_2618_to_fp16 = const()[name = tensor("op_2618_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2619_cast = add(x = var_2617_cast, y = var_2618_to_fp16)[name = tensor("op_2619_cast")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_2619_cast)[name = tensor("denom_27_cast")]; + tensor out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast)[name = tensor("out_27_cast")]; + tensor var_2623_to_fp16 = const()[name = tensor("op_2623_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61752576)))]; + tensor var_2624_cast = add(x = out_27_cast, y = var_2623_to_fp16)[name = tensor("op_2624_cast")]; + tensor var_2626_to_fp16 = const()[name = tensor("op_2626_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61755200)))]; + tensor hidden_states_83_cast = mul(x = var_2624_cast, y = var_2626_to_fp16)[name = tensor("hidden_states_83_cast")]; + tensor var_2633 = const()[name = tensor("op_2633"), val = tensor([1, 1])]; + tensor var_2635 = const()[name = tensor("op_2635"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61757824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62986688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_19_cast = conv(dilations = var_2635, groups = var_2319, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_2633, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_83_cast)[name = tensor("q_19_cast")]; + tensor var_2639 = const()[name = tensor("op_2639"), val = tensor([1, 1])]; + tensor var_2641 = const()[name = tensor("op_2641"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62986880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63724224))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_37_cast = conv(dilations = var_2641, groups = var_2319, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2639, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_37_cast")]; + tensor var_2645 = const()[name = tensor("op_2645"), val = tensor([1, 1])]; + tensor var_2647 = const()[name = tensor("op_2647"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63724416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64461760))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_19_cast = conv(dilations = var_2647, groups = var_2319, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_2645, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_19_cast")]; + tensor var_2651_begin_0 = const()[name = tensor("op_2651_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2651_end_0 = const()[name = tensor("op_2651_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_2651_end_mask_0 = const()[name = tensor("op_2651_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2651_cast = slice_by_index(begin = var_2651_begin_0, end = var_2651_end_0, end_mask = var_2651_end_mask_0, x = q_19_cast)[name = tensor("op_2651_cast")]; + tensor var_2655_begin_0 = const()[name = tensor("op_2655_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_2655_end_0 = const()[name = tensor("op_2655_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_2655_end_mask_0 = const()[name = tensor("op_2655_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2655_cast = slice_by_index(begin = var_2655_begin_0, end = var_2655_end_0, end_mask = var_2655_end_mask_0, x = q_19_cast)[name = tensor("op_2655_cast")]; + tensor var_2659_begin_0 = const()[name = tensor("op_2659_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_2659_end_0 = const()[name = tensor("op_2659_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_2659_end_mask_0 = const()[name = tensor("op_2659_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2659_cast = slice_by_index(begin = var_2659_begin_0, end = var_2659_end_0, end_mask = var_2659_end_mask_0, x = q_19_cast)[name = tensor("op_2659_cast")]; + tensor var_2663_begin_0 = const()[name = tensor("op_2663_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_2663_end_0 = const()[name = tensor("op_2663_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_2663_end_mask_0 = const()[name = tensor("op_2663_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2663_cast = slice_by_index(begin = var_2663_begin_0, end = var_2663_end_0, end_mask = var_2663_end_mask_0, x = q_19_cast)[name = tensor("op_2663_cast")]; + tensor var_2667_begin_0 = const()[name = tensor("op_2667_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_2667_end_0 = const()[name = tensor("op_2667_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_2667_end_mask_0 = const()[name = tensor("op_2667_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2667_cast = slice_by_index(begin = var_2667_begin_0, end = var_2667_end_0, end_mask = var_2667_end_mask_0, x = q_19_cast)[name = tensor("op_2667_cast")]; + tensor var_2671_begin_0 = const()[name = tensor("op_2671_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_2671_end_0 = const()[name = tensor("op_2671_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_2671_end_mask_0 = const()[name = tensor("op_2671_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2671_cast = slice_by_index(begin = var_2671_begin_0, end = var_2671_end_0, end_mask = var_2671_end_mask_0, x = q_19_cast)[name = tensor("op_2671_cast")]; + tensor var_2675_begin_0 = const()[name = tensor("op_2675_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_2675_end_0 = const()[name = tensor("op_2675_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_2675_end_mask_0 = const()[name = tensor("op_2675_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2675_cast = slice_by_index(begin = var_2675_begin_0, end = var_2675_end_0, end_mask = var_2675_end_mask_0, x = q_19_cast)[name = tensor("op_2675_cast")]; + tensor var_2679_begin_0 = const()[name = tensor("op_2679_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_2679_end_0 = const()[name = tensor("op_2679_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_2679_end_mask_0 = const()[name = tensor("op_2679_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2679_cast = slice_by_index(begin = var_2679_begin_0, end = var_2679_end_0, end_mask = var_2679_end_mask_0, x = q_19_cast)[name = tensor("op_2679_cast")]; + tensor k_39_perm_0 = const()[name = tensor("k_39_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_2686_begin_0 = const()[name = tensor("op_2686_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2686_end_0 = const()[name = tensor("op_2686_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_2686_end_mask_0 = const()[name = tensor("op_2686_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_22 = transpose(perm = k_39_perm_0, x = k_37_cast)[name = tensor("transpose_22")]; + tensor var_2686_cast = slice_by_index(begin = var_2686_begin_0, end = var_2686_end_0, end_mask = var_2686_end_mask_0, x = transpose_22)[name = tensor("op_2686_cast")]; + tensor var_2690_begin_0 = const()[name = tensor("op_2690_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_2690_end_0 = const()[name = tensor("op_2690_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_2690_end_mask_0 = const()[name = tensor("op_2690_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2690_cast = slice_by_index(begin = var_2690_begin_0, end = var_2690_end_0, end_mask = var_2690_end_mask_0, x = transpose_22)[name = tensor("op_2690_cast")]; + tensor var_2694_begin_0 = const()[name = tensor("op_2694_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_2694_end_0 = const()[name = tensor("op_2694_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_2694_end_mask_0 = const()[name = tensor("op_2694_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2694_cast = slice_by_index(begin = var_2694_begin_0, end = var_2694_end_0, end_mask = var_2694_end_mask_0, x = transpose_22)[name = tensor("op_2694_cast")]; + tensor var_2698_begin_0 = const()[name = tensor("op_2698_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_2698_end_0 = const()[name = tensor("op_2698_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_2698_end_mask_0 = const()[name = tensor("op_2698_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2698_cast = slice_by_index(begin = var_2698_begin_0, end = var_2698_end_0, end_mask = var_2698_end_mask_0, x = transpose_22)[name = tensor("op_2698_cast")]; + tensor var_2702_begin_0 = const()[name = tensor("op_2702_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_2702_end_0 = const()[name = tensor("op_2702_end_0"), val = tensor([2, 77, 1, 800])]; + tensor var_2702_end_mask_0 = const()[name = tensor("op_2702_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2702_cast = slice_by_index(begin = var_2702_begin_0, end = var_2702_end_0, end_mask = var_2702_end_mask_0, x = transpose_22)[name = tensor("op_2702_cast")]; + tensor var_2706_begin_0 = const()[name = tensor("op_2706_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_2706_end_0 = const()[name = tensor("op_2706_end_0"), val = tensor([2, 77, 1, 960])]; + tensor var_2706_end_mask_0 = const()[name = tensor("op_2706_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2706_cast = slice_by_index(begin = var_2706_begin_0, end = var_2706_end_0, end_mask = var_2706_end_mask_0, x = transpose_22)[name = tensor("op_2706_cast")]; + tensor var_2710_begin_0 = const()[name = tensor("op_2710_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_2710_end_0 = const()[name = tensor("op_2710_end_0"), val = tensor([2, 77, 1, 1120])]; + tensor var_2710_end_mask_0 = const()[name = tensor("op_2710_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2710_cast = slice_by_index(begin = var_2710_begin_0, end = var_2710_end_0, end_mask = var_2710_end_mask_0, x = transpose_22)[name = tensor("op_2710_cast")]; + tensor var_2714_begin_0 = const()[name = tensor("op_2714_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_2714_end_0 = const()[name = tensor("op_2714_end_0"), val = tensor([2, 77, 1, 1280])]; + tensor var_2714_end_mask_0 = const()[name = tensor("op_2714_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_2714_cast = slice_by_index(begin = var_2714_begin_0, end = var_2714_end_0, end_mask = var_2714_end_mask_0, x = transpose_22)[name = tensor("op_2714_cast")]; + tensor var_2716_begin_0 = const()[name = tensor("op_2716_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2716_end_0 = const()[name = tensor("op_2716_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_2716_end_mask_0 = const()[name = tensor("op_2716_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2716_cast = slice_by_index(begin = var_2716_begin_0, end = var_2716_end_0, end_mask = var_2716_end_mask_0, x = v_19_cast)[name = tensor("op_2716_cast")]; + tensor var_2720_begin_0 = const()[name = tensor("op_2720_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_2720_end_0 = const()[name = tensor("op_2720_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_2720_end_mask_0 = const()[name = tensor("op_2720_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2720_cast = slice_by_index(begin = var_2720_begin_0, end = var_2720_end_0, end_mask = var_2720_end_mask_0, x = v_19_cast)[name = tensor("op_2720_cast")]; + tensor var_2724_begin_0 = const()[name = tensor("op_2724_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_2724_end_0 = const()[name = tensor("op_2724_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_2724_end_mask_0 = const()[name = tensor("op_2724_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2724_cast = slice_by_index(begin = var_2724_begin_0, end = var_2724_end_0, end_mask = var_2724_end_mask_0, x = v_19_cast)[name = tensor("op_2724_cast")]; + tensor var_2728_begin_0 = const()[name = tensor("op_2728_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_2728_end_0 = const()[name = tensor("op_2728_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_2728_end_mask_0 = const()[name = tensor("op_2728_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2728_cast = slice_by_index(begin = var_2728_begin_0, end = var_2728_end_0, end_mask = var_2728_end_mask_0, x = v_19_cast)[name = tensor("op_2728_cast")]; + tensor var_2732_begin_0 = const()[name = tensor("op_2732_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_2732_end_0 = const()[name = tensor("op_2732_end_0"), val = tensor([2, 800, 1, 77])]; + tensor var_2732_end_mask_0 = const()[name = tensor("op_2732_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2732_cast = slice_by_index(begin = var_2732_begin_0, end = var_2732_end_0, end_mask = var_2732_end_mask_0, x = v_19_cast)[name = tensor("op_2732_cast")]; + tensor var_2736_begin_0 = const()[name = tensor("op_2736_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_2736_end_0 = const()[name = tensor("op_2736_end_0"), val = tensor([2, 960, 1, 77])]; + tensor var_2736_end_mask_0 = const()[name = tensor("op_2736_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2736_cast = slice_by_index(begin = var_2736_begin_0, end = var_2736_end_0, end_mask = var_2736_end_mask_0, x = v_19_cast)[name = tensor("op_2736_cast")]; + tensor var_2740_begin_0 = const()[name = tensor("op_2740_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_2740_end_0 = const()[name = tensor("op_2740_end_0"), val = tensor([2, 1120, 1, 77])]; + tensor var_2740_end_mask_0 = const()[name = tensor("op_2740_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2740_cast = slice_by_index(begin = var_2740_begin_0, end = var_2740_end_0, end_mask = var_2740_end_mask_0, x = v_19_cast)[name = tensor("op_2740_cast")]; + tensor var_2744_begin_0 = const()[name = tensor("op_2744_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_2744_end_0 = const()[name = tensor("op_2744_end_0"), val = tensor([2, 1280, 1, 77])]; + tensor var_2744_end_mask_0 = const()[name = tensor("op_2744_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2744_cast = slice_by_index(begin = var_2744_begin_0, end = var_2744_end_0, end_mask = var_2744_end_mask_0, x = v_19_cast)[name = tensor("op_2744_cast")]; + tensor var_2748_equation_0 = const()[name = tensor("op_2748_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2748_cast = einsum(equation = var_2748_equation_0, values = (var_2686_cast, var_2651_cast))[name = tensor("op_2748_cast")]; + tensor var_2749_to_fp16 = const()[name = tensor("op_2749_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_145_cast = mul(x = var_2748_cast, y = var_2749_to_fp16)[name = tensor("aw_145_cast")]; + tensor var_2752_equation_0 = const()[name = tensor("op_2752_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2752_cast = einsum(equation = var_2752_equation_0, values = (var_2690_cast, var_2655_cast))[name = tensor("op_2752_cast")]; + tensor var_2753_to_fp16 = const()[name = tensor("op_2753_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_147_cast = mul(x = var_2752_cast, y = var_2753_to_fp16)[name = tensor("aw_147_cast")]; + tensor var_2756_equation_0 = const()[name = tensor("op_2756_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2756_cast = einsum(equation = var_2756_equation_0, values = (var_2694_cast, var_2659_cast))[name = tensor("op_2756_cast")]; + tensor var_2757_to_fp16 = const()[name = tensor("op_2757_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_149_cast = mul(x = var_2756_cast, y = var_2757_to_fp16)[name = tensor("aw_149_cast")]; + tensor var_2760_equation_0 = const()[name = tensor("op_2760_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2760_cast = einsum(equation = var_2760_equation_0, values = (var_2698_cast, var_2663_cast))[name = tensor("op_2760_cast")]; + tensor var_2761_to_fp16 = const()[name = tensor("op_2761_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_151_cast = mul(x = var_2760_cast, y = var_2761_to_fp16)[name = tensor("aw_151_cast")]; + tensor var_2764_equation_0 = const()[name = tensor("op_2764_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2764_cast = einsum(equation = var_2764_equation_0, values = (var_2702_cast, var_2667_cast))[name = tensor("op_2764_cast")]; + tensor var_2765_to_fp16 = const()[name = tensor("op_2765_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_153_cast = mul(x = var_2764_cast, y = var_2765_to_fp16)[name = tensor("aw_153_cast")]; + tensor var_2768_equation_0 = const()[name = tensor("op_2768_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2768_cast = einsum(equation = var_2768_equation_0, values = (var_2706_cast, var_2671_cast))[name = tensor("op_2768_cast")]; + tensor var_2769_to_fp16 = const()[name = tensor("op_2769_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_155_cast = mul(x = var_2768_cast, y = var_2769_to_fp16)[name = tensor("aw_155_cast")]; + tensor var_2772_equation_0 = const()[name = tensor("op_2772_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2772_cast = einsum(equation = var_2772_equation_0, values = (var_2710_cast, var_2675_cast))[name = tensor("op_2772_cast")]; + tensor var_2773_to_fp16 = const()[name = tensor("op_2773_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_157_cast = mul(x = var_2772_cast, y = var_2773_to_fp16)[name = tensor("aw_157_cast")]; + tensor var_2776_equation_0 = const()[name = tensor("op_2776_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_2776_cast = einsum(equation = var_2776_equation_0, values = (var_2714_cast, var_2679_cast))[name = tensor("op_2776_cast")]; + tensor var_2777_to_fp16 = const()[name = tensor("op_2777_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_159_cast = mul(x = var_2776_cast, y = var_2777_to_fp16)[name = tensor("aw_159_cast")]; + tensor var_2779_cast = softmax(axis = var_2319, x = aw_145_cast)[name = tensor("op_2779_cast")]; + tensor var_2780_cast = softmax(axis = var_2319, x = aw_147_cast)[name = tensor("op_2780_cast")]; + tensor var_2781_cast = softmax(axis = var_2319, x = aw_149_cast)[name = tensor("op_2781_cast")]; + tensor var_2782_cast = softmax(axis = var_2319, x = aw_151_cast)[name = tensor("op_2782_cast")]; + tensor var_2783_cast = softmax(axis = var_2319, x = aw_153_cast)[name = tensor("op_2783_cast")]; + tensor var_2784_cast = softmax(axis = var_2319, x = aw_155_cast)[name = tensor("op_2784_cast")]; + tensor var_2785_cast = softmax(axis = var_2319, x = aw_157_cast)[name = tensor("op_2785_cast")]; + tensor var_2786_cast = softmax(axis = var_2319, x = aw_159_cast)[name = tensor("op_2786_cast")]; + tensor var_2788_equation_0 = const()[name = tensor("op_2788_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2788_cast = einsum(equation = var_2788_equation_0, values = (var_2716_cast, var_2779_cast))[name = tensor("op_2788_cast")]; + tensor var_2790_equation_0 = const()[name = tensor("op_2790_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2790_cast = einsum(equation = var_2790_equation_0, values = (var_2720_cast, var_2780_cast))[name = tensor("op_2790_cast")]; + tensor var_2792_equation_0 = const()[name = tensor("op_2792_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2792_cast = einsum(equation = var_2792_equation_0, values = (var_2724_cast, var_2781_cast))[name = tensor("op_2792_cast")]; + tensor var_2794_equation_0 = const()[name = tensor("op_2794_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2794_cast = einsum(equation = var_2794_equation_0, values = (var_2728_cast, var_2782_cast))[name = tensor("op_2794_cast")]; + tensor var_2796_equation_0 = const()[name = tensor("op_2796_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2796_cast = einsum(equation = var_2796_equation_0, values = (var_2732_cast, var_2783_cast))[name = tensor("op_2796_cast")]; + tensor var_2798_equation_0 = const()[name = tensor("op_2798_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2798_cast = einsum(equation = var_2798_equation_0, values = (var_2736_cast, var_2784_cast))[name = tensor("op_2798_cast")]; + tensor var_2800_equation_0 = const()[name = tensor("op_2800_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2800_cast = einsum(equation = var_2800_equation_0, values = (var_2740_cast, var_2785_cast))[name = tensor("op_2800_cast")]; + tensor var_2802_equation_0 = const()[name = tensor("op_2802_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_2802_cast = einsum(equation = var_2802_equation_0, values = (var_2744_cast, var_2786_cast))[name = tensor("op_2802_cast")]; + tensor input_135_interleave_0 = const()[name = tensor("input_135_interleave_0"), val = tensor(false)]; + tensor input_135_cast = concat(axis = var_2319, interleave = input_135_interleave_0, values = (var_2788_cast, var_2790_cast, var_2792_cast, var_2794_cast, var_2796_cast, var_2798_cast, var_2800_cast, var_2802_cast))[name = tensor("input_135_cast")]; + tensor var_2808 = const()[name = tensor("op_2808"), val = tensor([1, 1])]; + tensor var_2810 = const()[name = tensor("op_2810"), val = tensor([1, 1])]; + tensor var_2812_pad_type_0 = const()[name = tensor("op_2812_pad_type_0"), val = tensor("custom")]; + tensor var_2812_pad_0 = const()[name = tensor("op_2812_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64461952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65690816))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65691008)))]; + tensor var_2812_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2810, groups = var_2319, pad = var_2812_pad_0, pad_type = var_2812_pad_type_0, strides = var_2808, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_135_cast)[name = tensor("op_2812_cast")]; + tensor inputs_29_cast = add(x = var_2812_cast, y = inputs_27_cast)[name = tensor("inputs_29_cast")]; + tensor var_2816 = const()[name = tensor("op_2816"), val = tensor([1])]; + tensor channels_mean_29_cast = reduce_mean(axes = var_2816, keep_dims = var_2314, x = inputs_29_cast)[name = tensor("channels_mean_29_cast")]; + tensor zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast)[name = tensor("zero_mean_29_cast")]; + tensor zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast)[name = tensor("zero_mean_sq_29_cast")]; + tensor var_2820 = const()[name = tensor("op_2820"), val = tensor([1])]; + tensor var_2821_cast = reduce_mean(axes = var_2820, keep_dims = var_2314, x = zero_mean_sq_29_cast)[name = tensor("op_2821_cast")]; + tensor var_2822_to_fp16 = const()[name = tensor("op_2822_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2823_cast = add(x = var_2821_cast, y = var_2822_to_fp16)[name = tensor("op_2823_cast")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_2823_cast)[name = tensor("denom_29_cast")]; + tensor out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast)[name = tensor("out_29_cast")]; + tensor var_2827_to_fp16 = const()[name = tensor("op_2827_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65693632)))]; + tensor var_2828_cast = add(x = out_29_cast, y = var_2827_to_fp16)[name = tensor("op_2828_cast")]; + tensor var_2830_to_fp16 = const()[name = tensor("op_2830_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65696256)))]; + tensor input_137_cast = mul(x = var_2828_cast, y = var_2830_to_fp16)[name = tensor("input_137_cast")]; + tensor var_2838 = const()[name = tensor("op_2838"), val = tensor([1, 1])]; + tensor var_2840 = const()[name = tensor("op_2840"), val = tensor([1, 1])]; + tensor var_2842_pad_type_0 = const()[name = tensor("op_2842_pad_type_0"), val = tensor("custom")]; + tensor var_2842_pad_0 = const()[name = tensor("op_2842_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65698880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75529344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75529536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75537280))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2842_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2840, groups = var_2319, pad = var_2842_pad_0, pad_type = var_2842_pad_type_0, strides = var_2838, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_137_cast)[name = tensor("op_2842_cast")]; + tensor var_2843_split_sizes_0 = const()[name = tensor("op_2843_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2843_axis_0 = const()[name = tensor("op_2843_axis_0"), val = tensor(1)]; + tensor var_2843_cast_0, tensor var_2843_cast_1 = split(axis = var_2843_axis_0, split_sizes = var_2843_split_sizes_0, x = var_2842_cast)[name = tensor("op_2843_cast")]; + tensor var_2845_mode_0 = const()[name = tensor("op_2845_mode_0"), val = tensor("EXACT")]; + tensor var_2845_cast = gelu(mode = var_2845_mode_0, x = var_2843_cast_1)[name = tensor("op_2845_cast")]; + tensor input_139_cast = mul(x = var_2843_cast_0, y = var_2845_cast)[name = tensor("input_139_cast")]; + tensor var_2849 = const()[name = tensor("op_2849"), val = tensor([1, 1])]; + tensor var_2851 = const()[name = tensor("op_2851"), val = tensor([1, 1])]; + tensor var_2853_pad_type_0 = const()[name = tensor("op_2853_pad_type_0"), val = tensor("custom")]; + tensor var_2853_pad_0 = const()[name = tensor("op_2853_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75537472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80452736))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80452928)))]; + tensor var_2853_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2851, groups = var_2319, pad = var_2853_pad_0, pad_type = var_2853_pad_type_0, strides = var_2849, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_139_cast)[name = tensor("op_2853_cast")]; + tensor hidden_states_87_cast = add(x = var_2853_cast, y = inputs_29_cast)[name = tensor("hidden_states_87_cast")]; + tensor var_2855 = const()[name = tensor("op_2855"), val = tensor([2, 1280, 24, 24])]; + tensor input_141_cast = reshape(shape = var_2855, x = hidden_states_87_cast)[name = tensor("input_141_cast")]; + tensor var_2859 = const()[name = tensor("op_2859"), val = tensor([1, 1])]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor hidden_states_89_pad_type_0 = const()[name = tensor("hidden_states_89_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_89_pad_0 = const()[name = tensor("hidden_states_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80455552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81684416))), name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81684608)))]; + tensor hidden_states_89_cast = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_2861, groups = var_2319, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_2859, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_141_cast)[name = tensor("hidden_states_89_cast")]; + tensor input_143_cast = add(x = hidden_states_89_cast, y = hidden_states_77_cast)[name = tensor("input_143_cast")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_143_cast)[name = tensor("reshape_60_cast")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_47_cast")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81687232)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81689856)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_31_cast")]; + tensor input_147_cast = silu(x = add_31_cast)[name = tensor("input_147_cast")]; + tensor var_2876 = const()[name = tensor("op_2876"), val = tensor([1, 1])]; + tensor var_2878 = const()[name = tensor("op_2878"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_type_0 = const()[name = tensor("hidden_states_91_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_91_pad_0 = const()[name = tensor("hidden_states_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81692480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92751744))), name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92751936)))]; + tensor hidden_states_91_cast = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_2878, groups = var_2319, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_2876, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_147_cast)[name = tensor("hidden_states_91_cast")]; + tensor var_2884 = const()[name = tensor("op_2884"), val = tensor([1, 1])]; + tensor var_2886 = const()[name = tensor("op_2886"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92754560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93983424))), name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93983616)))]; + tensor temb_11_cast = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2886, groups = var_2319, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_2884, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_11_cast")]; + tensor input_151_cast = add(x = hidden_states_91_cast, y = temb_11_cast)[name = tensor("input_151_cast")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_151_cast)[name = tensor("reshape_64_cast")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_50_cast")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93986240)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93988864)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_33_cast")]; + tensor input_155_cast = silu(x = add_33_cast)[name = tensor("input_155_cast")]; + tensor var_2896 = const()[name = tensor("op_2896"), val = tensor([1, 1])]; + tensor var_2898 = const()[name = tensor("op_2898"), val = tensor([1, 1])]; + tensor hidden_states_93_pad_type_0 = const()[name = tensor("hidden_states_93_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_93_pad_0 = const()[name = tensor("hidden_states_93_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93991488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105050752))), name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105050944)))]; + tensor hidden_states_93_cast = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_2898, groups = var_2319, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_2896, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_155_cast)[name = tensor("hidden_states_93_cast")]; + tensor hidden_states_95_cast = add(x = input_143_cast, y = hidden_states_93_cast)[name = tensor("hidden_states_95_cast")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = hidden_states_95_cast)[name = tensor("reshape_68_cast")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_53_cast")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105053568)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105056192)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_35_cast")]; + tensor var_2918 = const()[name = tensor("op_2918"), val = tensor([1, 1])]; + tensor var_2920 = const()[name = tensor("op_2920"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105058816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106287680))), name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106287872)))]; + tensor hidden_states_97_cast = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_2920, groups = var_2319, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_2918, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_35_cast)[name = tensor("hidden_states_97_cast")]; + tensor var_2925 = const()[name = tensor("op_2925"), val = tensor([2, 1280, 1, 576])]; + tensor inputs_31_cast = reshape(shape = var_2925, x = hidden_states_97_cast)[name = tensor("inputs_31_cast")]; + tensor var_2935 = const()[name = tensor("op_2935"), val = tensor([1])]; + tensor channels_mean_31_cast = reduce_mean(axes = var_2935, keep_dims = var_2314, x = inputs_31_cast)[name = tensor("channels_mean_31_cast")]; + tensor zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast)[name = tensor("zero_mean_31_cast")]; + tensor zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast)[name = tensor("zero_mean_sq_31_cast")]; + tensor var_2939 = const()[name = tensor("op_2939"), val = tensor([1])]; + tensor var_2940_cast = reduce_mean(axes = var_2939, keep_dims = var_2314, x = zero_mean_sq_31_cast)[name = tensor("op_2940_cast")]; + tensor var_2941_to_fp16 = const()[name = tensor("op_2941_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2942_cast = add(x = var_2940_cast, y = var_2941_to_fp16)[name = tensor("op_2942_cast")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_2942_cast)[name = tensor("denom_31_cast")]; + tensor out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast)[name = tensor("out_31_cast")]; + tensor var_2946_to_fp16 = const()[name = tensor("op_2946_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106290496)))]; + tensor var_2947_cast = add(x = out_31_cast, y = var_2946_to_fp16)[name = tensor("op_2947_cast")]; + tensor var_2949_to_fp16 = const()[name = tensor("op_2949_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106293120)))]; + tensor hidden_states_99_cast = mul(x = var_2947_cast, y = var_2949_to_fp16)[name = tensor("hidden_states_99_cast")]; + tensor var_2956 = const()[name = tensor("op_2956"), val = tensor([1, 1])]; + tensor var_2958 = const()[name = tensor("op_2958"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106295744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107524608))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_21_cast = conv(dilations = var_2958, groups = var_2319, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_2956, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor("q_21_cast")]; + tensor var_2962 = const()[name = tensor("op_2962"), val = tensor([1, 1])]; + tensor var_2964 = const()[name = tensor("op_2964"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107524800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108753664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_41_cast = conv(dilations = var_2964, groups = var_2319, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2962, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor("k_41_cast")]; + tensor var_2968 = const()[name = tensor("op_2968"), val = tensor([1, 1])]; + tensor var_2970 = const()[name = tensor("op_2970"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108753856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109982720))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_21_cast = conv(dilations = var_2970, groups = var_2319, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_2968, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor("v_21_cast")]; + tensor var_2974_begin_0 = const()[name = tensor("op_2974_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_2974_end_0 = const()[name = tensor("op_2974_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_2974_end_mask_0 = const()[name = tensor("op_2974_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2974_cast = slice_by_index(begin = var_2974_begin_0, end = var_2974_end_0, end_mask = var_2974_end_mask_0, x = q_21_cast)[name = tensor("op_2974_cast")]; + tensor var_2978_begin_0 = const()[name = tensor("op_2978_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_2978_end_0 = const()[name = tensor("op_2978_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_2978_end_mask_0 = const()[name = tensor("op_2978_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2978_cast = slice_by_index(begin = var_2978_begin_0, end = var_2978_end_0, end_mask = var_2978_end_mask_0, x = q_21_cast)[name = tensor("op_2978_cast")]; + tensor var_2982_begin_0 = const()[name = tensor("op_2982_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_2982_end_0 = const()[name = tensor("op_2982_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_2982_end_mask_0 = const()[name = tensor("op_2982_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2982_cast = slice_by_index(begin = var_2982_begin_0, end = var_2982_end_0, end_mask = var_2982_end_mask_0, x = q_21_cast)[name = tensor("op_2982_cast")]; + tensor var_2986_begin_0 = const()[name = tensor("op_2986_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_2986_end_0 = const()[name = tensor("op_2986_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_2986_end_mask_0 = const()[name = tensor("op_2986_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2986_cast = slice_by_index(begin = var_2986_begin_0, end = var_2986_end_0, end_mask = var_2986_end_mask_0, x = q_21_cast)[name = tensor("op_2986_cast")]; + tensor var_2990_begin_0 = const()[name = tensor("op_2990_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_2990_end_0 = const()[name = tensor("op_2990_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_2990_end_mask_0 = const()[name = tensor("op_2990_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2990_cast = slice_by_index(begin = var_2990_begin_0, end = var_2990_end_0, end_mask = var_2990_end_mask_0, x = q_21_cast)[name = tensor("op_2990_cast")]; + tensor var_2994_begin_0 = const()[name = tensor("op_2994_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_2994_end_0 = const()[name = tensor("op_2994_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_2994_end_mask_0 = const()[name = tensor("op_2994_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2994_cast = slice_by_index(begin = var_2994_begin_0, end = var_2994_end_0, end_mask = var_2994_end_mask_0, x = q_21_cast)[name = tensor("op_2994_cast")]; + tensor var_2998_begin_0 = const()[name = tensor("op_2998_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_2998_end_0 = const()[name = tensor("op_2998_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_2998_end_mask_0 = const()[name = tensor("op_2998_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_2998_cast = slice_by_index(begin = var_2998_begin_0, end = var_2998_end_0, end_mask = var_2998_end_mask_0, x = q_21_cast)[name = tensor("op_2998_cast")]; + tensor var_3002_begin_0 = const()[name = tensor("op_3002_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3002_end_0 = const()[name = tensor("op_3002_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_3002_end_mask_0 = const()[name = tensor("op_3002_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3002_cast = slice_by_index(begin = var_3002_begin_0, end = var_3002_end_0, end_mask = var_3002_end_mask_0, x = q_21_cast)[name = tensor("op_3002_cast")]; + tensor k_43_perm_0 = const()[name = tensor("k_43_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_3009_begin_0 = const()[name = tensor("op_3009_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3009_end_0 = const()[name = tensor("op_3009_end_0"), val = tensor([2, 576, 1, 160])]; + tensor var_3009_end_mask_0 = const()[name = tensor("op_3009_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_21 = transpose(perm = k_43_perm_0, x = k_41_cast)[name = tensor("transpose_21")]; + tensor var_3009_cast = slice_by_index(begin = var_3009_begin_0, end = var_3009_end_0, end_mask = var_3009_end_mask_0, x = transpose_21)[name = tensor("op_3009_cast")]; + tensor var_3013_begin_0 = const()[name = tensor("op_3013_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_3013_end_0 = const()[name = tensor("op_3013_end_0"), val = tensor([2, 576, 1, 320])]; + tensor var_3013_end_mask_0 = const()[name = tensor("op_3013_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3013_cast = slice_by_index(begin = var_3013_begin_0, end = var_3013_end_0, end_mask = var_3013_end_mask_0, x = transpose_21)[name = tensor("op_3013_cast")]; + tensor var_3017_begin_0 = const()[name = tensor("op_3017_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_3017_end_0 = const()[name = tensor("op_3017_end_0"), val = tensor([2, 576, 1, 480])]; + tensor var_3017_end_mask_0 = const()[name = tensor("op_3017_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3017_cast = slice_by_index(begin = var_3017_begin_0, end = var_3017_end_0, end_mask = var_3017_end_mask_0, x = transpose_21)[name = tensor("op_3017_cast")]; + tensor var_3021_begin_0 = const()[name = tensor("op_3021_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_3021_end_0 = const()[name = tensor("op_3021_end_0"), val = tensor([2, 576, 1, 640])]; + tensor var_3021_end_mask_0 = const()[name = tensor("op_3021_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3021_cast = slice_by_index(begin = var_3021_begin_0, end = var_3021_end_0, end_mask = var_3021_end_mask_0, x = transpose_21)[name = tensor("op_3021_cast")]; + tensor var_3025_begin_0 = const()[name = tensor("op_3025_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_3025_end_0 = const()[name = tensor("op_3025_end_0"), val = tensor([2, 576, 1, 800])]; + tensor var_3025_end_mask_0 = const()[name = tensor("op_3025_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3025_cast = slice_by_index(begin = var_3025_begin_0, end = var_3025_end_0, end_mask = var_3025_end_mask_0, x = transpose_21)[name = tensor("op_3025_cast")]; + tensor var_3029_begin_0 = const()[name = tensor("op_3029_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_3029_end_0 = const()[name = tensor("op_3029_end_0"), val = tensor([2, 576, 1, 960])]; + tensor var_3029_end_mask_0 = const()[name = tensor("op_3029_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3029_cast = slice_by_index(begin = var_3029_begin_0, end = var_3029_end_0, end_mask = var_3029_end_mask_0, x = transpose_21)[name = tensor("op_3029_cast")]; + tensor var_3033_begin_0 = const()[name = tensor("op_3033_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_3033_end_0 = const()[name = tensor("op_3033_end_0"), val = tensor([2, 576, 1, 1120])]; + tensor var_3033_end_mask_0 = const()[name = tensor("op_3033_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3033_cast = slice_by_index(begin = var_3033_begin_0, end = var_3033_end_0, end_mask = var_3033_end_mask_0, x = transpose_21)[name = tensor("op_3033_cast")]; + tensor var_3037_begin_0 = const()[name = tensor("op_3037_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_3037_end_0 = const()[name = tensor("op_3037_end_0"), val = tensor([2, 576, 1, 1280])]; + tensor var_3037_end_mask_0 = const()[name = tensor("op_3037_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3037_cast = slice_by_index(begin = var_3037_begin_0, end = var_3037_end_0, end_mask = var_3037_end_mask_0, x = transpose_21)[name = tensor("op_3037_cast")]; + tensor var_3039_begin_0 = const()[name = tensor("op_3039_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3039_end_0 = const()[name = tensor("op_3039_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_3039_end_mask_0 = const()[name = tensor("op_3039_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3039_cast = slice_by_index(begin = var_3039_begin_0, end = var_3039_end_0, end_mask = var_3039_end_mask_0, x = v_21_cast)[name = tensor("op_3039_cast")]; + tensor var_3043_begin_0 = const()[name = tensor("op_3043_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_3043_end_0 = const()[name = tensor("op_3043_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_3043_end_mask_0 = const()[name = tensor("op_3043_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3043_cast = slice_by_index(begin = var_3043_begin_0, end = var_3043_end_0, end_mask = var_3043_end_mask_0, x = v_21_cast)[name = tensor("op_3043_cast")]; + tensor var_3047_begin_0 = const()[name = tensor("op_3047_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_3047_end_0 = const()[name = tensor("op_3047_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_3047_end_mask_0 = const()[name = tensor("op_3047_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3047_cast = slice_by_index(begin = var_3047_begin_0, end = var_3047_end_0, end_mask = var_3047_end_mask_0, x = v_21_cast)[name = tensor("op_3047_cast")]; + tensor var_3051_begin_0 = const()[name = tensor("op_3051_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_3051_end_0 = const()[name = tensor("op_3051_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_3051_end_mask_0 = const()[name = tensor("op_3051_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3051_cast = slice_by_index(begin = var_3051_begin_0, end = var_3051_end_0, end_mask = var_3051_end_mask_0, x = v_21_cast)[name = tensor("op_3051_cast")]; + tensor var_3055_begin_0 = const()[name = tensor("op_3055_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_3055_end_0 = const()[name = tensor("op_3055_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_3055_end_mask_0 = const()[name = tensor("op_3055_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3055_cast = slice_by_index(begin = var_3055_begin_0, end = var_3055_end_0, end_mask = var_3055_end_mask_0, x = v_21_cast)[name = tensor("op_3055_cast")]; + tensor var_3059_begin_0 = const()[name = tensor("op_3059_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_3059_end_0 = const()[name = tensor("op_3059_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_3059_end_mask_0 = const()[name = tensor("op_3059_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3059_cast = slice_by_index(begin = var_3059_begin_0, end = var_3059_end_0, end_mask = var_3059_end_mask_0, x = v_21_cast)[name = tensor("op_3059_cast")]; + tensor var_3063_begin_0 = const()[name = tensor("op_3063_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_3063_end_0 = const()[name = tensor("op_3063_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_3063_end_mask_0 = const()[name = tensor("op_3063_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3063_cast = slice_by_index(begin = var_3063_begin_0, end = var_3063_end_0, end_mask = var_3063_end_mask_0, x = v_21_cast)[name = tensor("op_3063_cast")]; + tensor var_3067_begin_0 = const()[name = tensor("op_3067_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3067_end_0 = const()[name = tensor("op_3067_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_3067_end_mask_0 = const()[name = tensor("op_3067_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3067_cast = slice_by_index(begin = var_3067_begin_0, end = var_3067_end_0, end_mask = var_3067_end_mask_0, x = v_21_cast)[name = tensor("op_3067_cast")]; + tensor var_3071_equation_0 = const()[name = tensor("op_3071_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3071_cast = einsum(equation = var_3071_equation_0, values = (var_3009_cast, var_2974_cast))[name = tensor("op_3071_cast")]; + tensor var_3072_to_fp16 = const()[name = tensor("op_3072_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_161_cast = mul(x = var_3071_cast, y = var_3072_to_fp16)[name = tensor("aw_161_cast")]; + tensor var_3075_equation_0 = const()[name = tensor("op_3075_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3075_cast = einsum(equation = var_3075_equation_0, values = (var_3013_cast, var_2978_cast))[name = tensor("op_3075_cast")]; + tensor var_3076_to_fp16 = const()[name = tensor("op_3076_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_163_cast = mul(x = var_3075_cast, y = var_3076_to_fp16)[name = tensor("aw_163_cast")]; + tensor var_3079_equation_0 = const()[name = tensor("op_3079_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3079_cast = einsum(equation = var_3079_equation_0, values = (var_3017_cast, var_2982_cast))[name = tensor("op_3079_cast")]; + tensor var_3080_to_fp16 = const()[name = tensor("op_3080_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_165_cast = mul(x = var_3079_cast, y = var_3080_to_fp16)[name = tensor("aw_165_cast")]; + tensor var_3083_equation_0 = const()[name = tensor("op_3083_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3083_cast = einsum(equation = var_3083_equation_0, values = (var_3021_cast, var_2986_cast))[name = tensor("op_3083_cast")]; + tensor var_3084_to_fp16 = const()[name = tensor("op_3084_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_167_cast = mul(x = var_3083_cast, y = var_3084_to_fp16)[name = tensor("aw_167_cast")]; + tensor var_3087_equation_0 = const()[name = tensor("op_3087_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3087_cast = einsum(equation = var_3087_equation_0, values = (var_3025_cast, var_2990_cast))[name = tensor("op_3087_cast")]; + tensor var_3088_to_fp16 = const()[name = tensor("op_3088_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_169_cast = mul(x = var_3087_cast, y = var_3088_to_fp16)[name = tensor("aw_169_cast")]; + tensor var_3091_equation_0 = const()[name = tensor("op_3091_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3091_cast = einsum(equation = var_3091_equation_0, values = (var_3029_cast, var_2994_cast))[name = tensor("op_3091_cast")]; + tensor var_3092_to_fp16 = const()[name = tensor("op_3092_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_171_cast = mul(x = var_3091_cast, y = var_3092_to_fp16)[name = tensor("aw_171_cast")]; + tensor var_3095_equation_0 = const()[name = tensor("op_3095_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3095_cast = einsum(equation = var_3095_equation_0, values = (var_3033_cast, var_2998_cast))[name = tensor("op_3095_cast")]; + tensor var_3096_to_fp16 = const()[name = tensor("op_3096_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_173_cast = mul(x = var_3095_cast, y = var_3096_to_fp16)[name = tensor("aw_173_cast")]; + tensor var_3099_equation_0 = const()[name = tensor("op_3099_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3099_cast = einsum(equation = var_3099_equation_0, values = (var_3037_cast, var_3002_cast))[name = tensor("op_3099_cast")]; + tensor var_3100_to_fp16 = const()[name = tensor("op_3100_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_175_cast = mul(x = var_3099_cast, y = var_3100_to_fp16)[name = tensor("aw_175_cast")]; + tensor var_3102_cast = softmax(axis = var_2319, x = aw_161_cast)[name = tensor("op_3102_cast")]; + tensor var_3103_cast = softmax(axis = var_2319, x = aw_163_cast)[name = tensor("op_3103_cast")]; + tensor var_3104_cast = softmax(axis = var_2319, x = aw_165_cast)[name = tensor("op_3104_cast")]; + tensor var_3105_cast = softmax(axis = var_2319, x = aw_167_cast)[name = tensor("op_3105_cast")]; + tensor var_3106_cast = softmax(axis = var_2319, x = aw_169_cast)[name = tensor("op_3106_cast")]; + tensor var_3107_cast = softmax(axis = var_2319, x = aw_171_cast)[name = tensor("op_3107_cast")]; + tensor var_3108_cast = softmax(axis = var_2319, x = aw_173_cast)[name = tensor("op_3108_cast")]; + tensor var_3109_cast = softmax(axis = var_2319, x = aw_175_cast)[name = tensor("op_3109_cast")]; + tensor var_3111_equation_0 = const()[name = tensor("op_3111_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3111_cast = einsum(equation = var_3111_equation_0, values = (var_3039_cast, var_3102_cast))[name = tensor("op_3111_cast")]; + tensor var_3113_equation_0 = const()[name = tensor("op_3113_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3113_cast = einsum(equation = var_3113_equation_0, values = (var_3043_cast, var_3103_cast))[name = tensor("op_3113_cast")]; + tensor var_3115_equation_0 = const()[name = tensor("op_3115_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3115_cast = einsum(equation = var_3115_equation_0, values = (var_3047_cast, var_3104_cast))[name = tensor("op_3115_cast")]; + tensor var_3117_equation_0 = const()[name = tensor("op_3117_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3117_cast = einsum(equation = var_3117_equation_0, values = (var_3051_cast, var_3105_cast))[name = tensor("op_3117_cast")]; + tensor var_3119_equation_0 = const()[name = tensor("op_3119_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3119_cast = einsum(equation = var_3119_equation_0, values = (var_3055_cast, var_3106_cast))[name = tensor("op_3119_cast")]; + tensor var_3121_equation_0 = const()[name = tensor("op_3121_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3121_cast = einsum(equation = var_3121_equation_0, values = (var_3059_cast, var_3107_cast))[name = tensor("op_3121_cast")]; + tensor var_3123_equation_0 = const()[name = tensor("op_3123_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3123_cast = einsum(equation = var_3123_equation_0, values = (var_3063_cast, var_3108_cast))[name = tensor("op_3123_cast")]; + tensor var_3125_equation_0 = const()[name = tensor("op_3125_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3125_cast = einsum(equation = var_3125_equation_0, values = (var_3067_cast, var_3109_cast))[name = tensor("op_3125_cast")]; + tensor input_159_interleave_0 = const()[name = tensor("input_159_interleave_0"), val = tensor(false)]; + tensor input_159_cast = concat(axis = var_2319, interleave = input_159_interleave_0, values = (var_3111_cast, var_3113_cast, var_3115_cast, var_3117_cast, var_3119_cast, var_3121_cast, var_3123_cast, var_3125_cast))[name = tensor("input_159_cast")]; + tensor var_3131 = const()[name = tensor("op_3131"), val = tensor([1, 1])]; + tensor var_3133 = const()[name = tensor("op_3133"), val = tensor([1, 1])]; + tensor var_3135_pad_type_0 = const()[name = tensor("op_3135_pad_type_0"), val = tensor("custom")]; + tensor var_3135_pad_0 = const()[name = tensor("op_3135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109982912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111211776))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111211968)))]; + tensor var_3135_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3133, groups = var_2319, pad = var_3135_pad_0, pad_type = var_3135_pad_type_0, strides = var_3131, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_159_cast)[name = tensor("op_3135_cast")]; + tensor inputs_33_cast = add(x = var_3135_cast, y = inputs_31_cast)[name = tensor("inputs_33_cast")]; + tensor var_3139 = const()[name = tensor("op_3139"), val = tensor([1])]; + tensor channels_mean_33_cast = reduce_mean(axes = var_3139, keep_dims = var_2314, x = inputs_33_cast)[name = tensor("channels_mean_33_cast")]; + tensor zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast)[name = tensor("zero_mean_33_cast")]; + tensor zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast)[name = tensor("zero_mean_sq_33_cast")]; + tensor var_3143 = const()[name = tensor("op_3143"), val = tensor([1])]; + tensor var_3144_cast = reduce_mean(axes = var_3143, keep_dims = var_2314, x = zero_mean_sq_33_cast)[name = tensor("op_3144_cast")]; + tensor var_3145_to_fp16 = const()[name = tensor("op_3145_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3146_cast = add(x = var_3144_cast, y = var_3145_to_fp16)[name = tensor("op_3146_cast")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_3146_cast)[name = tensor("denom_33_cast")]; + tensor out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast)[name = tensor("out_33_cast")]; + tensor var_3150_to_fp16 = const()[name = tensor("op_3150_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111214592)))]; + tensor var_3151_cast = add(x = out_33_cast, y = var_3150_to_fp16)[name = tensor("op_3151_cast")]; + tensor var_3153_to_fp16 = const()[name = tensor("op_3153_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111217216)))]; + tensor hidden_states_101_cast = mul(x = var_3151_cast, y = var_3153_to_fp16)[name = tensor("hidden_states_101_cast")]; + tensor var_3160 = const()[name = tensor("op_3160"), val = tensor([1, 1])]; + tensor var_3162 = const()[name = tensor("op_3162"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111219840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112448704))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_23_cast = conv(dilations = var_3162, groups = var_2319, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_3160, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_101_cast)[name = tensor("q_23_cast")]; + tensor var_3166 = const()[name = tensor("op_3166"), val = tensor([1, 1])]; + tensor var_3168 = const()[name = tensor("op_3168"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112448896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113186240))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_45_cast = conv(dilations = var_3168, groups = var_2319, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_3166, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_45_cast")]; + tensor var_3172 = const()[name = tensor("op_3172"), val = tensor([1, 1])]; + tensor var_3174 = const()[name = tensor("op_3174"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113186432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113923776))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_23_cast = conv(dilations = var_3174, groups = var_2319, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_3172, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_23_cast")]; + tensor var_3178_begin_0 = const()[name = tensor("op_3178_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3178_end_0 = const()[name = tensor("op_3178_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_3178_end_mask_0 = const()[name = tensor("op_3178_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3178_cast = slice_by_index(begin = var_3178_begin_0, end = var_3178_end_0, end_mask = var_3178_end_mask_0, x = q_23_cast)[name = tensor("op_3178_cast")]; + tensor var_3182_begin_0 = const()[name = tensor("op_3182_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_3182_end_0 = const()[name = tensor("op_3182_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_3182_end_mask_0 = const()[name = tensor("op_3182_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3182_cast = slice_by_index(begin = var_3182_begin_0, end = var_3182_end_0, end_mask = var_3182_end_mask_0, x = q_23_cast)[name = tensor("op_3182_cast")]; + tensor var_3186_begin_0 = const()[name = tensor("op_3186_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_3186_end_0 = const()[name = tensor("op_3186_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_3186_end_mask_0 = const()[name = tensor("op_3186_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3186_cast = slice_by_index(begin = var_3186_begin_0, end = var_3186_end_0, end_mask = var_3186_end_mask_0, x = q_23_cast)[name = tensor("op_3186_cast")]; + tensor var_3190_begin_0 = const()[name = tensor("op_3190_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_3190_end_0 = const()[name = tensor("op_3190_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_3190_end_mask_0 = const()[name = tensor("op_3190_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3190_cast = slice_by_index(begin = var_3190_begin_0, end = var_3190_end_0, end_mask = var_3190_end_mask_0, x = q_23_cast)[name = tensor("op_3190_cast")]; + tensor var_3194_begin_0 = const()[name = tensor("op_3194_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_3194_end_0 = const()[name = tensor("op_3194_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_3194_end_mask_0 = const()[name = tensor("op_3194_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3194_cast = slice_by_index(begin = var_3194_begin_0, end = var_3194_end_0, end_mask = var_3194_end_mask_0, x = q_23_cast)[name = tensor("op_3194_cast")]; + tensor var_3198_begin_0 = const()[name = tensor("op_3198_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_3198_end_0 = const()[name = tensor("op_3198_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_3198_end_mask_0 = const()[name = tensor("op_3198_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3198_cast = slice_by_index(begin = var_3198_begin_0, end = var_3198_end_0, end_mask = var_3198_end_mask_0, x = q_23_cast)[name = tensor("op_3198_cast")]; + tensor var_3202_begin_0 = const()[name = tensor("op_3202_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_3202_end_0 = const()[name = tensor("op_3202_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_3202_end_mask_0 = const()[name = tensor("op_3202_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3202_cast = slice_by_index(begin = var_3202_begin_0, end = var_3202_end_0, end_mask = var_3202_end_mask_0, x = q_23_cast)[name = tensor("op_3202_cast")]; + tensor var_3206_begin_0 = const()[name = tensor("op_3206_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3206_end_0 = const()[name = tensor("op_3206_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_3206_end_mask_0 = const()[name = tensor("op_3206_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3206_cast = slice_by_index(begin = var_3206_begin_0, end = var_3206_end_0, end_mask = var_3206_end_mask_0, x = q_23_cast)[name = tensor("op_3206_cast")]; + tensor k_47_perm_0 = const()[name = tensor("k_47_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_3213_begin_0 = const()[name = tensor("op_3213_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3213_end_0 = const()[name = tensor("op_3213_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_3213_end_mask_0 = const()[name = tensor("op_3213_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_20 = transpose(perm = k_47_perm_0, x = k_45_cast)[name = tensor("transpose_20")]; + tensor var_3213_cast = slice_by_index(begin = var_3213_begin_0, end = var_3213_end_0, end_mask = var_3213_end_mask_0, x = transpose_20)[name = tensor("op_3213_cast")]; + tensor var_3217_begin_0 = const()[name = tensor("op_3217_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_3217_end_0 = const()[name = tensor("op_3217_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_3217_end_mask_0 = const()[name = tensor("op_3217_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3217_cast = slice_by_index(begin = var_3217_begin_0, end = var_3217_end_0, end_mask = var_3217_end_mask_0, x = transpose_20)[name = tensor("op_3217_cast")]; + tensor var_3221_begin_0 = const()[name = tensor("op_3221_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_3221_end_0 = const()[name = tensor("op_3221_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_3221_end_mask_0 = const()[name = tensor("op_3221_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3221_cast = slice_by_index(begin = var_3221_begin_0, end = var_3221_end_0, end_mask = var_3221_end_mask_0, x = transpose_20)[name = tensor("op_3221_cast")]; + tensor var_3225_begin_0 = const()[name = tensor("op_3225_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_3225_end_0 = const()[name = tensor("op_3225_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_3225_end_mask_0 = const()[name = tensor("op_3225_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3225_cast = slice_by_index(begin = var_3225_begin_0, end = var_3225_end_0, end_mask = var_3225_end_mask_0, x = transpose_20)[name = tensor("op_3225_cast")]; + tensor var_3229_begin_0 = const()[name = tensor("op_3229_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_3229_end_0 = const()[name = tensor("op_3229_end_0"), val = tensor([2, 77, 1, 800])]; + tensor var_3229_end_mask_0 = const()[name = tensor("op_3229_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3229_cast = slice_by_index(begin = var_3229_begin_0, end = var_3229_end_0, end_mask = var_3229_end_mask_0, x = transpose_20)[name = tensor("op_3229_cast")]; + tensor var_3233_begin_0 = const()[name = tensor("op_3233_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_3233_end_0 = const()[name = tensor("op_3233_end_0"), val = tensor([2, 77, 1, 960])]; + tensor var_3233_end_mask_0 = const()[name = tensor("op_3233_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3233_cast = slice_by_index(begin = var_3233_begin_0, end = var_3233_end_0, end_mask = var_3233_end_mask_0, x = transpose_20)[name = tensor("op_3233_cast")]; + tensor var_3237_begin_0 = const()[name = tensor("op_3237_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_3237_end_0 = const()[name = tensor("op_3237_end_0"), val = tensor([2, 77, 1, 1120])]; + tensor var_3237_end_mask_0 = const()[name = tensor("op_3237_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3237_cast = slice_by_index(begin = var_3237_begin_0, end = var_3237_end_0, end_mask = var_3237_end_mask_0, x = transpose_20)[name = tensor("op_3237_cast")]; + tensor var_3241_begin_0 = const()[name = tensor("op_3241_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_3241_end_0 = const()[name = tensor("op_3241_end_0"), val = tensor([2, 77, 1, 1280])]; + tensor var_3241_end_mask_0 = const()[name = tensor("op_3241_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3241_cast = slice_by_index(begin = var_3241_begin_0, end = var_3241_end_0, end_mask = var_3241_end_mask_0, x = transpose_20)[name = tensor("op_3241_cast")]; + tensor var_3243_begin_0 = const()[name = tensor("op_3243_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3243_end_0 = const()[name = tensor("op_3243_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_3243_end_mask_0 = const()[name = tensor("op_3243_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3243_cast = slice_by_index(begin = var_3243_begin_0, end = var_3243_end_0, end_mask = var_3243_end_mask_0, x = v_23_cast)[name = tensor("op_3243_cast")]; + tensor var_3247_begin_0 = const()[name = tensor("op_3247_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_3247_end_0 = const()[name = tensor("op_3247_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_3247_end_mask_0 = const()[name = tensor("op_3247_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3247_cast = slice_by_index(begin = var_3247_begin_0, end = var_3247_end_0, end_mask = var_3247_end_mask_0, x = v_23_cast)[name = tensor("op_3247_cast")]; + tensor var_3251_begin_0 = const()[name = tensor("op_3251_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_3251_end_0 = const()[name = tensor("op_3251_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_3251_end_mask_0 = const()[name = tensor("op_3251_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3251_cast = slice_by_index(begin = var_3251_begin_0, end = var_3251_end_0, end_mask = var_3251_end_mask_0, x = v_23_cast)[name = tensor("op_3251_cast")]; + tensor var_3255_begin_0 = const()[name = tensor("op_3255_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_3255_end_0 = const()[name = tensor("op_3255_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_3255_end_mask_0 = const()[name = tensor("op_3255_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3255_cast = slice_by_index(begin = var_3255_begin_0, end = var_3255_end_0, end_mask = var_3255_end_mask_0, x = v_23_cast)[name = tensor("op_3255_cast")]; + tensor var_3259_begin_0 = const()[name = tensor("op_3259_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_3259_end_0 = const()[name = tensor("op_3259_end_0"), val = tensor([2, 800, 1, 77])]; + tensor var_3259_end_mask_0 = const()[name = tensor("op_3259_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3259_cast = slice_by_index(begin = var_3259_begin_0, end = var_3259_end_0, end_mask = var_3259_end_mask_0, x = v_23_cast)[name = tensor("op_3259_cast")]; + tensor var_3263_begin_0 = const()[name = tensor("op_3263_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_3263_end_0 = const()[name = tensor("op_3263_end_0"), val = tensor([2, 960, 1, 77])]; + tensor var_3263_end_mask_0 = const()[name = tensor("op_3263_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3263_cast = slice_by_index(begin = var_3263_begin_0, end = var_3263_end_0, end_mask = var_3263_end_mask_0, x = v_23_cast)[name = tensor("op_3263_cast")]; + tensor var_3267_begin_0 = const()[name = tensor("op_3267_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_3267_end_0 = const()[name = tensor("op_3267_end_0"), val = tensor([2, 1120, 1, 77])]; + tensor var_3267_end_mask_0 = const()[name = tensor("op_3267_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3267_cast = slice_by_index(begin = var_3267_begin_0, end = var_3267_end_0, end_mask = var_3267_end_mask_0, x = v_23_cast)[name = tensor("op_3267_cast")]; + tensor var_3271_begin_0 = const()[name = tensor("op_3271_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3271_end_0 = const()[name = tensor("op_3271_end_0"), val = tensor([2, 1280, 1, 77])]; + tensor var_3271_end_mask_0 = const()[name = tensor("op_3271_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3271_cast = slice_by_index(begin = var_3271_begin_0, end = var_3271_end_0, end_mask = var_3271_end_mask_0, x = v_23_cast)[name = tensor("op_3271_cast")]; + tensor var_3275_equation_0 = const()[name = tensor("op_3275_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3275_cast = einsum(equation = var_3275_equation_0, values = (var_3213_cast, var_3178_cast))[name = tensor("op_3275_cast")]; + tensor var_3276_to_fp16 = const()[name = tensor("op_3276_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_177_cast = mul(x = var_3275_cast, y = var_3276_to_fp16)[name = tensor("aw_177_cast")]; + tensor var_3279_equation_0 = const()[name = tensor("op_3279_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3279_cast = einsum(equation = var_3279_equation_0, values = (var_3217_cast, var_3182_cast))[name = tensor("op_3279_cast")]; + tensor var_3280_to_fp16 = const()[name = tensor("op_3280_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_179_cast = mul(x = var_3279_cast, y = var_3280_to_fp16)[name = tensor("aw_179_cast")]; + tensor var_3283_equation_0 = const()[name = tensor("op_3283_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3283_cast = einsum(equation = var_3283_equation_0, values = (var_3221_cast, var_3186_cast))[name = tensor("op_3283_cast")]; + tensor var_3284_to_fp16 = const()[name = tensor("op_3284_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_181_cast = mul(x = var_3283_cast, y = var_3284_to_fp16)[name = tensor("aw_181_cast")]; + tensor var_3287_equation_0 = const()[name = tensor("op_3287_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3287_cast = einsum(equation = var_3287_equation_0, values = (var_3225_cast, var_3190_cast))[name = tensor("op_3287_cast")]; + tensor var_3288_to_fp16 = const()[name = tensor("op_3288_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_183_cast = mul(x = var_3287_cast, y = var_3288_to_fp16)[name = tensor("aw_183_cast")]; + tensor var_3291_equation_0 = const()[name = tensor("op_3291_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3291_cast = einsum(equation = var_3291_equation_0, values = (var_3229_cast, var_3194_cast))[name = tensor("op_3291_cast")]; + tensor var_3292_to_fp16 = const()[name = tensor("op_3292_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_185_cast = mul(x = var_3291_cast, y = var_3292_to_fp16)[name = tensor("aw_185_cast")]; + tensor var_3295_equation_0 = const()[name = tensor("op_3295_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3295_cast = einsum(equation = var_3295_equation_0, values = (var_3233_cast, var_3198_cast))[name = tensor("op_3295_cast")]; + tensor var_3296_to_fp16 = const()[name = tensor("op_3296_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_187_cast = mul(x = var_3295_cast, y = var_3296_to_fp16)[name = tensor("aw_187_cast")]; + tensor var_3299_equation_0 = const()[name = tensor("op_3299_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3299_cast = einsum(equation = var_3299_equation_0, values = (var_3237_cast, var_3202_cast))[name = tensor("op_3299_cast")]; + tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_189_cast = mul(x = var_3299_cast, y = var_3300_to_fp16)[name = tensor("aw_189_cast")]; + tensor var_3303_equation_0 = const()[name = tensor("op_3303_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3303_cast = einsum(equation = var_3303_equation_0, values = (var_3241_cast, var_3206_cast))[name = tensor("op_3303_cast")]; + tensor var_3304_to_fp16 = const()[name = tensor("op_3304_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_191_cast = mul(x = var_3303_cast, y = var_3304_to_fp16)[name = tensor("aw_191_cast")]; + tensor var_3306_cast = softmax(axis = var_2319, x = aw_177_cast)[name = tensor("op_3306_cast")]; + tensor var_3307_cast = softmax(axis = var_2319, x = aw_179_cast)[name = tensor("op_3307_cast")]; + tensor var_3308_cast = softmax(axis = var_2319, x = aw_181_cast)[name = tensor("op_3308_cast")]; + tensor var_3309_cast = softmax(axis = var_2319, x = aw_183_cast)[name = tensor("op_3309_cast")]; + tensor var_3310_cast = softmax(axis = var_2319, x = aw_185_cast)[name = tensor("op_3310_cast")]; + tensor var_3311_cast = softmax(axis = var_2319, x = aw_187_cast)[name = tensor("op_3311_cast")]; + tensor var_3312_cast = softmax(axis = var_2319, x = aw_189_cast)[name = tensor("op_3312_cast")]; + tensor var_3313_cast = softmax(axis = var_2319, x = aw_191_cast)[name = tensor("op_3313_cast")]; + tensor var_3315_equation_0 = const()[name = tensor("op_3315_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3315_cast = einsum(equation = var_3315_equation_0, values = (var_3243_cast, var_3306_cast))[name = tensor("op_3315_cast")]; + tensor var_3317_equation_0 = const()[name = tensor("op_3317_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3317_cast = einsum(equation = var_3317_equation_0, values = (var_3247_cast, var_3307_cast))[name = tensor("op_3317_cast")]; + tensor var_3319_equation_0 = const()[name = tensor("op_3319_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3319_cast = einsum(equation = var_3319_equation_0, values = (var_3251_cast, var_3308_cast))[name = tensor("op_3319_cast")]; + tensor var_3321_equation_0 = const()[name = tensor("op_3321_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3321_cast = einsum(equation = var_3321_equation_0, values = (var_3255_cast, var_3309_cast))[name = tensor("op_3321_cast")]; + tensor var_3323_equation_0 = const()[name = tensor("op_3323_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3323_cast = einsum(equation = var_3323_equation_0, values = (var_3259_cast, var_3310_cast))[name = tensor("op_3323_cast")]; + tensor var_3325_equation_0 = const()[name = tensor("op_3325_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3325_cast = einsum(equation = var_3325_equation_0, values = (var_3263_cast, var_3311_cast))[name = tensor("op_3325_cast")]; + tensor var_3327_equation_0 = const()[name = tensor("op_3327_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3327_cast = einsum(equation = var_3327_equation_0, values = (var_3267_cast, var_3312_cast))[name = tensor("op_3327_cast")]; + tensor var_3329_equation_0 = const()[name = tensor("op_3329_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3329_cast = einsum(equation = var_3329_equation_0, values = (var_3271_cast, var_3313_cast))[name = tensor("op_3329_cast")]; + tensor input_161_interleave_0 = const()[name = tensor("input_161_interleave_0"), val = tensor(false)]; + tensor input_161_cast = concat(axis = var_2319, interleave = input_161_interleave_0, values = (var_3315_cast, var_3317_cast, var_3319_cast, var_3321_cast, var_3323_cast, var_3325_cast, var_3327_cast, var_3329_cast))[name = tensor("input_161_cast")]; + tensor var_3335 = const()[name = tensor("op_3335"), val = tensor([1, 1])]; + tensor var_3337 = const()[name = tensor("op_3337"), val = tensor([1, 1])]; + tensor var_3339_pad_type_0 = const()[name = tensor("op_3339_pad_type_0"), val = tensor("custom")]; + tensor var_3339_pad_0 = const()[name = tensor("op_3339_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113923968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115152832))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115153024)))]; + tensor var_3339_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3337, groups = var_2319, pad = var_3339_pad_0, pad_type = var_3339_pad_type_0, strides = var_3335, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_161_cast)[name = tensor("op_3339_cast")]; + tensor inputs_35_cast = add(x = var_3339_cast, y = inputs_33_cast)[name = tensor("inputs_35_cast")]; + tensor var_3343 = const()[name = tensor("op_3343"), val = tensor([1])]; + tensor channels_mean_35_cast = reduce_mean(axes = var_3343, keep_dims = var_2314, x = inputs_35_cast)[name = tensor("channels_mean_35_cast")]; + tensor zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast)[name = tensor("zero_mean_35_cast")]; + tensor zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast)[name = tensor("zero_mean_sq_35_cast")]; + tensor var_3347 = const()[name = tensor("op_3347"), val = tensor([1])]; + tensor var_3348_cast = reduce_mean(axes = var_3347, keep_dims = var_2314, x = zero_mean_sq_35_cast)[name = tensor("op_3348_cast")]; + tensor var_3349_to_fp16 = const()[name = tensor("op_3349_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3350_cast = add(x = var_3348_cast, y = var_3349_to_fp16)[name = tensor("op_3350_cast")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_3350_cast)[name = tensor("denom_35_cast")]; + tensor out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast)[name = tensor("out_35_cast")]; + tensor var_3354_to_fp16 = const()[name = tensor("op_3354_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115155648)))]; + tensor var_3355_cast = add(x = out_35_cast, y = var_3354_to_fp16)[name = tensor("op_3355_cast")]; + tensor var_3357_to_fp16 = const()[name = tensor("op_3357_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115158272)))]; + tensor input_163_cast = mul(x = var_3355_cast, y = var_3357_to_fp16)[name = tensor("input_163_cast")]; + tensor var_3365 = const()[name = tensor("op_3365"), val = tensor([1, 1])]; + tensor var_3367 = const()[name = tensor("op_3367"), val = tensor([1, 1])]; + tensor var_3369_pad_type_0 = const()[name = tensor("op_3369_pad_type_0"), val = tensor("custom")]; + tensor var_3369_pad_0 = const()[name = tensor("op_3369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115160896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124991360))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124991552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124999296))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3369_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3367, groups = var_2319, pad = var_3369_pad_0, pad_type = var_3369_pad_type_0, strides = var_3365, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_163_cast)[name = tensor("op_3369_cast")]; + tensor var_3370_split_sizes_0 = const()[name = tensor("op_3370_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3370_axis_0 = const()[name = tensor("op_3370_axis_0"), val = tensor(1)]; + tensor var_3370_cast_0, tensor var_3370_cast_1 = split(axis = var_3370_axis_0, split_sizes = var_3370_split_sizes_0, x = var_3369_cast)[name = tensor("op_3370_cast")]; + tensor var_3372_mode_0 = const()[name = tensor("op_3372_mode_0"), val = tensor("EXACT")]; + tensor var_3372_cast = gelu(mode = var_3372_mode_0, x = var_3370_cast_1)[name = tensor("op_3372_cast")]; + tensor input_165_cast = mul(x = var_3370_cast_0, y = var_3372_cast)[name = tensor("input_165_cast")]; + tensor var_3376 = const()[name = tensor("op_3376"), val = tensor([1, 1])]; + tensor var_3378 = const()[name = tensor("op_3378"), val = tensor([1, 1])]; + tensor var_3380_pad_type_0 = const()[name = tensor("op_3380_pad_type_0"), val = tensor("custom")]; + tensor var_3380_pad_0 = const()[name = tensor("op_3380_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124999488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129914752))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129914944)))]; + tensor var_3380_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3378, groups = var_2319, pad = var_3380_pad_0, pad_type = var_3380_pad_type_0, strides = var_3376, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_165_cast)[name = tensor("op_3380_cast")]; + tensor hidden_states_105_cast = add(x = var_3380_cast, y = inputs_35_cast)[name = tensor("hidden_states_105_cast")]; + tensor var_3382 = const()[name = tensor("op_3382"), val = tensor([2, 1280, 24, 24])]; + tensor input_167_cast = reshape(shape = var_3382, x = hidden_states_105_cast)[name = tensor("input_167_cast")]; + tensor var_3386 = const()[name = tensor("op_3386"), val = tensor([1, 1])]; + tensor var_3388 = const()[name = tensor("op_3388"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_type_0 = const()[name = tensor("hidden_states_107_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_107_pad_0 = const()[name = tensor("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129917568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131146432))), name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131146624)))]; + tensor hidden_states_107_cast = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3388, groups = var_2319, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_3386, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_167_cast)[name = tensor("hidden_states_107_cast")]; + tensor input_169_cast = add(x = hidden_states_107_cast, y = hidden_states_95_cast)[name = tensor("input_169_cast")]; + tensor var_3395 = const()[name = tensor("op_3395"), val = tensor([2, 2])]; + tensor var_3397 = const()[name = tensor("op_3397"), val = tensor([1, 1])]; + tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; + tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131149248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142208512))), name = tensor("down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142208704)))]; + tensor input_171_cast = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_3397, groups = var_2319, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_3395, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized, x = input_169_cast)[name = tensor("input_171_cast")]; + tensor var_3409 = const()[name = tensor("op_3409"), val = tensor(1)]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = input_171_cast)[name = tensor("reshape_72_cast")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_56_cast")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142211328)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142213952)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_37_cast")]; + tensor input_175_cast = silu(x = add_37_cast)[name = tensor("input_175_cast")]; + tensor var_3425 = const()[name = tensor("op_3425"), val = tensor([1, 1])]; + tensor var_3427 = const()[name = tensor("op_3427"), val = tensor([1, 1])]; + tensor hidden_states_109_pad_type_0 = const()[name = tensor("hidden_states_109_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_109_pad_0 = const()[name = tensor("hidden_states_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142216576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153275840))), name = tensor("down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153276032)))]; + tensor hidden_states_109_cast = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3427, groups = var_3409, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_3425, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_175_cast)[name = tensor("hidden_states_109_cast")]; + tensor var_3433 = const()[name = tensor("op_3433"), val = tensor([1, 1])]; + tensor var_3435 = const()[name = tensor("op_3435"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153278656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154507520))), name = tensor("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154507712)))]; + tensor temb_13_cast = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3435, groups = var_3409, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_3433, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_13_cast")]; + tensor input_179_cast = add(x = hidden_states_109_cast, y = temb_13_cast)[name = tensor("input_179_cast")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_179_cast)[name = tensor("reshape_76_cast")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_59_cast")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154510336)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154512960)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_39_cast")]; + tensor input_183_cast = silu(x = add_39_cast)[name = tensor("input_183_cast")]; + tensor var_3445 = const()[name = tensor("op_3445"), val = tensor([1, 1])]; + tensor var_3447 = const()[name = tensor("op_3447"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_type_0 = const()[name = tensor("hidden_states_111_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_111_pad_0 = const()[name = tensor("hidden_states_111_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154515584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165574848))), name = tensor("down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165575040)))]; + tensor hidden_states_111_cast = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3447, groups = var_3409, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_3445, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_183_cast)[name = tensor("hidden_states_111_cast")]; + tensor input_185_cast = add(x = input_171_cast, y = hidden_states_111_cast)[name = tensor("input_185_cast")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_185_cast)[name = tensor("reshape_80_cast")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_62_cast")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165577664)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165580288)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_41_cast")]; + tensor input_189_cast = silu(x = add_41_cast)[name = tensor("input_189_cast")]; + tensor var_3462 = const()[name = tensor("op_3462"), val = tensor([1, 1])]; + tensor var_3464 = const()[name = tensor("op_3464"), val = tensor([1, 1])]; + tensor hidden_states_113_pad_type_0 = const()[name = tensor("hidden_states_113_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_113_pad_0 = const()[name = tensor("hidden_states_113_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165582912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176642176))), name = tensor("down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176642368)))]; + tensor hidden_states_113_cast = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_3464, groups = var_3409, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_3462, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_189_cast)[name = tensor("hidden_states_113_cast")]; + tensor var_3470 = const()[name = tensor("op_3470"), val = tensor([1, 1])]; + tensor var_3472 = const()[name = tensor("op_3472"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176644992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177873856))), name = tensor("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177874048)))]; + tensor temb_15_cast = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3472, groups = var_3409, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_3470, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_15_cast")]; + tensor input_193_cast = add(x = hidden_states_113_cast, y = temb_15_cast)[name = tensor("input_193_cast")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_193_cast)[name = tensor("reshape_84_cast")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_65_cast")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177876672)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177879296)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_43_cast")]; + tensor input_197_cast = silu(x = add_43_cast)[name = tensor("input_197_cast")]; + tensor var_3482 = const()[name = tensor("op_3482"), val = tensor([1, 1])]; + tensor var_3484 = const()[name = tensor("op_3484"), val = tensor([1, 1])]; + tensor hidden_states_115_pad_type_0 = const()[name = tensor("hidden_states_115_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_115_pad_0 = const()[name = tensor("hidden_states_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177881920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188941184))), name = tensor("down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188941376)))]; + tensor hidden_states_115_cast = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_3484, groups = var_3409, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_3482, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_197_cast)[name = tensor("hidden_states_115_cast")]; + tensor input_199_cast = add(x = input_185_cast, y = hidden_states_115_cast)[name = tensor("input_199_cast")]; + tensor var_3507 = const()[name = tensor("op_3507"), val = tensor(true)]; + tensor var_3512 = const()[name = tensor("op_3512"), val = tensor(1)]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_199_cast)[name = tensor("reshape_88_cast")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_66_cast")]; + tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor("sub_44_cast")]; + tensor square_22_cast = square(x = sub_44_cast)[name = tensor("square_22_cast")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_68_cast")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast")]; + tensor sqrt_22_cast = sqrt(x = add_44_cast)[name = tensor("sqrt_22_cast")]; + tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast)[name = tensor("real_div_22_cast")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast)[name = tensor("reshape_89_cast")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188944000)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188946624)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_45_cast")]; + tensor input_203_cast = silu(x = add_45_cast)[name = tensor("input_203_cast")]; + tensor var_3530 = const()[name = tensor("op_3530"), val = tensor([1, 1])]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([1, 1])]; + tensor hidden_states_117_pad_type_0 = const()[name = tensor("hidden_states_117_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_117_pad_0 = const()[name = tensor("hidden_states_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188949248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200008512))), name = tensor("mid_block_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200008704)))]; + tensor hidden_states_117_cast = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_3532, groups = var_3512, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_3530, weight = mid_block_resnets_0_conv1_weight_to_fp16_palettized, x = input_203_cast)[name = tensor("hidden_states_117_cast")]; + tensor var_3538 = const()[name = tensor("op_3538"), val = tensor([1, 1])]; + tensor var_3540 = const()[name = tensor("op_3540"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200011328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201240192))), name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201240384)))]; + tensor temb_17_cast = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3540, groups = var_3512, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_3538, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_17_cast")]; + tensor input_207_cast = add(x = hidden_states_117_cast, y = temb_17_cast)[name = tensor("input_207_cast")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_92_cast = reshape(shape = reshape_92_shape_0, x = input_207_cast)[name = tensor("reshape_92_cast")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_69_cast")]; + tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor("sub_46_cast")]; + tensor square_23_cast = square(x = sub_46_cast)[name = tensor("square_23_cast")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_71_cast")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_46_cast = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast")]; + tensor sqrt_23_cast = sqrt(x = add_46_cast)[name = tensor("sqrt_23_cast")]; + tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast)[name = tensor("real_div_23_cast")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast)[name = tensor("reshape_93_cast")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201243008)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201245632)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_47_cast")]; + tensor input_211_cast = silu(x = add_47_cast)[name = tensor("input_211_cast")]; + tensor var_3550 = const()[name = tensor("op_3550"), val = tensor([1, 1])]; + tensor var_3552 = const()[name = tensor("op_3552"), val = tensor([1, 1])]; + tensor hidden_states_119_pad_type_0 = const()[name = tensor("hidden_states_119_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_119_pad_0 = const()[name = tensor("hidden_states_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201248256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212307520))), name = tensor("mid_block_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212307712)))]; + tensor hidden_states_119_cast = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_3552, groups = var_3512, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_3550, weight = mid_block_resnets_0_conv2_weight_to_fp16_palettized, x = input_211_cast)[name = tensor("hidden_states_119_cast")]; + tensor hidden_states_121_cast = add(x = input_199_cast, y = hidden_states_119_cast)[name = tensor("hidden_states_121_cast")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_96_cast = reshape(shape = reshape_96_shape_0, x = hidden_states_121_cast)[name = tensor("reshape_96_cast")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_72_cast")]; + tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor("sub_48_cast")]; + tensor square_24_cast = square(x = sub_48_cast)[name = tensor("square_24_cast")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_74_cast")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_48_cast = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast")]; + tensor sqrt_24_cast = sqrt(x = add_48_cast)[name = tensor("sqrt_24_cast")]; + tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast)[name = tensor("real_div_24_cast")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor("reshape_97_cast")]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212310336)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212312960)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_49_cast")]; + tensor var_3572 = const()[name = tensor("op_3572"), val = tensor([1, 1])]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_type_0 = const()[name = tensor("hidden_states_123_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_123_pad_0 = const()[name = tensor("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212315584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213544448))), name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213544640)))]; + tensor hidden_states_123_cast = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_3574, groups = var_3512, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_3572, weight = mid_block_attentions_0_proj_in_weight_to_fp16_palettized, x = add_49_cast)[name = tensor("hidden_states_123_cast")]; + tensor var_3579 = const()[name = tensor("op_3579"), val = tensor([2, 1280, 1, 144])]; + tensor inputs_37_cast = reshape(shape = var_3579, x = hidden_states_123_cast)[name = tensor("inputs_37_cast")]; + tensor var_3589 = const()[name = tensor("op_3589"), val = tensor([1])]; + tensor channels_mean_37_cast = reduce_mean(axes = var_3589, keep_dims = var_3507, x = inputs_37_cast)[name = tensor("channels_mean_37_cast")]; + tensor zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast)[name = tensor("zero_mean_37_cast")]; + tensor zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast)[name = tensor("zero_mean_sq_37_cast")]; + tensor var_3593 = const()[name = tensor("op_3593"), val = tensor([1])]; + tensor var_3594_cast = reduce_mean(axes = var_3593, keep_dims = var_3507, x = zero_mean_sq_37_cast)[name = tensor("op_3594_cast")]; + tensor var_3595_to_fp16 = const()[name = tensor("op_3595_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3596_cast = add(x = var_3594_cast, y = var_3595_to_fp16)[name = tensor("op_3596_cast")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_3596_cast)[name = tensor("denom_37_cast")]; + tensor out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast)[name = tensor("out_37_cast")]; + tensor var_3600_to_fp16 = const()[name = tensor("op_3600_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213547264)))]; + tensor var_3601_cast = add(x = out_37_cast, y = var_3600_to_fp16)[name = tensor("op_3601_cast")]; + tensor var_3603_to_fp16 = const()[name = tensor("op_3603_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213549888)))]; + tensor hidden_states_125_cast = mul(x = var_3601_cast, y = var_3603_to_fp16)[name = tensor("hidden_states_125_cast")]; + tensor var_3610 = const()[name = tensor("op_3610"), val = tensor([1, 1])]; + tensor var_3612 = const()[name = tensor("op_3612"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213552512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214781376))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_25_cast = conv(dilations = var_3612, groups = var_3512, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_3610, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor("q_25_cast")]; + tensor var_3616 = const()[name = tensor("op_3616"), val = tensor([1, 1])]; + tensor var_3618 = const()[name = tensor("op_3618"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214781568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216010432))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_49_cast = conv(dilations = var_3618, groups = var_3512, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_3616, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor("k_49_cast")]; + tensor var_3622 = const()[name = tensor("op_3622"), val = tensor([1, 1])]; + tensor var_3624 = const()[name = tensor("op_3624"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216010624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217239488))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_25_cast = conv(dilations = var_3624, groups = var_3512, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_3622, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor("v_25_cast")]; + tensor var_3628_begin_0 = const()[name = tensor("op_3628_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3628_end_0 = const()[name = tensor("op_3628_end_0"), val = tensor([2, 160, 1, 144])]; + tensor var_3628_end_mask_0 = const()[name = tensor("op_3628_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3628_cast = slice_by_index(begin = var_3628_begin_0, end = var_3628_end_0, end_mask = var_3628_end_mask_0, x = q_25_cast)[name = tensor("op_3628_cast")]; + tensor var_3632_begin_0 = const()[name = tensor("op_3632_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_3632_end_0 = const()[name = tensor("op_3632_end_0"), val = tensor([2, 320, 1, 144])]; + tensor var_3632_end_mask_0 = const()[name = tensor("op_3632_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3632_cast = slice_by_index(begin = var_3632_begin_0, end = var_3632_end_0, end_mask = var_3632_end_mask_0, x = q_25_cast)[name = tensor("op_3632_cast")]; + tensor var_3636_begin_0 = const()[name = tensor("op_3636_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_3636_end_0 = const()[name = tensor("op_3636_end_0"), val = tensor([2, 480, 1, 144])]; + tensor var_3636_end_mask_0 = const()[name = tensor("op_3636_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3636_cast = slice_by_index(begin = var_3636_begin_0, end = var_3636_end_0, end_mask = var_3636_end_mask_0, x = q_25_cast)[name = tensor("op_3636_cast")]; + tensor var_3640_begin_0 = const()[name = tensor("op_3640_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_3640_end_0 = const()[name = tensor("op_3640_end_0"), val = tensor([2, 640, 1, 144])]; + tensor var_3640_end_mask_0 = const()[name = tensor("op_3640_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3640_cast = slice_by_index(begin = var_3640_begin_0, end = var_3640_end_0, end_mask = var_3640_end_mask_0, x = q_25_cast)[name = tensor("op_3640_cast")]; + tensor var_3644_begin_0 = const()[name = tensor("op_3644_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_3644_end_0 = const()[name = tensor("op_3644_end_0"), val = tensor([2, 800, 1, 144])]; + tensor var_3644_end_mask_0 = const()[name = tensor("op_3644_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3644_cast = slice_by_index(begin = var_3644_begin_0, end = var_3644_end_0, end_mask = var_3644_end_mask_0, x = q_25_cast)[name = tensor("op_3644_cast")]; + tensor var_3648_begin_0 = const()[name = tensor("op_3648_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_3648_end_0 = const()[name = tensor("op_3648_end_0"), val = tensor([2, 960, 1, 144])]; + tensor var_3648_end_mask_0 = const()[name = tensor("op_3648_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3648_cast = slice_by_index(begin = var_3648_begin_0, end = var_3648_end_0, end_mask = var_3648_end_mask_0, x = q_25_cast)[name = tensor("op_3648_cast")]; + tensor var_3652_begin_0 = const()[name = tensor("op_3652_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_3652_end_0 = const()[name = tensor("op_3652_end_0"), val = tensor([2, 1120, 1, 144])]; + tensor var_3652_end_mask_0 = const()[name = tensor("op_3652_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3652_cast = slice_by_index(begin = var_3652_begin_0, end = var_3652_end_0, end_mask = var_3652_end_mask_0, x = q_25_cast)[name = tensor("op_3652_cast")]; + tensor var_3656_begin_0 = const()[name = tensor("op_3656_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3656_end_0 = const()[name = tensor("op_3656_end_0"), val = tensor([2, 1280, 1, 144])]; + tensor var_3656_end_mask_0 = const()[name = tensor("op_3656_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3656_cast = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = q_25_cast)[name = tensor("op_3656_cast")]; + tensor k_51_perm_0 = const()[name = tensor("k_51_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_3663_begin_0 = const()[name = tensor("op_3663_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3663_end_0 = const()[name = tensor("op_3663_end_0"), val = tensor([2, 144, 1, 160])]; + tensor var_3663_end_mask_0 = const()[name = tensor("op_3663_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_19 = transpose(perm = k_51_perm_0, x = k_49_cast)[name = tensor("transpose_19")]; + tensor var_3663_cast = slice_by_index(begin = var_3663_begin_0, end = var_3663_end_0, end_mask = var_3663_end_mask_0, x = transpose_19)[name = tensor("op_3663_cast")]; + tensor var_3667_begin_0 = const()[name = tensor("op_3667_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_3667_end_0 = const()[name = tensor("op_3667_end_0"), val = tensor([2, 144, 1, 320])]; + tensor var_3667_end_mask_0 = const()[name = tensor("op_3667_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3667_cast = slice_by_index(begin = var_3667_begin_0, end = var_3667_end_0, end_mask = var_3667_end_mask_0, x = transpose_19)[name = tensor("op_3667_cast")]; + tensor var_3671_begin_0 = const()[name = tensor("op_3671_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_3671_end_0 = const()[name = tensor("op_3671_end_0"), val = tensor([2, 144, 1, 480])]; + tensor var_3671_end_mask_0 = const()[name = tensor("op_3671_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3671_cast = slice_by_index(begin = var_3671_begin_0, end = var_3671_end_0, end_mask = var_3671_end_mask_0, x = transpose_19)[name = tensor("op_3671_cast")]; + tensor var_3675_begin_0 = const()[name = tensor("op_3675_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_3675_end_0 = const()[name = tensor("op_3675_end_0"), val = tensor([2, 144, 1, 640])]; + tensor var_3675_end_mask_0 = const()[name = tensor("op_3675_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3675_cast = slice_by_index(begin = var_3675_begin_0, end = var_3675_end_0, end_mask = var_3675_end_mask_0, x = transpose_19)[name = tensor("op_3675_cast")]; + tensor var_3679_begin_0 = const()[name = tensor("op_3679_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_3679_end_0 = const()[name = tensor("op_3679_end_0"), val = tensor([2, 144, 1, 800])]; + tensor var_3679_end_mask_0 = const()[name = tensor("op_3679_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3679_cast = slice_by_index(begin = var_3679_begin_0, end = var_3679_end_0, end_mask = var_3679_end_mask_0, x = transpose_19)[name = tensor("op_3679_cast")]; + tensor var_3683_begin_0 = const()[name = tensor("op_3683_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_3683_end_0 = const()[name = tensor("op_3683_end_0"), val = tensor([2, 144, 1, 960])]; + tensor var_3683_end_mask_0 = const()[name = tensor("op_3683_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3683_cast = slice_by_index(begin = var_3683_begin_0, end = var_3683_end_0, end_mask = var_3683_end_mask_0, x = transpose_19)[name = tensor("op_3683_cast")]; + tensor var_3687_begin_0 = const()[name = tensor("op_3687_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_3687_end_0 = const()[name = tensor("op_3687_end_0"), val = tensor([2, 144, 1, 1120])]; + tensor var_3687_end_mask_0 = const()[name = tensor("op_3687_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3687_cast = slice_by_index(begin = var_3687_begin_0, end = var_3687_end_0, end_mask = var_3687_end_mask_0, x = transpose_19)[name = tensor("op_3687_cast")]; + tensor var_3691_begin_0 = const()[name = tensor("op_3691_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_3691_end_0 = const()[name = tensor("op_3691_end_0"), val = tensor([2, 144, 1, 1280])]; + tensor var_3691_end_mask_0 = const()[name = tensor("op_3691_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3691_cast = slice_by_index(begin = var_3691_begin_0, end = var_3691_end_0, end_mask = var_3691_end_mask_0, x = transpose_19)[name = tensor("op_3691_cast")]; + tensor var_3693_begin_0 = const()[name = tensor("op_3693_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3693_end_0 = const()[name = tensor("op_3693_end_0"), val = tensor([2, 160, 1, 144])]; + tensor var_3693_end_mask_0 = const()[name = tensor("op_3693_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3693_cast = slice_by_index(begin = var_3693_begin_0, end = var_3693_end_0, end_mask = var_3693_end_mask_0, x = v_25_cast)[name = tensor("op_3693_cast")]; + tensor var_3697_begin_0 = const()[name = tensor("op_3697_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_3697_end_0 = const()[name = tensor("op_3697_end_0"), val = tensor([2, 320, 1, 144])]; + tensor var_3697_end_mask_0 = const()[name = tensor("op_3697_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3697_cast = slice_by_index(begin = var_3697_begin_0, end = var_3697_end_0, end_mask = var_3697_end_mask_0, x = v_25_cast)[name = tensor("op_3697_cast")]; + tensor var_3701_begin_0 = const()[name = tensor("op_3701_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_3701_end_0 = const()[name = tensor("op_3701_end_0"), val = tensor([2, 480, 1, 144])]; + tensor var_3701_end_mask_0 = const()[name = tensor("op_3701_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3701_cast = slice_by_index(begin = var_3701_begin_0, end = var_3701_end_0, end_mask = var_3701_end_mask_0, x = v_25_cast)[name = tensor("op_3701_cast")]; + tensor var_3705_begin_0 = const()[name = tensor("op_3705_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_3705_end_0 = const()[name = tensor("op_3705_end_0"), val = tensor([2, 640, 1, 144])]; + tensor var_3705_end_mask_0 = const()[name = tensor("op_3705_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3705_cast = slice_by_index(begin = var_3705_begin_0, end = var_3705_end_0, end_mask = var_3705_end_mask_0, x = v_25_cast)[name = tensor("op_3705_cast")]; + tensor var_3709_begin_0 = const()[name = tensor("op_3709_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_3709_end_0 = const()[name = tensor("op_3709_end_0"), val = tensor([2, 800, 1, 144])]; + tensor var_3709_end_mask_0 = const()[name = tensor("op_3709_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3709_cast = slice_by_index(begin = var_3709_begin_0, end = var_3709_end_0, end_mask = var_3709_end_mask_0, x = v_25_cast)[name = tensor("op_3709_cast")]; + tensor var_3713_begin_0 = const()[name = tensor("op_3713_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_3713_end_0 = const()[name = tensor("op_3713_end_0"), val = tensor([2, 960, 1, 144])]; + tensor var_3713_end_mask_0 = const()[name = tensor("op_3713_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3713_cast = slice_by_index(begin = var_3713_begin_0, end = var_3713_end_0, end_mask = var_3713_end_mask_0, x = v_25_cast)[name = tensor("op_3713_cast")]; + tensor var_3717_begin_0 = const()[name = tensor("op_3717_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_3717_end_0 = const()[name = tensor("op_3717_end_0"), val = tensor([2, 1120, 1, 144])]; + tensor var_3717_end_mask_0 = const()[name = tensor("op_3717_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3717_cast = slice_by_index(begin = var_3717_begin_0, end = var_3717_end_0, end_mask = var_3717_end_mask_0, x = v_25_cast)[name = tensor("op_3717_cast")]; + tensor var_3721_begin_0 = const()[name = tensor("op_3721_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3721_end_0 = const()[name = tensor("op_3721_end_0"), val = tensor([2, 1280, 1, 144])]; + tensor var_3721_end_mask_0 = const()[name = tensor("op_3721_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3721_cast = slice_by_index(begin = var_3721_begin_0, end = var_3721_end_0, end_mask = var_3721_end_mask_0, x = v_25_cast)[name = tensor("op_3721_cast")]; + tensor var_3725_equation_0 = const()[name = tensor("op_3725_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3725_cast = einsum(equation = var_3725_equation_0, values = (var_3663_cast, var_3628_cast))[name = tensor("op_3725_cast")]; + tensor var_3726_to_fp16 = const()[name = tensor("op_3726_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_193_cast = mul(x = var_3725_cast, y = var_3726_to_fp16)[name = tensor("aw_193_cast")]; + tensor var_3729_equation_0 = const()[name = tensor("op_3729_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3729_cast = einsum(equation = var_3729_equation_0, values = (var_3667_cast, var_3632_cast))[name = tensor("op_3729_cast")]; + tensor var_3730_to_fp16 = const()[name = tensor("op_3730_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_195_cast = mul(x = var_3729_cast, y = var_3730_to_fp16)[name = tensor("aw_195_cast")]; + tensor var_3733_equation_0 = const()[name = tensor("op_3733_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3733_cast = einsum(equation = var_3733_equation_0, values = (var_3671_cast, var_3636_cast))[name = tensor("op_3733_cast")]; + tensor var_3734_to_fp16 = const()[name = tensor("op_3734_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_197_cast = mul(x = var_3733_cast, y = var_3734_to_fp16)[name = tensor("aw_197_cast")]; + tensor var_3737_equation_0 = const()[name = tensor("op_3737_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3737_cast = einsum(equation = var_3737_equation_0, values = (var_3675_cast, var_3640_cast))[name = tensor("op_3737_cast")]; + tensor var_3738_to_fp16 = const()[name = tensor("op_3738_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_199_cast = mul(x = var_3737_cast, y = var_3738_to_fp16)[name = tensor("aw_199_cast")]; + tensor var_3741_equation_0 = const()[name = tensor("op_3741_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3741_cast = einsum(equation = var_3741_equation_0, values = (var_3679_cast, var_3644_cast))[name = tensor("op_3741_cast")]; + tensor var_3742_to_fp16 = const()[name = tensor("op_3742_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_201_cast = mul(x = var_3741_cast, y = var_3742_to_fp16)[name = tensor("aw_201_cast")]; + tensor var_3745_equation_0 = const()[name = tensor("op_3745_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3745_cast = einsum(equation = var_3745_equation_0, values = (var_3683_cast, var_3648_cast))[name = tensor("op_3745_cast")]; + tensor var_3746_to_fp16 = const()[name = tensor("op_3746_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_203_cast = mul(x = var_3745_cast, y = var_3746_to_fp16)[name = tensor("aw_203_cast")]; + tensor var_3749_equation_0 = const()[name = tensor("op_3749_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3749_cast = einsum(equation = var_3749_equation_0, values = (var_3687_cast, var_3652_cast))[name = tensor("op_3749_cast")]; + tensor var_3750_to_fp16 = const()[name = tensor("op_3750_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_205_cast = mul(x = var_3749_cast, y = var_3750_to_fp16)[name = tensor("aw_205_cast")]; + tensor var_3753_equation_0 = const()[name = tensor("op_3753_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3753_cast = einsum(equation = var_3753_equation_0, values = (var_3691_cast, var_3656_cast))[name = tensor("op_3753_cast")]; + tensor var_3754_to_fp16 = const()[name = tensor("op_3754_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_207_cast = mul(x = var_3753_cast, y = var_3754_to_fp16)[name = tensor("aw_207_cast")]; + tensor var_3756_cast = softmax(axis = var_3512, x = aw_193_cast)[name = tensor("op_3756_cast")]; + tensor var_3757_cast = softmax(axis = var_3512, x = aw_195_cast)[name = tensor("op_3757_cast")]; + tensor var_3758_cast = softmax(axis = var_3512, x = aw_197_cast)[name = tensor("op_3758_cast")]; + tensor var_3759_cast = softmax(axis = var_3512, x = aw_199_cast)[name = tensor("op_3759_cast")]; + tensor var_3760_cast = softmax(axis = var_3512, x = aw_201_cast)[name = tensor("op_3760_cast")]; + tensor var_3761_cast = softmax(axis = var_3512, x = aw_203_cast)[name = tensor("op_3761_cast")]; + tensor var_3762_cast = softmax(axis = var_3512, x = aw_205_cast)[name = tensor("op_3762_cast")]; + tensor var_3763_cast = softmax(axis = var_3512, x = aw_207_cast)[name = tensor("op_3763_cast")]; + tensor var_3765_equation_0 = const()[name = tensor("op_3765_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3765_cast = einsum(equation = var_3765_equation_0, values = (var_3693_cast, var_3756_cast))[name = tensor("op_3765_cast")]; + tensor var_3767_equation_0 = const()[name = tensor("op_3767_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3767_cast = einsum(equation = var_3767_equation_0, values = (var_3697_cast, var_3757_cast))[name = tensor("op_3767_cast")]; + tensor var_3769_equation_0 = const()[name = tensor("op_3769_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3769_cast = einsum(equation = var_3769_equation_0, values = (var_3701_cast, var_3758_cast))[name = tensor("op_3769_cast")]; + tensor var_3771_equation_0 = const()[name = tensor("op_3771_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3771_cast = einsum(equation = var_3771_equation_0, values = (var_3705_cast, var_3759_cast))[name = tensor("op_3771_cast")]; + tensor var_3773_equation_0 = const()[name = tensor("op_3773_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3773_cast = einsum(equation = var_3773_equation_0, values = (var_3709_cast, var_3760_cast))[name = tensor("op_3773_cast")]; + tensor var_3775_equation_0 = const()[name = tensor("op_3775_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3775_cast = einsum(equation = var_3775_equation_0, values = (var_3713_cast, var_3761_cast))[name = tensor("op_3775_cast")]; + tensor var_3777_equation_0 = const()[name = tensor("op_3777_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3777_cast = einsum(equation = var_3777_equation_0, values = (var_3717_cast, var_3762_cast))[name = tensor("op_3777_cast")]; + tensor var_3779_equation_0 = const()[name = tensor("op_3779_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3779_cast = einsum(equation = var_3779_equation_0, values = (var_3721_cast, var_3763_cast))[name = tensor("op_3779_cast")]; + tensor input_215_interleave_0 = const()[name = tensor("input_215_interleave_0"), val = tensor(false)]; + tensor input_215_cast = concat(axis = var_3512, interleave = input_215_interleave_0, values = (var_3765_cast, var_3767_cast, var_3769_cast, var_3771_cast, var_3773_cast, var_3775_cast, var_3777_cast, var_3779_cast))[name = tensor("input_215_cast")]; + tensor var_3785 = const()[name = tensor("op_3785"), val = tensor([1, 1])]; + tensor var_3787 = const()[name = tensor("op_3787"), val = tensor([1, 1])]; + tensor var_3789_pad_type_0 = const()[name = tensor("op_3789_pad_type_0"), val = tensor("custom")]; + tensor var_3789_pad_0 = const()[name = tensor("op_3789_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217239680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218468544))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218468736)))]; + tensor var_3789_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3787, groups = var_3512, pad = var_3789_pad_0, pad_type = var_3789_pad_type_0, strides = var_3785, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_215_cast)[name = tensor("op_3789_cast")]; + tensor inputs_39_cast = add(x = var_3789_cast, y = inputs_37_cast)[name = tensor("inputs_39_cast")]; + tensor var_3793 = const()[name = tensor("op_3793"), val = tensor([1])]; + tensor channels_mean_39_cast = reduce_mean(axes = var_3793, keep_dims = var_3507, x = inputs_39_cast)[name = tensor("channels_mean_39_cast")]; + tensor zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast)[name = tensor("zero_mean_39_cast")]; + tensor zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast)[name = tensor("zero_mean_sq_39_cast")]; + tensor var_3797 = const()[name = tensor("op_3797"), val = tensor([1])]; + tensor var_3798_cast = reduce_mean(axes = var_3797, keep_dims = var_3507, x = zero_mean_sq_39_cast)[name = tensor("op_3798_cast")]; + tensor var_3799_to_fp16 = const()[name = tensor("op_3799_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3800_cast = add(x = var_3798_cast, y = var_3799_to_fp16)[name = tensor("op_3800_cast")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_3800_cast)[name = tensor("denom_39_cast")]; + tensor out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast)[name = tensor("out_39_cast")]; + tensor var_3804_to_fp16 = const()[name = tensor("op_3804_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218471360)))]; + tensor var_3805_cast = add(x = out_39_cast, y = var_3804_to_fp16)[name = tensor("op_3805_cast")]; + tensor var_3807_to_fp16 = const()[name = tensor("op_3807_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218473984)))]; + tensor hidden_states_127_cast = mul(x = var_3805_cast, y = var_3807_to_fp16)[name = tensor("hidden_states_127_cast")]; + tensor var_3814 = const()[name = tensor("op_3814"), val = tensor([1, 1])]; + tensor var_3816 = const()[name = tensor("op_3816"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218476608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219705472))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_27_cast = conv(dilations = var_3816, groups = var_3512, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_3814, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor("q_27_cast")]; + tensor var_3820 = const()[name = tensor("op_3820"), val = tensor([1, 1])]; + tensor var_3822 = const()[name = tensor("op_3822"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219705664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220443008))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_53_cast = conv(dilations = var_3822, groups = var_3512, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_3820, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_53_cast")]; + tensor var_3826 = const()[name = tensor("op_3826"), val = tensor([1, 1])]; + tensor var_3828 = const()[name = tensor("op_3828"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220443200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221180544))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_27_cast = conv(dilations = var_3828, groups = var_3512, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_3826, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_27_cast")]; + tensor var_3832_begin_0 = const()[name = tensor("op_3832_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3832_end_0 = const()[name = tensor("op_3832_end_0"), val = tensor([2, 160, 1, 144])]; + tensor var_3832_end_mask_0 = const()[name = tensor("op_3832_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3832_cast = slice_by_index(begin = var_3832_begin_0, end = var_3832_end_0, end_mask = var_3832_end_mask_0, x = q_27_cast)[name = tensor("op_3832_cast")]; + tensor var_3836_begin_0 = const()[name = tensor("op_3836_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_3836_end_0 = const()[name = tensor("op_3836_end_0"), val = tensor([2, 320, 1, 144])]; + tensor var_3836_end_mask_0 = const()[name = tensor("op_3836_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3836_cast = slice_by_index(begin = var_3836_begin_0, end = var_3836_end_0, end_mask = var_3836_end_mask_0, x = q_27_cast)[name = tensor("op_3836_cast")]; + tensor var_3840_begin_0 = const()[name = tensor("op_3840_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_3840_end_0 = const()[name = tensor("op_3840_end_0"), val = tensor([2, 480, 1, 144])]; + tensor var_3840_end_mask_0 = const()[name = tensor("op_3840_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3840_cast = slice_by_index(begin = var_3840_begin_0, end = var_3840_end_0, end_mask = var_3840_end_mask_0, x = q_27_cast)[name = tensor("op_3840_cast")]; + tensor var_3844_begin_0 = const()[name = tensor("op_3844_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_3844_end_0 = const()[name = tensor("op_3844_end_0"), val = tensor([2, 640, 1, 144])]; + tensor var_3844_end_mask_0 = const()[name = tensor("op_3844_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3844_cast = slice_by_index(begin = var_3844_begin_0, end = var_3844_end_0, end_mask = var_3844_end_mask_0, x = q_27_cast)[name = tensor("op_3844_cast")]; + tensor var_3848_begin_0 = const()[name = tensor("op_3848_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_3848_end_0 = const()[name = tensor("op_3848_end_0"), val = tensor([2, 800, 1, 144])]; + tensor var_3848_end_mask_0 = const()[name = tensor("op_3848_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3848_cast = slice_by_index(begin = var_3848_begin_0, end = var_3848_end_0, end_mask = var_3848_end_mask_0, x = q_27_cast)[name = tensor("op_3848_cast")]; + tensor var_3852_begin_0 = const()[name = tensor("op_3852_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_3852_end_0 = const()[name = tensor("op_3852_end_0"), val = tensor([2, 960, 1, 144])]; + tensor var_3852_end_mask_0 = const()[name = tensor("op_3852_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3852_cast = slice_by_index(begin = var_3852_begin_0, end = var_3852_end_0, end_mask = var_3852_end_mask_0, x = q_27_cast)[name = tensor("op_3852_cast")]; + tensor var_3856_begin_0 = const()[name = tensor("op_3856_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_3856_end_0 = const()[name = tensor("op_3856_end_0"), val = tensor([2, 1120, 1, 144])]; + tensor var_3856_end_mask_0 = const()[name = tensor("op_3856_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3856_cast = slice_by_index(begin = var_3856_begin_0, end = var_3856_end_0, end_mask = var_3856_end_mask_0, x = q_27_cast)[name = tensor("op_3856_cast")]; + tensor var_3860_begin_0 = const()[name = tensor("op_3860_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3860_end_0 = const()[name = tensor("op_3860_end_0"), val = tensor([2, 1280, 1, 144])]; + tensor var_3860_end_mask_0 = const()[name = tensor("op_3860_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3860_cast = slice_by_index(begin = var_3860_begin_0, end = var_3860_end_0, end_mask = var_3860_end_mask_0, x = q_27_cast)[name = tensor("op_3860_cast")]; + tensor k_55_perm_0 = const()[name = tensor("k_55_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_3867_begin_0 = const()[name = tensor("op_3867_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3867_end_0 = const()[name = tensor("op_3867_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_3867_end_mask_0 = const()[name = tensor("op_3867_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_18 = transpose(perm = k_55_perm_0, x = k_53_cast)[name = tensor("transpose_18")]; + tensor var_3867_cast = slice_by_index(begin = var_3867_begin_0, end = var_3867_end_0, end_mask = var_3867_end_mask_0, x = transpose_18)[name = tensor("op_3867_cast")]; + tensor var_3871_begin_0 = const()[name = tensor("op_3871_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_3871_end_0 = const()[name = tensor("op_3871_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_3871_end_mask_0 = const()[name = tensor("op_3871_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3871_cast = slice_by_index(begin = var_3871_begin_0, end = var_3871_end_0, end_mask = var_3871_end_mask_0, x = transpose_18)[name = tensor("op_3871_cast")]; + tensor var_3875_begin_0 = const()[name = tensor("op_3875_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_3875_end_0 = const()[name = tensor("op_3875_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_3875_end_mask_0 = const()[name = tensor("op_3875_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3875_cast = slice_by_index(begin = var_3875_begin_0, end = var_3875_end_0, end_mask = var_3875_end_mask_0, x = transpose_18)[name = tensor("op_3875_cast")]; + tensor var_3879_begin_0 = const()[name = tensor("op_3879_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_3879_end_0 = const()[name = tensor("op_3879_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_3879_end_mask_0 = const()[name = tensor("op_3879_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3879_cast = slice_by_index(begin = var_3879_begin_0, end = var_3879_end_0, end_mask = var_3879_end_mask_0, x = transpose_18)[name = tensor("op_3879_cast")]; + tensor var_3883_begin_0 = const()[name = tensor("op_3883_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_3883_end_0 = const()[name = tensor("op_3883_end_0"), val = tensor([2, 77, 1, 800])]; + tensor var_3883_end_mask_0 = const()[name = tensor("op_3883_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3883_cast = slice_by_index(begin = var_3883_begin_0, end = var_3883_end_0, end_mask = var_3883_end_mask_0, x = transpose_18)[name = tensor("op_3883_cast")]; + tensor var_3887_begin_0 = const()[name = tensor("op_3887_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_3887_end_0 = const()[name = tensor("op_3887_end_0"), val = tensor([2, 77, 1, 960])]; + tensor var_3887_end_mask_0 = const()[name = tensor("op_3887_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3887_cast = slice_by_index(begin = var_3887_begin_0, end = var_3887_end_0, end_mask = var_3887_end_mask_0, x = transpose_18)[name = tensor("op_3887_cast")]; + tensor var_3891_begin_0 = const()[name = tensor("op_3891_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_3891_end_0 = const()[name = tensor("op_3891_end_0"), val = tensor([2, 77, 1, 1120])]; + tensor var_3891_end_mask_0 = const()[name = tensor("op_3891_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3891_cast = slice_by_index(begin = var_3891_begin_0, end = var_3891_end_0, end_mask = var_3891_end_mask_0, x = transpose_18)[name = tensor("op_3891_cast")]; + tensor var_3895_begin_0 = const()[name = tensor("op_3895_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_3895_end_0 = const()[name = tensor("op_3895_end_0"), val = tensor([2, 77, 1, 1280])]; + tensor var_3895_end_mask_0 = const()[name = tensor("op_3895_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_3895_cast = slice_by_index(begin = var_3895_begin_0, end = var_3895_end_0, end_mask = var_3895_end_mask_0, x = transpose_18)[name = tensor("op_3895_cast")]; + tensor var_3897_begin_0 = const()[name = tensor("op_3897_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_3897_end_0 = const()[name = tensor("op_3897_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_3897_end_mask_0 = const()[name = tensor("op_3897_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3897_cast = slice_by_index(begin = var_3897_begin_0, end = var_3897_end_0, end_mask = var_3897_end_mask_0, x = v_27_cast)[name = tensor("op_3897_cast")]; + tensor var_3901_begin_0 = const()[name = tensor("op_3901_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_3901_end_0 = const()[name = tensor("op_3901_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_3901_end_mask_0 = const()[name = tensor("op_3901_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3901_cast = slice_by_index(begin = var_3901_begin_0, end = var_3901_end_0, end_mask = var_3901_end_mask_0, x = v_27_cast)[name = tensor("op_3901_cast")]; + tensor var_3905_begin_0 = const()[name = tensor("op_3905_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_3905_end_0 = const()[name = tensor("op_3905_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_3905_end_mask_0 = const()[name = tensor("op_3905_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3905_cast = slice_by_index(begin = var_3905_begin_0, end = var_3905_end_0, end_mask = var_3905_end_mask_0, x = v_27_cast)[name = tensor("op_3905_cast")]; + tensor var_3909_begin_0 = const()[name = tensor("op_3909_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_3909_end_0 = const()[name = tensor("op_3909_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_3909_end_mask_0 = const()[name = tensor("op_3909_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3909_cast = slice_by_index(begin = var_3909_begin_0, end = var_3909_end_0, end_mask = var_3909_end_mask_0, x = v_27_cast)[name = tensor("op_3909_cast")]; + tensor var_3913_begin_0 = const()[name = tensor("op_3913_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_3913_end_0 = const()[name = tensor("op_3913_end_0"), val = tensor([2, 800, 1, 77])]; + tensor var_3913_end_mask_0 = const()[name = tensor("op_3913_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3913_cast = slice_by_index(begin = var_3913_begin_0, end = var_3913_end_0, end_mask = var_3913_end_mask_0, x = v_27_cast)[name = tensor("op_3913_cast")]; + tensor var_3917_begin_0 = const()[name = tensor("op_3917_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_3917_end_0 = const()[name = tensor("op_3917_end_0"), val = tensor([2, 960, 1, 77])]; + tensor var_3917_end_mask_0 = const()[name = tensor("op_3917_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3917_cast = slice_by_index(begin = var_3917_begin_0, end = var_3917_end_0, end_mask = var_3917_end_mask_0, x = v_27_cast)[name = tensor("op_3917_cast")]; + tensor var_3921_begin_0 = const()[name = tensor("op_3921_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_3921_end_0 = const()[name = tensor("op_3921_end_0"), val = tensor([2, 1120, 1, 77])]; + tensor var_3921_end_mask_0 = const()[name = tensor("op_3921_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3921_cast = slice_by_index(begin = var_3921_begin_0, end = var_3921_end_0, end_mask = var_3921_end_mask_0, x = v_27_cast)[name = tensor("op_3921_cast")]; + tensor var_3925_begin_0 = const()[name = tensor("op_3925_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_3925_end_0 = const()[name = tensor("op_3925_end_0"), val = tensor([2, 1280, 1, 77])]; + tensor var_3925_end_mask_0 = const()[name = tensor("op_3925_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_3925_cast = slice_by_index(begin = var_3925_begin_0, end = var_3925_end_0, end_mask = var_3925_end_mask_0, x = v_27_cast)[name = tensor("op_3925_cast")]; + tensor var_3929_equation_0 = const()[name = tensor("op_3929_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3929_cast = einsum(equation = var_3929_equation_0, values = (var_3867_cast, var_3832_cast))[name = tensor("op_3929_cast")]; + tensor var_3930_to_fp16 = const()[name = tensor("op_3930_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_209_cast = mul(x = var_3929_cast, y = var_3930_to_fp16)[name = tensor("aw_209_cast")]; + tensor var_3933_equation_0 = const()[name = tensor("op_3933_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3933_cast = einsum(equation = var_3933_equation_0, values = (var_3871_cast, var_3836_cast))[name = tensor("op_3933_cast")]; + tensor var_3934_to_fp16 = const()[name = tensor("op_3934_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_211_cast = mul(x = var_3933_cast, y = var_3934_to_fp16)[name = tensor("aw_211_cast")]; + tensor var_3937_equation_0 = const()[name = tensor("op_3937_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3937_cast = einsum(equation = var_3937_equation_0, values = (var_3875_cast, var_3840_cast))[name = tensor("op_3937_cast")]; + tensor var_3938_to_fp16 = const()[name = tensor("op_3938_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_213_cast = mul(x = var_3937_cast, y = var_3938_to_fp16)[name = tensor("aw_213_cast")]; + tensor var_3941_equation_0 = const()[name = tensor("op_3941_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3941_cast = einsum(equation = var_3941_equation_0, values = (var_3879_cast, var_3844_cast))[name = tensor("op_3941_cast")]; + tensor var_3942_to_fp16 = const()[name = tensor("op_3942_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_215_cast = mul(x = var_3941_cast, y = var_3942_to_fp16)[name = tensor("aw_215_cast")]; + tensor var_3945_equation_0 = const()[name = tensor("op_3945_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3945_cast = einsum(equation = var_3945_equation_0, values = (var_3883_cast, var_3848_cast))[name = tensor("op_3945_cast")]; + tensor var_3946_to_fp16 = const()[name = tensor("op_3946_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_217_cast = mul(x = var_3945_cast, y = var_3946_to_fp16)[name = tensor("aw_217_cast")]; + tensor var_3949_equation_0 = const()[name = tensor("op_3949_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3949_cast = einsum(equation = var_3949_equation_0, values = (var_3887_cast, var_3852_cast))[name = tensor("op_3949_cast")]; + tensor var_3950_to_fp16 = const()[name = tensor("op_3950_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_219_cast = mul(x = var_3949_cast, y = var_3950_to_fp16)[name = tensor("aw_219_cast")]; + tensor var_3953_equation_0 = const()[name = tensor("op_3953_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3953_cast = einsum(equation = var_3953_equation_0, values = (var_3891_cast, var_3856_cast))[name = tensor("op_3953_cast")]; + tensor var_3954_to_fp16 = const()[name = tensor("op_3954_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_221_cast = mul(x = var_3953_cast, y = var_3954_to_fp16)[name = tensor("aw_221_cast")]; + tensor var_3957_equation_0 = const()[name = tensor("op_3957_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_3957_cast = einsum(equation = var_3957_equation_0, values = (var_3895_cast, var_3860_cast))[name = tensor("op_3957_cast")]; + tensor var_3958_to_fp16 = const()[name = tensor("op_3958_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_223_cast = mul(x = var_3957_cast, y = var_3958_to_fp16)[name = tensor("aw_223_cast")]; + tensor var_3960_cast = softmax(axis = var_3512, x = aw_209_cast)[name = tensor("op_3960_cast")]; + tensor var_3961_cast = softmax(axis = var_3512, x = aw_211_cast)[name = tensor("op_3961_cast")]; + tensor var_3962_cast = softmax(axis = var_3512, x = aw_213_cast)[name = tensor("op_3962_cast")]; + tensor var_3963_cast = softmax(axis = var_3512, x = aw_215_cast)[name = tensor("op_3963_cast")]; + tensor var_3964_cast = softmax(axis = var_3512, x = aw_217_cast)[name = tensor("op_3964_cast")]; + tensor var_3965_cast = softmax(axis = var_3512, x = aw_219_cast)[name = tensor("op_3965_cast")]; + tensor var_3966_cast = softmax(axis = var_3512, x = aw_221_cast)[name = tensor("op_3966_cast")]; + tensor var_3967_cast = softmax(axis = var_3512, x = aw_223_cast)[name = tensor("op_3967_cast")]; + tensor var_3969_equation_0 = const()[name = tensor("op_3969_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3969_cast = einsum(equation = var_3969_equation_0, values = (var_3897_cast, var_3960_cast))[name = tensor("op_3969_cast")]; + tensor var_3971_equation_0 = const()[name = tensor("op_3971_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3971_cast = einsum(equation = var_3971_equation_0, values = (var_3901_cast, var_3961_cast))[name = tensor("op_3971_cast")]; + tensor var_3973_equation_0 = const()[name = tensor("op_3973_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3973_cast = einsum(equation = var_3973_equation_0, values = (var_3905_cast, var_3962_cast))[name = tensor("op_3973_cast")]; + tensor var_3975_equation_0 = const()[name = tensor("op_3975_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3975_cast = einsum(equation = var_3975_equation_0, values = (var_3909_cast, var_3963_cast))[name = tensor("op_3975_cast")]; + tensor var_3977_equation_0 = const()[name = tensor("op_3977_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3977_cast = einsum(equation = var_3977_equation_0, values = (var_3913_cast, var_3964_cast))[name = tensor("op_3977_cast")]; + tensor var_3979_equation_0 = const()[name = tensor("op_3979_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3979_cast = einsum(equation = var_3979_equation_0, values = (var_3917_cast, var_3965_cast))[name = tensor("op_3979_cast")]; + tensor var_3981_equation_0 = const()[name = tensor("op_3981_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3981_cast = einsum(equation = var_3981_equation_0, values = (var_3921_cast, var_3966_cast))[name = tensor("op_3981_cast")]; + tensor var_3983_equation_0 = const()[name = tensor("op_3983_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_3983_cast = einsum(equation = var_3983_equation_0, values = (var_3925_cast, var_3967_cast))[name = tensor("op_3983_cast")]; + tensor input_217_interleave_0 = const()[name = tensor("input_217_interleave_0"), val = tensor(false)]; + tensor input_217_cast = concat(axis = var_3512, interleave = input_217_interleave_0, values = (var_3969_cast, var_3971_cast, var_3973_cast, var_3975_cast, var_3977_cast, var_3979_cast, var_3981_cast, var_3983_cast))[name = tensor("input_217_cast")]; + tensor var_3989 = const()[name = tensor("op_3989"), val = tensor([1, 1])]; + tensor var_3991 = const()[name = tensor("op_3991"), val = tensor([1, 1])]; + tensor var_3993_pad_type_0 = const()[name = tensor("op_3993_pad_type_0"), val = tensor("custom")]; + tensor var_3993_pad_0 = const()[name = tensor("op_3993_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221180736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222409600))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222409792)))]; + tensor var_3993_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3991, groups = var_3512, pad = var_3993_pad_0, pad_type = var_3993_pad_type_0, strides = var_3989, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_217_cast)[name = tensor("op_3993_cast")]; + tensor inputs_41_cast = add(x = var_3993_cast, y = inputs_39_cast)[name = tensor("inputs_41_cast")]; + tensor var_3997 = const()[name = tensor("op_3997"), val = tensor([1])]; + tensor channels_mean_41_cast = reduce_mean(axes = var_3997, keep_dims = var_3507, x = inputs_41_cast)[name = tensor("channels_mean_41_cast")]; + tensor zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast)[name = tensor("zero_mean_41_cast")]; + tensor zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast)[name = tensor("zero_mean_sq_41_cast")]; + tensor var_4001 = const()[name = tensor("op_4001"), val = tensor([1])]; + tensor var_4002_cast = reduce_mean(axes = var_4001, keep_dims = var_3507, x = zero_mean_sq_41_cast)[name = tensor("op_4002_cast")]; + tensor var_4003_to_fp16 = const()[name = tensor("op_4003_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4004_cast = add(x = var_4002_cast, y = var_4003_to_fp16)[name = tensor("op_4004_cast")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_4004_cast)[name = tensor("denom_41_cast")]; + tensor out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast)[name = tensor("out_41_cast")]; + tensor var_4008_to_fp16 = const()[name = tensor("op_4008_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222412416)))]; + tensor var_4009_cast = add(x = out_41_cast, y = var_4008_to_fp16)[name = tensor("op_4009_cast")]; + tensor var_4011_to_fp16 = const()[name = tensor("op_4011_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222415040)))]; + tensor input_219_cast = mul(x = var_4009_cast, y = var_4011_to_fp16)[name = tensor("input_219_cast")]; + tensor var_4019 = const()[name = tensor("op_4019"), val = tensor([1, 1])]; + tensor var_4021 = const()[name = tensor("op_4021"), val = tensor([1, 1])]; + tensor var_4023_pad_type_0 = const()[name = tensor("op_4023_pad_type_0"), val = tensor("custom")]; + tensor var_4023_pad_0 = const()[name = tensor("op_4023_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222417664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232248128))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232248320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232256064))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4023_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4021, groups = var_3512, pad = var_4023_pad_0, pad_type = var_4023_pad_type_0, strides = var_4019, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_219_cast)[name = tensor("op_4023_cast")]; + tensor var_4024_split_sizes_0 = const()[name = tensor("op_4024_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4024_axis_0 = const()[name = tensor("op_4024_axis_0"), val = tensor(1)]; + tensor var_4024_cast_0, tensor var_4024_cast_1 = split(axis = var_4024_axis_0, split_sizes = var_4024_split_sizes_0, x = var_4023_cast)[name = tensor("op_4024_cast")]; + tensor var_4026_mode_0 = const()[name = tensor("op_4026_mode_0"), val = tensor("EXACT")]; + tensor var_4026_cast = gelu(mode = var_4026_mode_0, x = var_4024_cast_1)[name = tensor("op_4026_cast")]; + tensor input_221_cast = mul(x = var_4024_cast_0, y = var_4026_cast)[name = tensor("input_221_cast")]; + tensor var_4030 = const()[name = tensor("op_4030"), val = tensor([1, 1])]; + tensor var_4032 = const()[name = tensor("op_4032"), val = tensor([1, 1])]; + tensor var_4034_pad_type_0 = const()[name = tensor("op_4034_pad_type_0"), val = tensor("custom")]; + tensor var_4034_pad_0 = const()[name = tensor("op_4034_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232256256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237171520))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237171712)))]; + tensor var_4034_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4032, groups = var_3512, pad = var_4034_pad_0, pad_type = var_4034_pad_type_0, strides = var_4030, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_221_cast)[name = tensor("op_4034_cast")]; + tensor hidden_states_131_cast = add(x = var_4034_cast, y = inputs_41_cast)[name = tensor("hidden_states_131_cast")]; + tensor var_4036 = const()[name = tensor("op_4036"), val = tensor([2, 1280, 12, 12])]; + tensor input_223_cast = reshape(shape = var_4036, x = hidden_states_131_cast)[name = tensor("input_223_cast")]; + tensor var_4040 = const()[name = tensor("op_4040"), val = tensor([1, 1])]; + tensor var_4042 = const()[name = tensor("op_4042"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_type_0 = const()[name = tensor("hidden_states_133_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_133_pad_0 = const()[name = tensor("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237174336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238403200))), name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238403392)))]; + tensor hidden_states_133_cast = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_4042, groups = var_3512, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_4040, weight = mid_block_attentions_0_proj_out_weight_to_fp16_palettized, x = input_223_cast)[name = tensor("hidden_states_133_cast")]; + tensor input_225_cast = add(x = hidden_states_133_cast, y = hidden_states_121_cast)[name = tensor("input_225_cast")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_225_cast)[name = tensor("reshape_100_cast")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_75_cast")]; + tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor("sub_50_cast")]; + tensor square_25_cast = square(x = sub_50_cast)[name = tensor("square_25_cast")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_77_cast")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast")]; + tensor sqrt_25_cast = sqrt(x = add_50_cast)[name = tensor("sqrt_25_cast")]; + tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast)[name = tensor("real_div_25_cast")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast)[name = tensor("reshape_101_cast")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238406016)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238408640)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_51_cast")]; + tensor input_229_cast = silu(x = add_51_cast)[name = tensor("input_229_cast")]; + tensor var_4057 = const()[name = tensor("op_4057"), val = tensor([1, 1])]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238411264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249470528))), name = tensor("mid_block_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249470720)))]; + tensor hidden_states_135_cast = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_4059, groups = var_3512, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_4057, weight = mid_block_resnets_1_conv1_weight_to_fp16_palettized, x = input_229_cast)[name = tensor("hidden_states_135_cast")]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249473344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250702208))), name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250702400)))]; + tensor temb_19_cast = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4067, groups = var_3512, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_4065, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_19_cast")]; + tensor input_233_cast = add(x = hidden_states_135_cast, y = temb_19_cast)[name = tensor("input_233_cast")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_104_cast = reshape(shape = reshape_104_shape_0, x = input_233_cast)[name = tensor("reshape_104_cast")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_78_cast")]; + tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor("sub_52_cast")]; + tensor square_26_cast = square(x = sub_52_cast)[name = tensor("square_26_cast")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_80_cast")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_52_cast = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast")]; + tensor sqrt_26_cast = sqrt(x = add_52_cast)[name = tensor("sqrt_26_cast")]; + tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast)[name = tensor("real_div_26_cast")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast)[name = tensor("reshape_105_cast")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250705024)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250707648)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_53_cast")]; + tensor input_237_cast = silu(x = add_53_cast)[name = tensor("input_237_cast")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([1, 1])]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_type_0 = const()[name = tensor("hidden_states_137_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_137_pad_0 = const()[name = tensor("hidden_states_137_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250710272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261769536))), name = tensor("mid_block_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261769728)))]; + tensor hidden_states_137_cast = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_4079, groups = var_3512, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_4077, weight = mid_block_resnets_1_conv2_weight_to_fp16_palettized, x = input_237_cast)[name = tensor("hidden_states_137_cast")]; + tensor hidden_states_139_cast = add(x = input_225_cast, y = hidden_states_137_cast)[name = tensor("hidden_states_139_cast")]; + tensor var_4090 = const()[name = tensor("op_4090"), val = tensor(1)]; + tensor input_239_interleave_0 = const()[name = tensor("input_239_interleave_0"), val = tensor(false)]; + tensor input_239_cast = concat(axis = var_4090, interleave = input_239_interleave_0, values = (hidden_states_139_cast, input_199_cast))[name = tensor("input_239_cast")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 80, 12, 12])]; + tensor reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_239_cast)[name = tensor("reshape_108_cast")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_81_cast")]; + tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor("sub_54_cast")]; + tensor square_27_cast = square(x = sub_54_cast)[name = tensor("square_27_cast")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_83_cast")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast")]; + tensor sqrt_27_cast = sqrt(x = add_54_cast)[name = tensor("sqrt_27_cast")]; + tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast)[name = tensor("real_div_27_cast")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 2560, 12, 12])]; + tensor reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor("reshape_109_cast")]; + tensor add_55_mean_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261772352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261774336))), name = tensor("add_55_mean_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_variance_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261774528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261776512))), name = tensor("add_55_variance_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261776704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261778688))), name = tensor("add_55_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261778880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261780864))), name = tensor("add_55_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_55_cast")]; + tensor input_243_cast = silu(x = add_55_cast)[name = tensor("input_243_cast")]; + tensor var_4113 = const()[name = tensor("op_4113"), val = tensor([1, 1])]; + tensor var_4115 = const()[name = tensor("op_4115"), val = tensor([1, 1])]; + tensor hidden_states_141_pad_type_0 = const()[name = tensor("hidden_states_141_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_141_pad_0 = const()[name = tensor("hidden_states_141_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261781056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283899520))), name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283899712)))]; + tensor hidden_states_141_cast = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_4115, groups = var_4090, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_4113, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_243_cast)[name = tensor("hidden_states_141_cast")]; + tensor var_4121 = const()[name = tensor("op_4121"), val = tensor([1, 1])]; + tensor var_4123 = const()[name = tensor("op_4123"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283902336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285131200))), name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285131392)))]; + tensor temb_21_cast = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4123, groups = var_4090, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_4121, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_21_cast")]; + tensor input_247_cast = add(x = hidden_states_141_cast, y = temb_21_cast)[name = tensor("input_247_cast")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_247_cast)[name = tensor("reshape_112_cast")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_84_cast")]; + tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor("sub_56_cast")]; + tensor square_28_cast = square(x = sub_56_cast)[name = tensor("square_28_cast")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_86_cast")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast")]; + tensor sqrt_28_cast = sqrt(x = add_56_cast)[name = tensor("sqrt_28_cast")]; + tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast)[name = tensor("real_div_28_cast")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast)[name = tensor("reshape_113_cast")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285134016)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285136640)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_57_cast")]; + tensor input_251_cast = silu(x = add_57_cast)[name = tensor("input_251_cast")]; + tensor var_4133 = const()[name = tensor("op_4133"), val = tensor([1, 1])]; + tensor var_4135 = const()[name = tensor("op_4135"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_type_0 = const()[name = tensor("hidden_states_143_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_143_pad_0 = const()[name = tensor("hidden_states_143_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285139264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296198528))), name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296198720)))]; + tensor hidden_states_143_cast = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_4135, groups = var_4090, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_4133, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_251_cast)[name = tensor("hidden_states_143_cast")]; + tensor var_4140 = const()[name = tensor("op_4140"), val = tensor([1, 1])]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296201344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298659008))), name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298659200)))]; + tensor x_5_cast = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4142, groups = var_4090, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_4140, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_239_cast)[name = tensor("x_5_cast")]; + tensor hidden_states_145_cast = add(x = x_5_cast, y = hidden_states_143_cast)[name = tensor("hidden_states_145_cast")]; + tensor input_253_interleave_0 = const()[name = tensor("input_253_interleave_0"), val = tensor(false)]; + tensor input_253_cast = concat(axis = var_4090, interleave = input_253_interleave_0, values = (hidden_states_145_cast, input_185_cast))[name = tensor("input_253_cast")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 80, 12, 12])]; + tensor reshape_116_cast = reshape(shape = reshape_116_shape_0, x = input_253_cast)[name = tensor("reshape_116_cast")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_87_cast")]; + tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor("sub_58_cast")]; + tensor square_29_cast = square(x = sub_58_cast)[name = tensor("square_29_cast")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_89_cast")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_58_cast = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast")]; + tensor sqrt_29_cast = sqrt(x = add_58_cast)[name = tensor("sqrt_29_cast")]; + tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast)[name = tensor("real_div_29_cast")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 2560, 12, 12])]; + tensor reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor("reshape_117_cast")]; + tensor add_59_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298661824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298663808))), name = tensor("add_59_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_59_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298664000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298665984))), name = tensor("add_59_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_59_cast")]; + tensor input_257_cast = silu(x = add_59_cast)[name = tensor("input_257_cast")]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162 = const()[name = tensor("op_4162"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_type_0 = const()[name = tensor("hidden_states_147_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_147_pad_0 = const()[name = tensor("hidden_states_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298666176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320784640))), name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320784832)))]; + tensor hidden_states_147_cast = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_4162, groups = var_4090, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_4160, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_257_cast)[name = tensor("hidden_states_147_cast")]; + tensor var_4168 = const()[name = tensor("op_4168"), val = tensor([1, 1])]; + tensor var_4170 = const()[name = tensor("op_4170"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320787456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322016320))), name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322016512)))]; + tensor temb_23_cast = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4170, groups = var_4090, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_4168, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_23_cast")]; + tensor input_261_cast = add(x = hidden_states_147_cast, y = temb_23_cast)[name = tensor("input_261_cast")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_261_cast)[name = tensor("reshape_120_cast")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_90_cast")]; + tensor sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast)[name = tensor("sub_60_cast")]; + tensor square_30_cast = square(x = sub_60_cast)[name = tensor("square_30_cast")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_92_cast")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast = add(x = reduce_mean_92_cast, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast")]; + tensor sqrt_30_cast = sqrt(x = add_60_cast)[name = tensor("sqrt_30_cast")]; + tensor real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast)[name = tensor("real_div_30_cast")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast)[name = tensor("reshape_121_cast")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322019136)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322021760)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_61_cast")]; + tensor input_265_cast = silu(x = add_61_cast)[name = tensor("input_265_cast")]; + tensor var_4180 = const()[name = tensor("op_4180"), val = tensor([1, 1])]; + tensor var_4182 = const()[name = tensor("op_4182"), val = tensor([1, 1])]; + tensor hidden_states_149_pad_type_0 = const()[name = tensor("hidden_states_149_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_149_pad_0 = const()[name = tensor("hidden_states_149_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322024384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333083648))), name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333083840)))]; + tensor hidden_states_149_cast = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_4182, groups = var_4090, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_4180, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_265_cast)[name = tensor("hidden_states_149_cast")]; + tensor var_4187 = const()[name = tensor("op_4187"), val = tensor([1, 1])]; + tensor var_4189 = const()[name = tensor("op_4189"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333086464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335544128))), name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335544320)))]; + tensor x_7_cast = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4189, groups = var_4090, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_4187, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_253_cast)[name = tensor("x_7_cast")]; + tensor hidden_states_151_cast = add(x = x_7_cast, y = hidden_states_149_cast)[name = tensor("hidden_states_151_cast")]; + tensor input_267_interleave_0 = const()[name = tensor("input_267_interleave_0"), val = tensor(false)]; + tensor input_267_cast = concat(axis = var_4090, interleave = input_267_interleave_0, values = (hidden_states_151_cast, input_171_cast))[name = tensor("input_267_cast")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 80, 12, 12])]; + tensor reshape_124_cast = reshape(shape = reshape_124_shape_0, x = input_267_cast)[name = tensor("reshape_124_cast")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_93_cast")]; + tensor sub_62_cast = sub(x = reshape_124_cast, y = reduce_mean_93_cast)[name = tensor("sub_62_cast")]; + tensor square_31_cast = square(x = sub_62_cast)[name = tensor("square_31_cast")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_95_cast")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast = add(x = reduce_mean_95_cast, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast")]; + tensor sqrt_31_cast = sqrt(x = add_62_cast)[name = tensor("sqrt_31_cast")]; + tensor real_div_31_cast = real_div(x = sub_62_cast, y = sqrt_31_cast)[name = tensor("real_div_31_cast")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 2560, 12, 12])]; + tensor reshape_125_cast = reshape(shape = reshape_125_shape_0, x = real_div_31_cast)[name = tensor("reshape_125_cast")]; + tensor add_63_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335546944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335548928))), name = tensor("add_63_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_63_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335549120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335551104))), name = tensor("add_63_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_63_cast")]; + tensor input_271_cast = silu(x = add_63_cast)[name = tensor("input_271_cast")]; + tensor var_4207 = const()[name = tensor("op_4207"), val = tensor([1, 1])]; + tensor var_4209 = const()[name = tensor("op_4209"), val = tensor([1, 1])]; + tensor hidden_states_153_pad_type_0 = const()[name = tensor("hidden_states_153_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_153_pad_0 = const()[name = tensor("hidden_states_153_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335551296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357669760))), name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357669952)))]; + tensor hidden_states_153_cast = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_4209, groups = var_4090, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_4207, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized, x = input_271_cast)[name = tensor("hidden_states_153_cast")]; + tensor var_4215 = const()[name = tensor("op_4215"), val = tensor([1, 1])]; + tensor var_4217 = const()[name = tensor("op_4217"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357672576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358901440))), name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358901632)))]; + tensor temb_25_cast = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4217, groups = var_4090, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_4215, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_25_cast")]; + tensor input_275_cast = add(x = hidden_states_153_cast, y = temb_25_cast)[name = tensor("input_275_cast")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 40, 12, 12])]; + tensor reshape_128_cast = reshape(shape = reshape_128_shape_0, x = input_275_cast)[name = tensor("reshape_128_cast")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_96_cast")]; + tensor sub_64_cast = sub(x = reshape_128_cast, y = reduce_mean_96_cast)[name = tensor("sub_64_cast")]; + tensor square_32_cast = square(x = sub_64_cast)[name = tensor("square_32_cast")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_98_cast")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_64_cast = add(x = reduce_mean_98_cast, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast")]; + tensor sqrt_32_cast = sqrt(x = add_64_cast)[name = tensor("sqrt_32_cast")]; + tensor real_div_32_cast = real_div(x = sub_64_cast, y = sqrt_32_cast)[name = tensor("real_div_32_cast")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 1280, 12, 12])]; + tensor reshape_129_cast = reshape(shape = reshape_129_shape_0, x = real_div_32_cast)[name = tensor("reshape_129_cast")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358904256)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358906880)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_65_cast")]; + tensor input_279_cast = silu(x = add_65_cast)[name = tensor("input_279_cast")]; + tensor var_4227 = const()[name = tensor("op_4227"), val = tensor([1, 1])]; + tensor var_4229 = const()[name = tensor("op_4229"), val = tensor([1, 1])]; + tensor hidden_states_155_pad_type_0 = const()[name = tensor("hidden_states_155_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_155_pad_0 = const()[name = tensor("hidden_states_155_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358909504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369968768))), name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369968960)))]; + tensor hidden_states_155_cast = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_4229, groups = var_4090, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_4227, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized, x = input_279_cast)[name = tensor("hidden_states_155_cast")]; + tensor var_4234 = const()[name = tensor("op_4234"), val = tensor([1, 1])]; + tensor var_4236 = const()[name = tensor("op_4236"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369971584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372429248))), name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372429440)))]; + tensor x_9_cast = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4236, groups = var_4090, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_4234, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_267_cast)[name = tensor("x_9_cast")]; + tensor input_281_cast = add(x = x_9_cast, y = hidden_states_155_cast)[name = tensor("input_281_cast")]; + tensor input_283_scale_factor_height_0 = const()[name = tensor("input_283_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_283_scale_factor_width_0 = const()[name = tensor("input_283_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor 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("input_283_cast")]; + tensor var_4245 = const()[name = tensor("op_4245"), val = tensor([1, 1])]; + tensor var_4247 = const()[name = tensor("op_4247"), val = tensor([1, 1])]; + tensor hidden_states_157_pad_type_0 = const()[name = tensor("hidden_states_157_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_157_pad_0 = const()[name = tensor("hidden_states_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372432064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383491328))), name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383491520)))]; + tensor hidden_states_157_cast = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_4247, groups = var_4090, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_4245, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized, x = input_283_cast)[name = tensor("hidden_states_157_cast")]; + tensor var_4267 = const()[name = tensor("op_4267"), val = tensor(true)]; + tensor var_4272 = const()[name = tensor("op_4272"), val = tensor(1)]; + tensor input_285_interleave_0 = const()[name = tensor("input_285_interleave_0"), val = tensor(false)]; + tensor input_285_cast = concat(axis = var_4272, interleave = input_285_interleave_0, values = (hidden_states_157_cast, input_169_cast))[name = tensor("input_285_cast")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 80, 24, 24])]; + tensor reshape_132_cast = reshape(shape = reshape_132_shape_0, x = input_285_cast)[name = tensor("reshape_132_cast")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_99_cast")]; + tensor sub_66_cast = sub(x = reshape_132_cast, y = reduce_mean_99_cast)[name = tensor("sub_66_cast")]; + tensor square_33_cast = square(x = sub_66_cast)[name = tensor("square_33_cast")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_101_cast")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast = add(x = reduce_mean_101_cast, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast")]; + tensor sqrt_33_cast = sqrt(x = add_66_cast)[name = tensor("sqrt_33_cast")]; + tensor real_div_33_cast = real_div(x = sub_66_cast, y = sqrt_33_cast)[name = tensor("real_div_33_cast")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 2560, 24, 24])]; + tensor reshape_133_cast = reshape(shape = reshape_133_shape_0, x = real_div_33_cast)[name = tensor("reshape_133_cast")]; + tensor add_67_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383494144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383496128))), name = tensor("add_67_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_67_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383496320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383498304))), name = tensor("add_67_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_67_cast")]; + tensor input_289_cast = silu(x = add_67_cast)[name = tensor("input_289_cast")]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1, 1])]; + tensor var_4303 = const()[name = tensor("op_4303"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_type_0 = const()[name = tensor("hidden_states_159_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_159_pad_0 = const()[name = tensor("hidden_states_159_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383498496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405616960))), name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405617152)))]; + tensor hidden_states_159_cast = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_4303, groups = var_4272, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_4301, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_289_cast)[name = tensor("hidden_states_159_cast")]; + tensor var_4309 = const()[name = tensor("op_4309"), val = tensor([1, 1])]; + tensor var_4311 = const()[name = tensor("op_4311"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405619776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406848640))), name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406848832)))]; + tensor temb_27_cast = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4311, groups = var_4272, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_4309, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_27_cast")]; + tensor input_293_cast = add(x = hidden_states_159_cast, y = temb_27_cast)[name = tensor("input_293_cast")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_136_cast = reshape(shape = reshape_136_shape_0, x = input_293_cast)[name = tensor("reshape_136_cast")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_102_cast")]; + tensor sub_68_cast = sub(x = reshape_136_cast, y = reduce_mean_102_cast)[name = tensor("sub_68_cast")]; + tensor square_34_cast = square(x = sub_68_cast)[name = tensor("square_34_cast")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_104_cast")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast = add(x = reduce_mean_104_cast, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast")]; + tensor sqrt_34_cast = sqrt(x = add_68_cast)[name = tensor("sqrt_34_cast")]; + tensor real_div_34_cast = real_div(x = sub_68_cast, y = sqrt_34_cast)[name = tensor("real_div_34_cast")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_137_cast = reshape(shape = reshape_137_shape_0, x = real_div_34_cast)[name = tensor("reshape_137_cast")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406851456)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406854080)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_69_cast")]; + tensor input_297_cast = silu(x = add_69_cast)[name = tensor("input_297_cast")]; + tensor var_4321 = const()[name = tensor("op_4321"), val = tensor([1, 1])]; + tensor var_4323 = const()[name = tensor("op_4323"), val = tensor([1, 1])]; + tensor hidden_states_161_pad_type_0 = const()[name = tensor("hidden_states_161_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_161_pad_0 = const()[name = tensor("hidden_states_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406856704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417915968))), name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417916160)))]; + tensor hidden_states_161_cast = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_4323, groups = var_4272, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_4321, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_297_cast)[name = tensor("hidden_states_161_cast")]; + tensor var_4328 = const()[name = tensor("op_4328"), val = tensor([1, 1])]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417918784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420376448))), name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420376640)))]; + tensor x_11_cast = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4330, groups = var_4272, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_4328, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_285_cast)[name = tensor("x_11_cast")]; + tensor hidden_states_163_cast = add(x = x_11_cast, y = hidden_states_161_cast)[name = tensor("hidden_states_163_cast")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_140_cast = reshape(shape = reshape_140_shape_0, x = hidden_states_163_cast)[name = tensor("reshape_140_cast")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_105_cast")]; + tensor sub_70_cast = sub(x = reshape_140_cast, y = reduce_mean_105_cast)[name = tensor("sub_70_cast")]; + tensor square_35_cast = square(x = sub_70_cast)[name = tensor("square_35_cast")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_107_cast")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast = add(x = reduce_mean_107_cast, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast")]; + tensor sqrt_35_cast = sqrt(x = add_70_cast)[name = tensor("sqrt_35_cast")]; + tensor real_div_35_cast = real_div(x = sub_70_cast, y = sqrt_35_cast)[name = tensor("real_div_35_cast")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_141_cast = reshape(shape = reshape_141_shape_0, x = real_div_35_cast)[name = tensor("reshape_141_cast")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420379264)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420381888)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_71_cast")]; + tensor var_4350 = const()[name = tensor("op_4350"), val = tensor([1, 1])]; + tensor var_4352 = const()[name = tensor("op_4352"), val = tensor([1, 1])]; + tensor hidden_states_165_pad_type_0 = const()[name = tensor("hidden_states_165_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_165_pad_0 = const()[name = tensor("hidden_states_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420384512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421613376))), name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421613568)))]; + tensor hidden_states_165_cast = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_4352, groups = var_4272, pad = hidden_states_165_pad_0, pad_type = hidden_states_165_pad_type_0, strides = var_4350, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_71_cast)[name = tensor("hidden_states_165_cast")]; + tensor var_4357 = const()[name = tensor("op_4357"), val = tensor([2, 1280, 1, 576])]; + tensor inputs_43_cast = reshape(shape = var_4357, x = hidden_states_165_cast)[name = tensor("inputs_43_cast")]; + tensor var_4367 = const()[name = tensor("op_4367"), val = tensor([1])]; + tensor channels_mean_43_cast = reduce_mean(axes = var_4367, keep_dims = var_4267, x = inputs_43_cast)[name = tensor("channels_mean_43_cast")]; + tensor zero_mean_43_cast = sub(x = inputs_43_cast, y = channels_mean_43_cast)[name = tensor("zero_mean_43_cast")]; + tensor zero_mean_sq_43_cast = mul(x = zero_mean_43_cast, y = zero_mean_43_cast)[name = tensor("zero_mean_sq_43_cast")]; + tensor var_4371 = const()[name = tensor("op_4371"), val = tensor([1])]; + tensor var_4372_cast = reduce_mean(axes = var_4371, keep_dims = var_4267, x = zero_mean_sq_43_cast)[name = tensor("op_4372_cast")]; + tensor var_4373_to_fp16 = const()[name = tensor("op_4373_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4374_cast = add(x = var_4372_cast, y = var_4373_to_fp16)[name = tensor("op_4374_cast")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_4374_cast)[name = tensor("denom_43_cast")]; + tensor out_43_cast = mul(x = zero_mean_43_cast, y = denom_43_cast)[name = tensor("out_43_cast")]; + tensor var_4378_to_fp16 = const()[name = tensor("op_4378_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421616192)))]; + tensor var_4379_cast = add(x = out_43_cast, y = var_4378_to_fp16)[name = tensor("op_4379_cast")]; + tensor var_4381_to_fp16 = const()[name = tensor("op_4381_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421618816)))]; + tensor hidden_states_167_cast = mul(x = var_4379_cast, y = var_4381_to_fp16)[name = tensor("hidden_states_167_cast")]; + tensor var_4388 = const()[name = tensor("op_4388"), val = tensor([1, 1])]; + tensor var_4390 = const()[name = tensor("op_4390"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421621440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(422850304))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_29_cast = conv(dilations = var_4390, groups = var_4272, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_4388, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor("q_29_cast")]; + tensor var_4394 = const()[name = tensor("op_4394"), val = tensor([1, 1])]; + tensor var_4396 = const()[name = tensor("op_4396"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(422850496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424079360))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_57_cast = conv(dilations = var_4396, groups = var_4272, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_4394, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor("k_57_cast")]; + tensor var_4400 = const()[name = tensor("op_4400"), val = tensor([1, 1])]; + tensor var_4402 = const()[name = tensor("op_4402"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424079552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(425308416))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_29_cast = conv(dilations = var_4402, groups = var_4272, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_4400, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor("v_29_cast")]; + tensor var_4406_begin_0 = const()[name = tensor("op_4406_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4406_end_0 = const()[name = tensor("op_4406_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_4406_end_mask_0 = const()[name = tensor("op_4406_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4406_cast = slice_by_index(begin = var_4406_begin_0, end = var_4406_end_0, end_mask = var_4406_end_mask_0, x = q_29_cast)[name = tensor("op_4406_cast")]; + tensor var_4410_begin_0 = const()[name = tensor("op_4410_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_4410_end_0 = const()[name = tensor("op_4410_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_4410_end_mask_0 = const()[name = tensor("op_4410_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4410_cast = slice_by_index(begin = var_4410_begin_0, end = var_4410_end_0, end_mask = var_4410_end_mask_0, x = q_29_cast)[name = tensor("op_4410_cast")]; + tensor var_4414_begin_0 = const()[name = tensor("op_4414_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_4414_end_0 = const()[name = tensor("op_4414_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_4414_end_mask_0 = const()[name = tensor("op_4414_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4414_cast = slice_by_index(begin = var_4414_begin_0, end = var_4414_end_0, end_mask = var_4414_end_mask_0, x = q_29_cast)[name = tensor("op_4414_cast")]; + tensor var_4418_begin_0 = const()[name = tensor("op_4418_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_4418_end_0 = const()[name = tensor("op_4418_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_4418_end_mask_0 = const()[name = tensor("op_4418_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4418_cast = slice_by_index(begin = var_4418_begin_0, end = var_4418_end_0, end_mask = var_4418_end_mask_0, x = q_29_cast)[name = tensor("op_4418_cast")]; + tensor var_4422_begin_0 = const()[name = tensor("op_4422_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_4422_end_0 = const()[name = tensor("op_4422_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_4422_end_mask_0 = const()[name = tensor("op_4422_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4422_cast = slice_by_index(begin = var_4422_begin_0, end = var_4422_end_0, end_mask = var_4422_end_mask_0, x = q_29_cast)[name = tensor("op_4422_cast")]; + tensor var_4426_begin_0 = const()[name = tensor("op_4426_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_4426_end_0 = const()[name = tensor("op_4426_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_4426_end_mask_0 = const()[name = tensor("op_4426_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4426_cast = slice_by_index(begin = var_4426_begin_0, end = var_4426_end_0, end_mask = var_4426_end_mask_0, x = q_29_cast)[name = tensor("op_4426_cast")]; + tensor var_4430_begin_0 = const()[name = tensor("op_4430_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_4430_end_0 = const()[name = tensor("op_4430_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_4430_end_mask_0 = const()[name = tensor("op_4430_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4430_cast = slice_by_index(begin = var_4430_begin_0, end = var_4430_end_0, end_mask = var_4430_end_mask_0, x = q_29_cast)[name = tensor("op_4430_cast")]; + tensor var_4434_begin_0 = const()[name = tensor("op_4434_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_4434_end_0 = const()[name = tensor("op_4434_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_4434_end_mask_0 = const()[name = tensor("op_4434_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4434_cast = slice_by_index(begin = var_4434_begin_0, end = var_4434_end_0, end_mask = var_4434_end_mask_0, x = q_29_cast)[name = tensor("op_4434_cast")]; + tensor k_59_perm_0 = const()[name = tensor("k_59_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_4441_begin_0 = const()[name = tensor("op_4441_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4441_end_0 = const()[name = tensor("op_4441_end_0"), val = tensor([2, 576, 1, 160])]; + tensor var_4441_end_mask_0 = const()[name = tensor("op_4441_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_17 = transpose(perm = k_59_perm_0, x = k_57_cast)[name = tensor("transpose_17")]; + tensor var_4441_cast = slice_by_index(begin = var_4441_begin_0, end = var_4441_end_0, end_mask = var_4441_end_mask_0, x = transpose_17)[name = tensor("op_4441_cast")]; + tensor var_4445_begin_0 = const()[name = tensor("op_4445_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_4445_end_0 = const()[name = tensor("op_4445_end_0"), val = tensor([2, 576, 1, 320])]; + tensor var_4445_end_mask_0 = const()[name = tensor("op_4445_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4445_cast = slice_by_index(begin = var_4445_begin_0, end = var_4445_end_0, end_mask = var_4445_end_mask_0, x = transpose_17)[name = tensor("op_4445_cast")]; + tensor var_4449_begin_0 = const()[name = tensor("op_4449_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_4449_end_0 = const()[name = tensor("op_4449_end_0"), val = tensor([2, 576, 1, 480])]; + tensor var_4449_end_mask_0 = const()[name = tensor("op_4449_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4449_cast = slice_by_index(begin = var_4449_begin_0, end = var_4449_end_0, end_mask = var_4449_end_mask_0, x = transpose_17)[name = tensor("op_4449_cast")]; + tensor var_4453_begin_0 = const()[name = tensor("op_4453_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_4453_end_0 = const()[name = tensor("op_4453_end_0"), val = tensor([2, 576, 1, 640])]; + tensor var_4453_end_mask_0 = const()[name = tensor("op_4453_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4453_cast = slice_by_index(begin = var_4453_begin_0, end = var_4453_end_0, end_mask = var_4453_end_mask_0, x = transpose_17)[name = tensor("op_4453_cast")]; + tensor var_4457_begin_0 = const()[name = tensor("op_4457_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_4457_end_0 = const()[name = tensor("op_4457_end_0"), val = tensor([2, 576, 1, 800])]; + tensor var_4457_end_mask_0 = const()[name = tensor("op_4457_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4457_cast = slice_by_index(begin = var_4457_begin_0, end = var_4457_end_0, end_mask = var_4457_end_mask_0, x = transpose_17)[name = tensor("op_4457_cast")]; + tensor var_4461_begin_0 = const()[name = tensor("op_4461_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_4461_end_0 = const()[name = tensor("op_4461_end_0"), val = tensor([2, 576, 1, 960])]; + tensor var_4461_end_mask_0 = const()[name = tensor("op_4461_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4461_cast = slice_by_index(begin = var_4461_begin_0, end = var_4461_end_0, end_mask = var_4461_end_mask_0, x = transpose_17)[name = tensor("op_4461_cast")]; + tensor var_4465_begin_0 = const()[name = tensor("op_4465_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_4465_end_0 = const()[name = tensor("op_4465_end_0"), val = tensor([2, 576, 1, 1120])]; + tensor var_4465_end_mask_0 = const()[name = tensor("op_4465_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4465_cast = slice_by_index(begin = var_4465_begin_0, end = var_4465_end_0, end_mask = var_4465_end_mask_0, x = transpose_17)[name = tensor("op_4465_cast")]; + tensor var_4469_begin_0 = const()[name = tensor("op_4469_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_4469_end_0 = const()[name = tensor("op_4469_end_0"), val = tensor([2, 576, 1, 1280])]; + tensor var_4469_end_mask_0 = const()[name = tensor("op_4469_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4469_cast = slice_by_index(begin = var_4469_begin_0, end = var_4469_end_0, end_mask = var_4469_end_mask_0, x = transpose_17)[name = tensor("op_4469_cast")]; + tensor var_4471_begin_0 = const()[name = tensor("op_4471_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4471_end_0 = const()[name = tensor("op_4471_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_4471_end_mask_0 = const()[name = tensor("op_4471_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4471_cast = slice_by_index(begin = var_4471_begin_0, end = var_4471_end_0, end_mask = var_4471_end_mask_0, x = v_29_cast)[name = tensor("op_4471_cast")]; + tensor var_4475_begin_0 = const()[name = tensor("op_4475_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_4475_end_0 = const()[name = tensor("op_4475_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_4475_end_mask_0 = const()[name = tensor("op_4475_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4475_cast = slice_by_index(begin = var_4475_begin_0, end = var_4475_end_0, end_mask = var_4475_end_mask_0, x = v_29_cast)[name = tensor("op_4475_cast")]; + tensor var_4479_begin_0 = const()[name = tensor("op_4479_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_4479_end_0 = const()[name = tensor("op_4479_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_4479_end_mask_0 = const()[name = tensor("op_4479_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4479_cast = slice_by_index(begin = var_4479_begin_0, end = var_4479_end_0, end_mask = var_4479_end_mask_0, x = v_29_cast)[name = tensor("op_4479_cast")]; + tensor var_4483_begin_0 = const()[name = tensor("op_4483_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_4483_end_0 = const()[name = tensor("op_4483_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_4483_end_mask_0 = const()[name = tensor("op_4483_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4483_cast = slice_by_index(begin = var_4483_begin_0, end = var_4483_end_0, end_mask = var_4483_end_mask_0, x = v_29_cast)[name = tensor("op_4483_cast")]; + tensor var_4487_begin_0 = const()[name = tensor("op_4487_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_4487_end_0 = const()[name = tensor("op_4487_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_4487_end_mask_0 = const()[name = tensor("op_4487_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4487_cast = slice_by_index(begin = var_4487_begin_0, end = var_4487_end_0, end_mask = var_4487_end_mask_0, x = v_29_cast)[name = tensor("op_4487_cast")]; + tensor var_4491_begin_0 = const()[name = tensor("op_4491_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_4491_end_0 = const()[name = tensor("op_4491_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_4491_end_mask_0 = const()[name = tensor("op_4491_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4491_cast = slice_by_index(begin = var_4491_begin_0, end = var_4491_end_0, end_mask = var_4491_end_mask_0, x = v_29_cast)[name = tensor("op_4491_cast")]; + tensor var_4495_begin_0 = const()[name = tensor("op_4495_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_4495_end_0 = const()[name = tensor("op_4495_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_4495_end_mask_0 = const()[name = tensor("op_4495_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4495_cast = slice_by_index(begin = var_4495_begin_0, end = var_4495_end_0, end_mask = var_4495_end_mask_0, x = v_29_cast)[name = tensor("op_4495_cast")]; + tensor var_4499_begin_0 = const()[name = tensor("op_4499_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_4499_end_0 = const()[name = tensor("op_4499_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_4499_end_mask_0 = const()[name = tensor("op_4499_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4499_cast = slice_by_index(begin = var_4499_begin_0, end = var_4499_end_0, end_mask = var_4499_end_mask_0, x = v_29_cast)[name = tensor("op_4499_cast")]; + tensor var_4503_equation_0 = const()[name = tensor("op_4503_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4503_cast = einsum(equation = var_4503_equation_0, values = (var_4441_cast, var_4406_cast))[name = tensor("op_4503_cast")]; + tensor var_4504_to_fp16 = const()[name = tensor("op_4504_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_225_cast = mul(x = var_4503_cast, y = var_4504_to_fp16)[name = tensor("aw_225_cast")]; + tensor var_4507_equation_0 = const()[name = tensor("op_4507_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4507_cast = einsum(equation = var_4507_equation_0, values = (var_4445_cast, var_4410_cast))[name = tensor("op_4507_cast")]; + tensor var_4508_to_fp16 = const()[name = tensor("op_4508_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_227_cast = mul(x = var_4507_cast, y = var_4508_to_fp16)[name = tensor("aw_227_cast")]; + tensor var_4511_equation_0 = const()[name = tensor("op_4511_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4511_cast = einsum(equation = var_4511_equation_0, values = (var_4449_cast, var_4414_cast))[name = tensor("op_4511_cast")]; + tensor var_4512_to_fp16 = const()[name = tensor("op_4512_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_229_cast = mul(x = var_4511_cast, y = var_4512_to_fp16)[name = tensor("aw_229_cast")]; + tensor var_4515_equation_0 = const()[name = tensor("op_4515_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4515_cast = einsum(equation = var_4515_equation_0, values = (var_4453_cast, var_4418_cast))[name = tensor("op_4515_cast")]; + tensor var_4516_to_fp16 = const()[name = tensor("op_4516_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_231_cast = mul(x = var_4515_cast, y = var_4516_to_fp16)[name = tensor("aw_231_cast")]; + tensor var_4519_equation_0 = const()[name = tensor("op_4519_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4519_cast = einsum(equation = var_4519_equation_0, values = (var_4457_cast, var_4422_cast))[name = tensor("op_4519_cast")]; + tensor var_4520_to_fp16 = const()[name = tensor("op_4520_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_233_cast = mul(x = var_4519_cast, y = var_4520_to_fp16)[name = tensor("aw_233_cast")]; + tensor var_4523_equation_0 = const()[name = tensor("op_4523_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4523_cast = einsum(equation = var_4523_equation_0, values = (var_4461_cast, var_4426_cast))[name = tensor("op_4523_cast")]; + tensor var_4524_to_fp16 = const()[name = tensor("op_4524_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_235_cast = mul(x = var_4523_cast, y = var_4524_to_fp16)[name = tensor("aw_235_cast")]; + tensor var_4527_equation_0 = const()[name = tensor("op_4527_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4527_cast = einsum(equation = var_4527_equation_0, values = (var_4465_cast, var_4430_cast))[name = tensor("op_4527_cast")]; + tensor var_4528_to_fp16 = const()[name = tensor("op_4528_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_237_cast = mul(x = var_4527_cast, y = var_4528_to_fp16)[name = tensor("aw_237_cast")]; + tensor var_4531_equation_0 = const()[name = tensor("op_4531_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4531_cast = einsum(equation = var_4531_equation_0, values = (var_4469_cast, var_4434_cast))[name = tensor("op_4531_cast")]; + tensor var_4532_to_fp16 = const()[name = tensor("op_4532_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_239_cast = mul(x = var_4531_cast, y = var_4532_to_fp16)[name = tensor("aw_239_cast")]; + tensor var_4534_cast = softmax(axis = var_4272, x = aw_225_cast)[name = tensor("op_4534_cast")]; + tensor var_4535_cast = softmax(axis = var_4272, x = aw_227_cast)[name = tensor("op_4535_cast")]; + tensor var_4536_cast = softmax(axis = var_4272, x = aw_229_cast)[name = tensor("op_4536_cast")]; + tensor var_4537_cast = softmax(axis = var_4272, x = aw_231_cast)[name = tensor("op_4537_cast")]; + tensor var_4538_cast = softmax(axis = var_4272, x = aw_233_cast)[name = tensor("op_4538_cast")]; + tensor var_4539_cast = softmax(axis = var_4272, x = aw_235_cast)[name = tensor("op_4539_cast")]; + tensor var_4540_cast = softmax(axis = var_4272, x = aw_237_cast)[name = tensor("op_4540_cast")]; + tensor var_4541_cast = softmax(axis = var_4272, x = aw_239_cast)[name = tensor("op_4541_cast")]; + tensor var_4543_equation_0 = const()[name = tensor("op_4543_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4543_cast = einsum(equation = var_4543_equation_0, values = (var_4471_cast, var_4534_cast))[name = tensor("op_4543_cast")]; + tensor var_4545_equation_0 = const()[name = tensor("op_4545_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4545_cast = einsum(equation = var_4545_equation_0, values = (var_4475_cast, var_4535_cast))[name = tensor("op_4545_cast")]; + tensor var_4547_equation_0 = const()[name = tensor("op_4547_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4547_cast = einsum(equation = var_4547_equation_0, values = (var_4479_cast, var_4536_cast))[name = tensor("op_4547_cast")]; + tensor var_4549_equation_0 = const()[name = tensor("op_4549_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4549_cast = einsum(equation = var_4549_equation_0, values = (var_4483_cast, var_4537_cast))[name = tensor("op_4549_cast")]; + tensor var_4551_equation_0 = const()[name = tensor("op_4551_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4551_cast = einsum(equation = var_4551_equation_0, values = (var_4487_cast, var_4538_cast))[name = tensor("op_4551_cast")]; + tensor var_4553_equation_0 = const()[name = tensor("op_4553_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4553_cast = einsum(equation = var_4553_equation_0, values = (var_4491_cast, var_4539_cast))[name = tensor("op_4553_cast")]; + tensor var_4555_equation_0 = const()[name = tensor("op_4555_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4555_cast = einsum(equation = var_4555_equation_0, values = (var_4495_cast, var_4540_cast))[name = tensor("op_4555_cast")]; + tensor var_4557_equation_0 = const()[name = tensor("op_4557_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4557_cast = einsum(equation = var_4557_equation_0, values = (var_4499_cast, var_4541_cast))[name = tensor("op_4557_cast")]; + tensor input_301_interleave_0 = const()[name = tensor("input_301_interleave_0"), val = tensor(false)]; + tensor input_301_cast = concat(axis = var_4272, interleave = input_301_interleave_0, values = (var_4543_cast, var_4545_cast, var_4547_cast, var_4549_cast, var_4551_cast, var_4553_cast, var_4555_cast, var_4557_cast))[name = tensor("input_301_cast")]; + tensor var_4563 = const()[name = tensor("op_4563"), val = tensor([1, 1])]; + tensor var_4565 = const()[name = tensor("op_4565"), val = tensor([1, 1])]; + tensor var_4567_pad_type_0 = const()[name = tensor("op_4567_pad_type_0"), val = tensor("custom")]; + tensor var_4567_pad_0 = const()[name = tensor("op_4567_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(425308608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426537472))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426537664)))]; + tensor var_4567_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4565, groups = var_4272, pad = var_4567_pad_0, pad_type = var_4567_pad_type_0, strides = var_4563, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_301_cast)[name = tensor("op_4567_cast")]; + tensor inputs_45_cast = add(x = var_4567_cast, y = inputs_43_cast)[name = tensor("inputs_45_cast")]; + tensor var_4571 = const()[name = tensor("op_4571"), val = tensor([1])]; + tensor channels_mean_45_cast = reduce_mean(axes = var_4571, keep_dims = var_4267, x = inputs_45_cast)[name = tensor("channels_mean_45_cast")]; + tensor zero_mean_45_cast = sub(x = inputs_45_cast, y = channels_mean_45_cast)[name = tensor("zero_mean_45_cast")]; + tensor zero_mean_sq_45_cast = mul(x = zero_mean_45_cast, y = zero_mean_45_cast)[name = tensor("zero_mean_sq_45_cast")]; + tensor var_4575 = const()[name = tensor("op_4575"), val = tensor([1])]; + tensor var_4576_cast = reduce_mean(axes = var_4575, keep_dims = var_4267, x = zero_mean_sq_45_cast)[name = tensor("op_4576_cast")]; + tensor var_4577_to_fp16 = const()[name = tensor("op_4577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4578_cast = add(x = var_4576_cast, y = var_4577_to_fp16)[name = tensor("op_4578_cast")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_4578_cast)[name = tensor("denom_45_cast")]; + tensor out_45_cast = mul(x = zero_mean_45_cast, y = denom_45_cast)[name = tensor("out_45_cast")]; + tensor var_4582_to_fp16 = const()[name = tensor("op_4582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426540288)))]; + tensor var_4583_cast = add(x = out_45_cast, y = var_4582_to_fp16)[name = tensor("op_4583_cast")]; + tensor var_4585_to_fp16 = const()[name = tensor("op_4585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426542912)))]; + tensor hidden_states_169_cast = mul(x = var_4583_cast, y = var_4585_to_fp16)[name = tensor("hidden_states_169_cast")]; + tensor var_4592 = const()[name = tensor("op_4592"), val = tensor([1, 1])]; + tensor var_4594 = const()[name = tensor("op_4594"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426545536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(427774400))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_31_cast = conv(dilations = var_4594, groups = var_4272, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_4592, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_169_cast)[name = tensor("q_31_cast")]; + tensor var_4598 = const()[name = tensor("op_4598"), val = tensor([1, 1])]; + tensor var_4600 = const()[name = tensor("op_4600"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(427774592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428511936))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_61_cast = conv(dilations = var_4600, groups = var_4272, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_4598, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_61_cast")]; + tensor var_4604 = const()[name = tensor("op_4604"), val = tensor([1, 1])]; + tensor var_4606 = const()[name = tensor("op_4606"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428512128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429249472))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_31_cast = conv(dilations = var_4606, groups = var_4272, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_4604, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_31_cast")]; + tensor var_4610_begin_0 = const()[name = tensor("op_4610_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4610_end_0 = const()[name = tensor("op_4610_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_4610_end_mask_0 = const()[name = tensor("op_4610_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4610_cast = slice_by_index(begin = var_4610_begin_0, end = var_4610_end_0, end_mask = var_4610_end_mask_0, x = q_31_cast)[name = tensor("op_4610_cast")]; + tensor var_4614_begin_0 = const()[name = tensor("op_4614_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_4614_end_0 = const()[name = tensor("op_4614_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_4614_end_mask_0 = const()[name = tensor("op_4614_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4614_cast = slice_by_index(begin = var_4614_begin_0, end = var_4614_end_0, end_mask = var_4614_end_mask_0, x = q_31_cast)[name = tensor("op_4614_cast")]; + tensor var_4618_begin_0 = const()[name = tensor("op_4618_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_4618_end_0 = const()[name = tensor("op_4618_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_4618_end_mask_0 = const()[name = tensor("op_4618_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4618_cast = slice_by_index(begin = var_4618_begin_0, end = var_4618_end_0, end_mask = var_4618_end_mask_0, x = q_31_cast)[name = tensor("op_4618_cast")]; + tensor var_4622_begin_0 = const()[name = tensor("op_4622_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_4622_end_0 = const()[name = tensor("op_4622_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_4622_end_mask_0 = const()[name = tensor("op_4622_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4622_cast = slice_by_index(begin = var_4622_begin_0, end = var_4622_end_0, end_mask = var_4622_end_mask_0, x = q_31_cast)[name = tensor("op_4622_cast")]; + tensor var_4626_begin_0 = const()[name = tensor("op_4626_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_4626_end_0 = const()[name = tensor("op_4626_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_4626_end_mask_0 = const()[name = tensor("op_4626_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4626_cast = slice_by_index(begin = var_4626_begin_0, end = var_4626_end_0, end_mask = var_4626_end_mask_0, x = q_31_cast)[name = tensor("op_4626_cast")]; + tensor var_4630_begin_0 = const()[name = tensor("op_4630_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_4630_end_0 = const()[name = tensor("op_4630_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_4630_end_mask_0 = const()[name = tensor("op_4630_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4630_cast = slice_by_index(begin = var_4630_begin_0, end = var_4630_end_0, end_mask = var_4630_end_mask_0, x = q_31_cast)[name = tensor("op_4630_cast")]; + tensor var_4634_begin_0 = const()[name = tensor("op_4634_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_4634_end_0 = const()[name = tensor("op_4634_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_4634_end_mask_0 = const()[name = tensor("op_4634_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4634_cast = slice_by_index(begin = var_4634_begin_0, end = var_4634_end_0, end_mask = var_4634_end_mask_0, x = q_31_cast)[name = tensor("op_4634_cast")]; + tensor var_4638_begin_0 = const()[name = tensor("op_4638_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_4638_end_0 = const()[name = tensor("op_4638_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_4638_end_mask_0 = const()[name = tensor("op_4638_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4638_cast = slice_by_index(begin = var_4638_begin_0, end = var_4638_end_0, end_mask = var_4638_end_mask_0, x = q_31_cast)[name = tensor("op_4638_cast")]; + tensor k_63_perm_0 = const()[name = tensor("k_63_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_4645_begin_0 = const()[name = tensor("op_4645_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4645_end_0 = const()[name = tensor("op_4645_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_4645_end_mask_0 = const()[name = tensor("op_4645_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_16 = transpose(perm = k_63_perm_0, x = k_61_cast)[name = tensor("transpose_16")]; + tensor var_4645_cast = slice_by_index(begin = var_4645_begin_0, end = var_4645_end_0, end_mask = var_4645_end_mask_0, x = transpose_16)[name = tensor("op_4645_cast")]; + tensor var_4649_begin_0 = const()[name = tensor("op_4649_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_4649_end_0 = const()[name = tensor("op_4649_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_4649_end_mask_0 = const()[name = tensor("op_4649_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4649_cast = slice_by_index(begin = var_4649_begin_0, end = var_4649_end_0, end_mask = var_4649_end_mask_0, x = transpose_16)[name = tensor("op_4649_cast")]; + tensor var_4653_begin_0 = const()[name = tensor("op_4653_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_4653_end_0 = const()[name = tensor("op_4653_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_4653_end_mask_0 = const()[name = tensor("op_4653_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4653_cast = slice_by_index(begin = var_4653_begin_0, end = var_4653_end_0, end_mask = var_4653_end_mask_0, x = transpose_16)[name = tensor("op_4653_cast")]; + tensor var_4657_begin_0 = const()[name = tensor("op_4657_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_4657_end_0 = const()[name = tensor("op_4657_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_4657_end_mask_0 = const()[name = tensor("op_4657_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4657_cast = slice_by_index(begin = var_4657_begin_0, end = var_4657_end_0, end_mask = var_4657_end_mask_0, x = transpose_16)[name = tensor("op_4657_cast")]; + tensor var_4661_begin_0 = const()[name = tensor("op_4661_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_4661_end_0 = const()[name = tensor("op_4661_end_0"), val = tensor([2, 77, 1, 800])]; + tensor var_4661_end_mask_0 = const()[name = tensor("op_4661_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4661_cast = slice_by_index(begin = var_4661_begin_0, end = var_4661_end_0, end_mask = var_4661_end_mask_0, x = transpose_16)[name = tensor("op_4661_cast")]; + tensor var_4665_begin_0 = const()[name = tensor("op_4665_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_4665_end_0 = const()[name = tensor("op_4665_end_0"), val = tensor([2, 77, 1, 960])]; + tensor var_4665_end_mask_0 = const()[name = tensor("op_4665_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4665_cast = slice_by_index(begin = var_4665_begin_0, end = var_4665_end_0, end_mask = var_4665_end_mask_0, x = transpose_16)[name = tensor("op_4665_cast")]; + tensor var_4669_begin_0 = const()[name = tensor("op_4669_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_4669_end_0 = const()[name = tensor("op_4669_end_0"), val = tensor([2, 77, 1, 1120])]; + tensor var_4669_end_mask_0 = const()[name = tensor("op_4669_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4669_cast = slice_by_index(begin = var_4669_begin_0, end = var_4669_end_0, end_mask = var_4669_end_mask_0, x = transpose_16)[name = tensor("op_4669_cast")]; + tensor var_4673_begin_0 = const()[name = tensor("op_4673_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_4673_end_0 = const()[name = tensor("op_4673_end_0"), val = tensor([2, 77, 1, 1280])]; + tensor var_4673_end_mask_0 = const()[name = tensor("op_4673_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4673_cast = slice_by_index(begin = var_4673_begin_0, end = var_4673_end_0, end_mask = var_4673_end_mask_0, x = transpose_16)[name = tensor("op_4673_cast")]; + tensor var_4675_begin_0 = const()[name = tensor("op_4675_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4675_end_0 = const()[name = tensor("op_4675_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_4675_end_mask_0 = const()[name = tensor("op_4675_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4675_cast = slice_by_index(begin = var_4675_begin_0, end = var_4675_end_0, end_mask = var_4675_end_mask_0, x = v_31_cast)[name = tensor("op_4675_cast")]; + tensor var_4679_begin_0 = const()[name = tensor("op_4679_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_4679_end_0 = const()[name = tensor("op_4679_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_4679_end_mask_0 = const()[name = tensor("op_4679_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4679_cast = slice_by_index(begin = var_4679_begin_0, end = var_4679_end_0, end_mask = var_4679_end_mask_0, x = v_31_cast)[name = tensor("op_4679_cast")]; + tensor var_4683_begin_0 = const()[name = tensor("op_4683_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_4683_end_0 = const()[name = tensor("op_4683_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_4683_end_mask_0 = const()[name = tensor("op_4683_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4683_cast = slice_by_index(begin = var_4683_begin_0, end = var_4683_end_0, end_mask = var_4683_end_mask_0, x = v_31_cast)[name = tensor("op_4683_cast")]; + tensor var_4687_begin_0 = const()[name = tensor("op_4687_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_4687_end_0 = const()[name = tensor("op_4687_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_4687_end_mask_0 = const()[name = tensor("op_4687_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4687_cast = slice_by_index(begin = var_4687_begin_0, end = var_4687_end_0, end_mask = var_4687_end_mask_0, x = v_31_cast)[name = tensor("op_4687_cast")]; + tensor var_4691_begin_0 = const()[name = tensor("op_4691_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_4691_end_0 = const()[name = tensor("op_4691_end_0"), val = tensor([2, 800, 1, 77])]; + tensor var_4691_end_mask_0 = const()[name = tensor("op_4691_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4691_cast = slice_by_index(begin = var_4691_begin_0, end = var_4691_end_0, end_mask = var_4691_end_mask_0, x = v_31_cast)[name = tensor("op_4691_cast")]; + tensor var_4695_begin_0 = const()[name = tensor("op_4695_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_4695_end_0 = const()[name = tensor("op_4695_end_0"), val = tensor([2, 960, 1, 77])]; + tensor var_4695_end_mask_0 = const()[name = tensor("op_4695_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4695_cast = slice_by_index(begin = var_4695_begin_0, end = var_4695_end_0, end_mask = var_4695_end_mask_0, x = v_31_cast)[name = tensor("op_4695_cast")]; + tensor var_4699_begin_0 = const()[name = tensor("op_4699_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_4699_end_0 = const()[name = tensor("op_4699_end_0"), val = tensor([2, 1120, 1, 77])]; + tensor var_4699_end_mask_0 = const()[name = tensor("op_4699_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4699_cast = slice_by_index(begin = var_4699_begin_0, end = var_4699_end_0, end_mask = var_4699_end_mask_0, x = v_31_cast)[name = tensor("op_4699_cast")]; + tensor var_4703_begin_0 = const()[name = tensor("op_4703_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_4703_end_0 = const()[name = tensor("op_4703_end_0"), val = tensor([2, 1280, 1, 77])]; + tensor var_4703_end_mask_0 = const()[name = tensor("op_4703_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4703_cast = slice_by_index(begin = var_4703_begin_0, end = var_4703_end_0, end_mask = var_4703_end_mask_0, x = v_31_cast)[name = tensor("op_4703_cast")]; + tensor var_4707_equation_0 = const()[name = tensor("op_4707_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4707_cast = einsum(equation = var_4707_equation_0, values = (var_4645_cast, var_4610_cast))[name = tensor("op_4707_cast")]; + tensor var_4708_to_fp16 = const()[name = tensor("op_4708_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_241_cast = mul(x = var_4707_cast, y = var_4708_to_fp16)[name = tensor("aw_241_cast")]; + tensor var_4711_equation_0 = const()[name = tensor("op_4711_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4711_cast = einsum(equation = var_4711_equation_0, values = (var_4649_cast, var_4614_cast))[name = tensor("op_4711_cast")]; + tensor var_4712_to_fp16 = const()[name = tensor("op_4712_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_243_cast = mul(x = var_4711_cast, y = var_4712_to_fp16)[name = tensor("aw_243_cast")]; + tensor var_4715_equation_0 = const()[name = tensor("op_4715_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4715_cast = einsum(equation = var_4715_equation_0, values = (var_4653_cast, var_4618_cast))[name = tensor("op_4715_cast")]; + tensor var_4716_to_fp16 = const()[name = tensor("op_4716_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_245_cast = mul(x = var_4715_cast, y = var_4716_to_fp16)[name = tensor("aw_245_cast")]; + tensor var_4719_equation_0 = const()[name = tensor("op_4719_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4719_cast = einsum(equation = var_4719_equation_0, values = (var_4657_cast, var_4622_cast))[name = tensor("op_4719_cast")]; + tensor var_4720_to_fp16 = const()[name = tensor("op_4720_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_247_cast = mul(x = var_4719_cast, y = var_4720_to_fp16)[name = tensor("aw_247_cast")]; + tensor var_4723_equation_0 = const()[name = tensor("op_4723_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4723_cast = einsum(equation = var_4723_equation_0, values = (var_4661_cast, var_4626_cast))[name = tensor("op_4723_cast")]; + tensor var_4724_to_fp16 = const()[name = tensor("op_4724_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_249_cast = mul(x = var_4723_cast, y = var_4724_to_fp16)[name = tensor("aw_249_cast")]; + tensor var_4727_equation_0 = const()[name = tensor("op_4727_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4727_cast = einsum(equation = var_4727_equation_0, values = (var_4665_cast, var_4630_cast))[name = tensor("op_4727_cast")]; + tensor var_4728_to_fp16 = const()[name = tensor("op_4728_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_251_cast = mul(x = var_4727_cast, y = var_4728_to_fp16)[name = tensor("aw_251_cast")]; + tensor var_4731_equation_0 = const()[name = tensor("op_4731_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4731_cast = einsum(equation = var_4731_equation_0, values = (var_4669_cast, var_4634_cast))[name = tensor("op_4731_cast")]; + tensor var_4732_to_fp16 = const()[name = tensor("op_4732_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_253_cast = mul(x = var_4731_cast, y = var_4732_to_fp16)[name = tensor("aw_253_cast")]; + tensor var_4735_equation_0 = const()[name = tensor("op_4735_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_4735_cast = einsum(equation = var_4735_equation_0, values = (var_4673_cast, var_4638_cast))[name = tensor("op_4735_cast")]; + tensor var_4736_to_fp16 = const()[name = tensor("op_4736_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_255_cast = mul(x = var_4735_cast, y = var_4736_to_fp16)[name = tensor("aw_255_cast")]; + tensor var_4738_cast = softmax(axis = var_4272, x = aw_241_cast)[name = tensor("op_4738_cast")]; + tensor var_4739_cast = softmax(axis = var_4272, x = aw_243_cast)[name = tensor("op_4739_cast")]; + tensor var_4740_cast = softmax(axis = var_4272, x = aw_245_cast)[name = tensor("op_4740_cast")]; + tensor var_4741_cast = softmax(axis = var_4272, x = aw_247_cast)[name = tensor("op_4741_cast")]; + tensor var_4742_cast = softmax(axis = var_4272, x = aw_249_cast)[name = tensor("op_4742_cast")]; + tensor var_4743_cast = softmax(axis = var_4272, x = aw_251_cast)[name = tensor("op_4743_cast")]; + tensor var_4744_cast = softmax(axis = var_4272, x = aw_253_cast)[name = tensor("op_4744_cast")]; + tensor var_4745_cast = softmax(axis = var_4272, x = aw_255_cast)[name = tensor("op_4745_cast")]; + tensor var_4747_equation_0 = const()[name = tensor("op_4747_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4747_cast = einsum(equation = var_4747_equation_0, values = (var_4675_cast, var_4738_cast))[name = tensor("op_4747_cast")]; + tensor var_4749_equation_0 = const()[name = tensor("op_4749_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4749_cast = einsum(equation = var_4749_equation_0, values = (var_4679_cast, var_4739_cast))[name = tensor("op_4749_cast")]; + tensor var_4751_equation_0 = const()[name = tensor("op_4751_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4751_cast = einsum(equation = var_4751_equation_0, values = (var_4683_cast, var_4740_cast))[name = tensor("op_4751_cast")]; + tensor var_4753_equation_0 = const()[name = tensor("op_4753_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4753_cast = einsum(equation = var_4753_equation_0, values = (var_4687_cast, var_4741_cast))[name = tensor("op_4753_cast")]; + tensor var_4755_equation_0 = const()[name = tensor("op_4755_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4755_cast = einsum(equation = var_4755_equation_0, values = (var_4691_cast, var_4742_cast))[name = tensor("op_4755_cast")]; + tensor var_4757_equation_0 = const()[name = tensor("op_4757_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4757_cast = einsum(equation = var_4757_equation_0, values = (var_4695_cast, var_4743_cast))[name = tensor("op_4757_cast")]; + tensor var_4759_equation_0 = const()[name = tensor("op_4759_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4759_cast = einsum(equation = var_4759_equation_0, values = (var_4699_cast, var_4744_cast))[name = tensor("op_4759_cast")]; + tensor var_4761_equation_0 = const()[name = tensor("op_4761_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_4761_cast = einsum(equation = var_4761_equation_0, values = (var_4703_cast, var_4745_cast))[name = tensor("op_4761_cast")]; + tensor input_303_interleave_0 = const()[name = tensor("input_303_interleave_0"), val = tensor(false)]; + tensor input_303_cast = concat(axis = var_4272, interleave = input_303_interleave_0, values = (var_4747_cast, var_4749_cast, var_4751_cast, var_4753_cast, var_4755_cast, var_4757_cast, var_4759_cast, var_4761_cast))[name = tensor("input_303_cast")]; + tensor var_4767 = const()[name = tensor("op_4767"), val = tensor([1, 1])]; + tensor var_4769 = const()[name = tensor("op_4769"), val = tensor([1, 1])]; + tensor var_4771_pad_type_0 = const()[name = tensor("op_4771_pad_type_0"), val = tensor("custom")]; + tensor var_4771_pad_0 = const()[name = tensor("op_4771_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429249664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430478528))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430478720)))]; + tensor var_4771_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4769, groups = var_4272, pad = var_4771_pad_0, pad_type = var_4771_pad_type_0, strides = var_4767, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_303_cast)[name = tensor("op_4771_cast")]; + tensor inputs_47_cast = add(x = var_4771_cast, y = inputs_45_cast)[name = tensor("inputs_47_cast")]; + tensor var_4775 = const()[name = tensor("op_4775"), val = tensor([1])]; + tensor channels_mean_47_cast = reduce_mean(axes = var_4775, keep_dims = var_4267, x = inputs_47_cast)[name = tensor("channels_mean_47_cast")]; + tensor zero_mean_47_cast = sub(x = inputs_47_cast, y = channels_mean_47_cast)[name = tensor("zero_mean_47_cast")]; + tensor zero_mean_sq_47_cast = mul(x = zero_mean_47_cast, y = zero_mean_47_cast)[name = tensor("zero_mean_sq_47_cast")]; + tensor var_4779 = const()[name = tensor("op_4779"), val = tensor([1])]; + tensor var_4780_cast = reduce_mean(axes = var_4779, keep_dims = var_4267, x = zero_mean_sq_47_cast)[name = tensor("op_4780_cast")]; + tensor var_4781_to_fp16 = const()[name = tensor("op_4781_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4782_cast = add(x = var_4780_cast, y = var_4781_to_fp16)[name = tensor("op_4782_cast")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_4782_cast)[name = tensor("denom_47_cast")]; + tensor out_47_cast = mul(x = zero_mean_47_cast, y = denom_47_cast)[name = tensor("out_47_cast")]; + tensor var_4786_to_fp16 = const()[name = tensor("op_4786_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430481344)))]; + tensor var_4787_cast = add(x = out_47_cast, y = var_4786_to_fp16)[name = tensor("op_4787_cast")]; + tensor var_4789_to_fp16 = const()[name = tensor("op_4789_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430483968)))]; + tensor input_305_cast = mul(x = var_4787_cast, y = var_4789_to_fp16)[name = tensor("input_305_cast")]; + tensor var_4797 = const()[name = tensor("op_4797"), val = tensor([1, 1])]; + tensor var_4799 = const()[name = tensor("op_4799"), val = tensor([1, 1])]; + tensor var_4801_pad_type_0 = const()[name = tensor("op_4801_pad_type_0"), val = tensor("custom")]; + tensor var_4801_pad_0 = const()[name = tensor("op_4801_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430486592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440317056))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440317248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440324992))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4801_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4799, groups = var_4272, pad = var_4801_pad_0, pad_type = var_4801_pad_type_0, strides = var_4797, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_305_cast)[name = tensor("op_4801_cast")]; + tensor var_4802_split_sizes_0 = const()[name = tensor("op_4802_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4802_axis_0 = const()[name = tensor("op_4802_axis_0"), val = tensor(1)]; + tensor var_4802_cast_0, tensor var_4802_cast_1 = split(axis = var_4802_axis_0, split_sizes = var_4802_split_sizes_0, x = var_4801_cast)[name = tensor("op_4802_cast")]; + tensor var_4804_mode_0 = const()[name = tensor("op_4804_mode_0"), val = tensor("EXACT")]; + tensor var_4804_cast = gelu(mode = var_4804_mode_0, x = var_4802_cast_1)[name = tensor("op_4804_cast")]; + tensor input_307_cast = mul(x = var_4802_cast_0, y = var_4804_cast)[name = tensor("input_307_cast")]; + tensor var_4808 = const()[name = tensor("op_4808"), val = tensor([1, 1])]; + tensor var_4810 = const()[name = tensor("op_4810"), val = tensor([1, 1])]; + tensor var_4812_pad_type_0 = const()[name = tensor("op_4812_pad_type_0"), val = tensor("custom")]; + tensor var_4812_pad_0 = const()[name = tensor("op_4812_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440325184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445240448))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445240640)))]; + tensor var_4812_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4810, groups = var_4272, pad = var_4812_pad_0, pad_type = var_4812_pad_type_0, strides = var_4808, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_307_cast)[name = tensor("op_4812_cast")]; + tensor hidden_states_173_cast = add(x = var_4812_cast, y = inputs_47_cast)[name = tensor("hidden_states_173_cast")]; + tensor var_4814 = const()[name = tensor("op_4814"), val = tensor([2, 1280, 24, 24])]; + tensor input_309_cast = reshape(shape = var_4814, x = hidden_states_173_cast)[name = tensor("input_309_cast")]; + tensor var_4818 = const()[name = tensor("op_4818"), val = tensor([1, 1])]; + tensor var_4820 = const()[name = tensor("op_4820"), val = tensor([1, 1])]; + tensor hidden_states_175_pad_type_0 = const()[name = tensor("hidden_states_175_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_175_pad_0 = const()[name = tensor("hidden_states_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445243264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446472128))), name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446472320)))]; + tensor hidden_states_175_cast = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_4820, groups = var_4272, pad = hidden_states_175_pad_0, pad_type = hidden_states_175_pad_type_0, strides = var_4818, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_309_cast)[name = tensor("hidden_states_175_cast")]; + tensor hidden_states_177_cast = add(x = hidden_states_175_cast, y = hidden_states_163_cast)[name = tensor("hidden_states_177_cast")]; + tensor input_311_interleave_0 = const()[name = tensor("input_311_interleave_0"), val = tensor(false)]; + tensor input_311_cast = concat(axis = var_4272, interleave = input_311_interleave_0, values = (hidden_states_177_cast, input_143_cast))[name = tensor("input_311_cast")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 80, 24, 24])]; + tensor reshape_144_cast = reshape(shape = reshape_144_shape_0, x = input_311_cast)[name = tensor("reshape_144_cast")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_108_cast")]; + tensor sub_72_cast = sub(x = reshape_144_cast, y = reduce_mean_108_cast)[name = tensor("sub_72_cast")]; + tensor square_36_cast = square(x = sub_72_cast)[name = tensor("square_36_cast")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_110_cast")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast = add(x = reduce_mean_110_cast, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast")]; + tensor sqrt_36_cast = sqrt(x = add_72_cast)[name = tensor("sqrt_36_cast")]; + tensor real_div_36_cast = real_div(x = sub_72_cast, y = sqrt_36_cast)[name = tensor("real_div_36_cast")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 2560, 24, 24])]; + tensor reshape_145_cast = reshape(shape = reshape_145_shape_0, x = real_div_36_cast)[name = tensor("reshape_145_cast")]; + tensor add_73_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446474944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446476928))), name = tensor("add_73_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_73_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446477120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446479104))), name = tensor("add_73_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_73_cast")]; + tensor input_315_cast = silu(x = add_73_cast)[name = tensor("input_315_cast")]; + tensor var_4838 = const()[name = tensor("op_4838"), val = tensor([1, 1])]; + tensor var_4840 = const()[name = tensor("op_4840"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_type_0 = const()[name = tensor("hidden_states_179_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_179_pad_0 = const()[name = tensor("hidden_states_179_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446479296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(468597760))), name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(468597952)))]; + tensor hidden_states_179_cast = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_4840, groups = var_4272, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = var_4838, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_315_cast)[name = tensor("hidden_states_179_cast")]; + tensor var_4846 = const()[name = tensor("op_4846"), val = tensor([1, 1])]; + tensor var_4848 = const()[name = tensor("op_4848"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(468600576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469829440))), name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469829632)))]; + tensor temb_29_cast = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4848, groups = var_4272, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_4846, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_29_cast")]; + tensor input_319_cast = add(x = hidden_states_179_cast, y = temb_29_cast)[name = tensor("input_319_cast")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_148_cast = reshape(shape = reshape_148_shape_0, x = input_319_cast)[name = tensor("reshape_148_cast")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_111_cast")]; + tensor sub_74_cast = sub(x = reshape_148_cast, y = reduce_mean_111_cast)[name = tensor("sub_74_cast")]; + tensor square_37_cast = square(x = sub_74_cast)[name = tensor("square_37_cast")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_113_cast")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast = add(x = reduce_mean_113_cast, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast")]; + tensor sqrt_37_cast = sqrt(x = add_74_cast)[name = tensor("sqrt_37_cast")]; + tensor real_div_37_cast = real_div(x = sub_74_cast, y = sqrt_37_cast)[name = tensor("real_div_37_cast")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_149_cast = reshape(shape = reshape_149_shape_0, x = real_div_37_cast)[name = tensor("reshape_149_cast")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469832256)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469834880)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_75_cast")]; + tensor input_323_cast = silu(x = add_75_cast)[name = tensor("input_323_cast")]; + tensor var_4858 = const()[name = tensor("op_4858"), val = tensor([1, 1])]; + tensor var_4860 = const()[name = tensor("op_4860"), val = tensor([1, 1])]; + tensor hidden_states_181_pad_type_0 = const()[name = tensor("hidden_states_181_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_181_pad_0 = const()[name = tensor("hidden_states_181_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469837504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480896768))), name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480896960)))]; + tensor hidden_states_181_cast = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_4860, groups = var_4272, pad = hidden_states_181_pad_0, pad_type = hidden_states_181_pad_type_0, strides = var_4858, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_323_cast)[name = tensor("hidden_states_181_cast")]; + tensor var_4865 = const()[name = tensor("op_4865"), val = tensor([1, 1])]; + tensor var_4867 = const()[name = tensor("op_4867"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480899584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483357248))), name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483357440)))]; + tensor x_13_cast = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4867, groups = var_4272, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_4865, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_311_cast)[name = tensor("x_13_cast")]; + tensor hidden_states_183_cast = add(x = x_13_cast, y = hidden_states_181_cast)[name = tensor("hidden_states_183_cast")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_152_cast = reshape(shape = reshape_152_shape_0, x = hidden_states_183_cast)[name = tensor("reshape_152_cast")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_114_cast")]; + tensor sub_76_cast = sub(x = reshape_152_cast, y = reduce_mean_114_cast)[name = tensor("sub_76_cast")]; + tensor square_38_cast = square(x = sub_76_cast)[name = tensor("square_38_cast")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_116_cast")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast = add(x = reduce_mean_116_cast, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast")]; + tensor sqrt_38_cast = sqrt(x = add_76_cast)[name = tensor("sqrt_38_cast")]; + tensor real_div_38_cast = real_div(x = sub_76_cast, y = sqrt_38_cast)[name = tensor("real_div_38_cast")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_153_cast = reshape(shape = reshape_153_shape_0, x = real_div_38_cast)[name = tensor("reshape_153_cast")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483360064)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483362688)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_77_cast")]; + tensor var_4887 = const()[name = tensor("op_4887"), val = tensor([1, 1])]; + tensor var_4889 = const()[name = tensor("op_4889"), val = tensor([1, 1])]; + tensor hidden_states_185_pad_type_0 = const()[name = tensor("hidden_states_185_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_185_pad_0 = const()[name = tensor("hidden_states_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483365312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484594176))), name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484594368)))]; + tensor hidden_states_185_cast = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_4889, groups = var_4272, pad = hidden_states_185_pad_0, pad_type = hidden_states_185_pad_type_0, strides = var_4887, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_77_cast)[name = tensor("hidden_states_185_cast")]; + tensor var_4894 = const()[name = tensor("op_4894"), val = tensor([2, 1280, 1, 576])]; + tensor inputs_49_cast = reshape(shape = var_4894, x = hidden_states_185_cast)[name = tensor("inputs_49_cast")]; + tensor var_4904 = const()[name = tensor("op_4904"), val = tensor([1])]; + tensor channels_mean_49_cast = reduce_mean(axes = var_4904, keep_dims = var_4267, x = inputs_49_cast)[name = tensor("channels_mean_49_cast")]; + tensor zero_mean_49_cast = sub(x = inputs_49_cast, y = channels_mean_49_cast)[name = tensor("zero_mean_49_cast")]; + tensor zero_mean_sq_49_cast = mul(x = zero_mean_49_cast, y = zero_mean_49_cast)[name = tensor("zero_mean_sq_49_cast")]; + tensor var_4908 = const()[name = tensor("op_4908"), val = tensor([1])]; + tensor var_4909_cast = reduce_mean(axes = var_4908, keep_dims = var_4267, x = zero_mean_sq_49_cast)[name = tensor("op_4909_cast")]; + tensor var_4910_to_fp16 = const()[name = tensor("op_4910_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4911_cast = add(x = var_4909_cast, y = var_4910_to_fp16)[name = tensor("op_4911_cast")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_4911_cast)[name = tensor("denom_49_cast")]; + tensor out_49_cast = mul(x = zero_mean_49_cast, y = denom_49_cast)[name = tensor("out_49_cast")]; + tensor var_4915_to_fp16 = const()[name = tensor("op_4915_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484596992)))]; + tensor var_4916_cast = add(x = out_49_cast, y = var_4915_to_fp16)[name = tensor("op_4916_cast")]; + tensor var_4918_to_fp16 = const()[name = tensor("op_4918_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484599616)))]; + tensor hidden_states_187_cast = mul(x = var_4916_cast, y = var_4918_to_fp16)[name = tensor("hidden_states_187_cast")]; + tensor var_4925 = const()[name = tensor("op_4925"), val = tensor([1, 1])]; + tensor var_4927 = const()[name = tensor("op_4927"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484602240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(485831104))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_33_cast = conv(dilations = var_4927, groups = var_4272, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_4925, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor("q_33_cast")]; + tensor var_4931 = const()[name = tensor("op_4931"), val = tensor([1, 1])]; + tensor var_4933 = const()[name = tensor("op_4933"), val = tensor([1, 1])]; + tensor k_65_pad_type_0 = const()[name = tensor("k_65_pad_type_0"), val = tensor("custom")]; + tensor k_65_pad_0 = const()[name = tensor("k_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(485831296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487060160))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_65_cast = conv(dilations = var_4933, groups = var_4272, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_4931, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor("k_65_cast")]; + tensor var_4937 = const()[name = tensor("op_4937"), val = tensor([1, 1])]; + tensor var_4939 = const()[name = tensor("op_4939"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487060352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(488289216))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_33_cast = conv(dilations = var_4939, groups = var_4272, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_4937, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor("v_33_cast")]; + tensor var_4943_begin_0 = const()[name = tensor("op_4943_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4943_end_0 = const()[name = tensor("op_4943_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_4943_end_mask_0 = const()[name = tensor("op_4943_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4943_cast = slice_by_index(begin = var_4943_begin_0, end = var_4943_end_0, end_mask = var_4943_end_mask_0, x = q_33_cast)[name = tensor("op_4943_cast")]; + tensor var_4947_begin_0 = const()[name = tensor("op_4947_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_4947_end_0 = const()[name = tensor("op_4947_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_4947_end_mask_0 = const()[name = tensor("op_4947_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4947_cast = slice_by_index(begin = var_4947_begin_0, end = var_4947_end_0, end_mask = var_4947_end_mask_0, x = q_33_cast)[name = tensor("op_4947_cast")]; + tensor var_4951_begin_0 = const()[name = tensor("op_4951_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_4951_end_0 = const()[name = tensor("op_4951_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_4951_end_mask_0 = const()[name = tensor("op_4951_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4951_cast = slice_by_index(begin = var_4951_begin_0, end = var_4951_end_0, end_mask = var_4951_end_mask_0, x = q_33_cast)[name = tensor("op_4951_cast")]; + tensor var_4955_begin_0 = const()[name = tensor("op_4955_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_4955_end_0 = const()[name = tensor("op_4955_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_4955_end_mask_0 = const()[name = tensor("op_4955_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4955_cast = slice_by_index(begin = var_4955_begin_0, end = var_4955_end_0, end_mask = var_4955_end_mask_0, x = q_33_cast)[name = tensor("op_4955_cast")]; + tensor var_4959_begin_0 = const()[name = tensor("op_4959_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_4959_end_0 = const()[name = tensor("op_4959_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_4959_end_mask_0 = const()[name = tensor("op_4959_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4959_cast = slice_by_index(begin = var_4959_begin_0, end = var_4959_end_0, end_mask = var_4959_end_mask_0, x = q_33_cast)[name = tensor("op_4959_cast")]; + tensor var_4963_begin_0 = const()[name = tensor("op_4963_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_4963_end_0 = const()[name = tensor("op_4963_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_4963_end_mask_0 = const()[name = tensor("op_4963_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4963_cast = slice_by_index(begin = var_4963_begin_0, end = var_4963_end_0, end_mask = var_4963_end_mask_0, x = q_33_cast)[name = tensor("op_4963_cast")]; + tensor var_4967_begin_0 = const()[name = tensor("op_4967_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_4967_end_0 = const()[name = tensor("op_4967_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_4967_end_mask_0 = const()[name = tensor("op_4967_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4967_cast = slice_by_index(begin = var_4967_begin_0, end = var_4967_end_0, end_mask = var_4967_end_mask_0, x = q_33_cast)[name = tensor("op_4967_cast")]; + tensor var_4971_begin_0 = const()[name = tensor("op_4971_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_4971_end_0 = const()[name = tensor("op_4971_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_4971_end_mask_0 = const()[name = tensor("op_4971_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_4971_cast = slice_by_index(begin = var_4971_begin_0, end = var_4971_end_0, end_mask = var_4971_end_mask_0, x = q_33_cast)[name = tensor("op_4971_cast")]; + tensor k_67_perm_0 = const()[name = tensor("k_67_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_4978_begin_0 = const()[name = tensor("op_4978_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_4978_end_0 = const()[name = tensor("op_4978_end_0"), val = tensor([2, 576, 1, 160])]; + tensor var_4978_end_mask_0 = const()[name = tensor("op_4978_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_15 = transpose(perm = k_67_perm_0, x = k_65_cast)[name = tensor("transpose_15")]; + tensor var_4978_cast = slice_by_index(begin = var_4978_begin_0, end = var_4978_end_0, end_mask = var_4978_end_mask_0, x = transpose_15)[name = tensor("op_4978_cast")]; + tensor var_4982_begin_0 = const()[name = tensor("op_4982_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_4982_end_0 = const()[name = tensor("op_4982_end_0"), val = tensor([2, 576, 1, 320])]; + tensor var_4982_end_mask_0 = const()[name = tensor("op_4982_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4982_cast = slice_by_index(begin = var_4982_begin_0, end = var_4982_end_0, end_mask = var_4982_end_mask_0, x = transpose_15)[name = tensor("op_4982_cast")]; + tensor var_4986_begin_0 = const()[name = tensor("op_4986_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_4986_end_0 = const()[name = tensor("op_4986_end_0"), val = tensor([2, 576, 1, 480])]; + tensor var_4986_end_mask_0 = const()[name = tensor("op_4986_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4986_cast = slice_by_index(begin = var_4986_begin_0, end = var_4986_end_0, end_mask = var_4986_end_mask_0, x = transpose_15)[name = tensor("op_4986_cast")]; + tensor var_4990_begin_0 = const()[name = tensor("op_4990_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_4990_end_0 = const()[name = tensor("op_4990_end_0"), val = tensor([2, 576, 1, 640])]; + tensor var_4990_end_mask_0 = const()[name = tensor("op_4990_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4990_cast = slice_by_index(begin = var_4990_begin_0, end = var_4990_end_0, end_mask = var_4990_end_mask_0, x = transpose_15)[name = tensor("op_4990_cast")]; + tensor var_4994_begin_0 = const()[name = tensor("op_4994_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_4994_end_0 = const()[name = tensor("op_4994_end_0"), val = tensor([2, 576, 1, 800])]; + tensor var_4994_end_mask_0 = const()[name = tensor("op_4994_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4994_cast = slice_by_index(begin = var_4994_begin_0, end = var_4994_end_0, end_mask = var_4994_end_mask_0, x = transpose_15)[name = tensor("op_4994_cast")]; + tensor var_4998_begin_0 = const()[name = tensor("op_4998_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_4998_end_0 = const()[name = tensor("op_4998_end_0"), val = tensor([2, 576, 1, 960])]; + tensor var_4998_end_mask_0 = const()[name = tensor("op_4998_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_4998_cast = slice_by_index(begin = var_4998_begin_0, end = var_4998_end_0, end_mask = var_4998_end_mask_0, x = transpose_15)[name = tensor("op_4998_cast")]; + tensor var_5002_begin_0 = const()[name = tensor("op_5002_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_5002_end_0 = const()[name = tensor("op_5002_end_0"), val = tensor([2, 576, 1, 1120])]; + tensor var_5002_end_mask_0 = const()[name = tensor("op_5002_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5002_cast = slice_by_index(begin = var_5002_begin_0, end = var_5002_end_0, end_mask = var_5002_end_mask_0, x = transpose_15)[name = tensor("op_5002_cast")]; + tensor var_5006_begin_0 = const()[name = tensor("op_5006_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_5006_end_0 = const()[name = tensor("op_5006_end_0"), val = tensor([2, 576, 1, 1280])]; + tensor var_5006_end_mask_0 = const()[name = tensor("op_5006_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5006_cast = slice_by_index(begin = var_5006_begin_0, end = var_5006_end_0, end_mask = var_5006_end_mask_0, x = transpose_15)[name = tensor("op_5006_cast")]; + tensor var_5008_begin_0 = const()[name = tensor("op_5008_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5008_end_0 = const()[name = tensor("op_5008_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_5008_end_mask_0 = const()[name = tensor("op_5008_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5008_cast = slice_by_index(begin = var_5008_begin_0, end = var_5008_end_0, end_mask = var_5008_end_mask_0, x = v_33_cast)[name = tensor("op_5008_cast")]; + tensor var_5012_begin_0 = const()[name = tensor("op_5012_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_5012_end_0 = const()[name = tensor("op_5012_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_5012_end_mask_0 = const()[name = tensor("op_5012_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5012_cast = slice_by_index(begin = var_5012_begin_0, end = var_5012_end_0, end_mask = var_5012_end_mask_0, x = v_33_cast)[name = tensor("op_5012_cast")]; + tensor var_5016_begin_0 = const()[name = tensor("op_5016_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_5016_end_0 = const()[name = tensor("op_5016_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_5016_end_mask_0 = const()[name = tensor("op_5016_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5016_cast = slice_by_index(begin = var_5016_begin_0, end = var_5016_end_0, end_mask = var_5016_end_mask_0, x = v_33_cast)[name = tensor("op_5016_cast")]; + tensor var_5020_begin_0 = const()[name = tensor("op_5020_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_5020_end_0 = const()[name = tensor("op_5020_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_5020_end_mask_0 = const()[name = tensor("op_5020_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5020_cast = slice_by_index(begin = var_5020_begin_0, end = var_5020_end_0, end_mask = var_5020_end_mask_0, x = v_33_cast)[name = tensor("op_5020_cast")]; + tensor var_5024_begin_0 = const()[name = tensor("op_5024_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_5024_end_0 = const()[name = tensor("op_5024_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_5024_end_mask_0 = const()[name = tensor("op_5024_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5024_cast = slice_by_index(begin = var_5024_begin_0, end = var_5024_end_0, end_mask = var_5024_end_mask_0, x = v_33_cast)[name = tensor("op_5024_cast")]; + tensor var_5028_begin_0 = const()[name = tensor("op_5028_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_5028_end_0 = const()[name = tensor("op_5028_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_5028_end_mask_0 = const()[name = tensor("op_5028_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5028_cast = slice_by_index(begin = var_5028_begin_0, end = var_5028_end_0, end_mask = var_5028_end_mask_0, x = v_33_cast)[name = tensor("op_5028_cast")]; + tensor var_5032_begin_0 = const()[name = tensor("op_5032_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_5032_end_0 = const()[name = tensor("op_5032_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_5032_end_mask_0 = const()[name = tensor("op_5032_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5032_cast = slice_by_index(begin = var_5032_begin_0, end = var_5032_end_0, end_mask = var_5032_end_mask_0, x = v_33_cast)[name = tensor("op_5032_cast")]; + tensor var_5036_begin_0 = const()[name = tensor("op_5036_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_5036_end_0 = const()[name = tensor("op_5036_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_5036_end_mask_0 = const()[name = tensor("op_5036_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5036_cast = slice_by_index(begin = var_5036_begin_0, end = var_5036_end_0, end_mask = var_5036_end_mask_0, x = v_33_cast)[name = tensor("op_5036_cast")]; + tensor var_5040_equation_0 = const()[name = tensor("op_5040_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5040_cast = einsum(equation = var_5040_equation_0, values = (var_4978_cast, var_4943_cast))[name = tensor("op_5040_cast")]; + tensor var_5041_to_fp16 = const()[name = tensor("op_5041_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_257_cast = mul(x = var_5040_cast, y = var_5041_to_fp16)[name = tensor("aw_257_cast")]; + tensor var_5044_equation_0 = const()[name = tensor("op_5044_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5044_cast = einsum(equation = var_5044_equation_0, values = (var_4982_cast, var_4947_cast))[name = tensor("op_5044_cast")]; + tensor var_5045_to_fp16 = const()[name = tensor("op_5045_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_259_cast = mul(x = var_5044_cast, y = var_5045_to_fp16)[name = tensor("aw_259_cast")]; + tensor var_5048_equation_0 = const()[name = tensor("op_5048_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5048_cast = einsum(equation = var_5048_equation_0, values = (var_4986_cast, var_4951_cast))[name = tensor("op_5048_cast")]; + tensor var_5049_to_fp16 = const()[name = tensor("op_5049_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_261_cast = mul(x = var_5048_cast, y = var_5049_to_fp16)[name = tensor("aw_261_cast")]; + tensor var_5052_equation_0 = const()[name = tensor("op_5052_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5052_cast = einsum(equation = var_5052_equation_0, values = (var_4990_cast, var_4955_cast))[name = tensor("op_5052_cast")]; + tensor var_5053_to_fp16 = const()[name = tensor("op_5053_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_263_cast = mul(x = var_5052_cast, y = var_5053_to_fp16)[name = tensor("aw_263_cast")]; + tensor var_5056_equation_0 = const()[name = tensor("op_5056_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5056_cast = einsum(equation = var_5056_equation_0, values = (var_4994_cast, var_4959_cast))[name = tensor("op_5056_cast")]; + tensor var_5057_to_fp16 = const()[name = tensor("op_5057_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_265_cast = mul(x = var_5056_cast, y = var_5057_to_fp16)[name = tensor("aw_265_cast")]; + tensor var_5060_equation_0 = const()[name = tensor("op_5060_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5060_cast = einsum(equation = var_5060_equation_0, values = (var_4998_cast, var_4963_cast))[name = tensor("op_5060_cast")]; + tensor var_5061_to_fp16 = const()[name = tensor("op_5061_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_267_cast = mul(x = var_5060_cast, y = var_5061_to_fp16)[name = tensor("aw_267_cast")]; + tensor var_5064_equation_0 = const()[name = tensor("op_5064_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5064_cast = einsum(equation = var_5064_equation_0, values = (var_5002_cast, var_4967_cast))[name = tensor("op_5064_cast")]; + tensor var_5065_to_fp16 = const()[name = tensor("op_5065_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_269_cast = mul(x = var_5064_cast, y = var_5065_to_fp16)[name = tensor("aw_269_cast")]; + tensor var_5068_equation_0 = const()[name = tensor("op_5068_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5068_cast = einsum(equation = var_5068_equation_0, values = (var_5006_cast, var_4971_cast))[name = tensor("op_5068_cast")]; + tensor var_5069_to_fp16 = const()[name = tensor("op_5069_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_271_cast = mul(x = var_5068_cast, y = var_5069_to_fp16)[name = tensor("aw_271_cast")]; + tensor var_5071_cast = softmax(axis = var_4272, x = aw_257_cast)[name = tensor("op_5071_cast")]; + tensor var_5072_cast = softmax(axis = var_4272, x = aw_259_cast)[name = tensor("op_5072_cast")]; + tensor var_5073_cast = softmax(axis = var_4272, x = aw_261_cast)[name = tensor("op_5073_cast")]; + tensor var_5074_cast = softmax(axis = var_4272, x = aw_263_cast)[name = tensor("op_5074_cast")]; + tensor var_5075_cast = softmax(axis = var_4272, x = aw_265_cast)[name = tensor("op_5075_cast")]; + tensor var_5076_cast = softmax(axis = var_4272, x = aw_267_cast)[name = tensor("op_5076_cast")]; + tensor var_5077_cast = softmax(axis = var_4272, x = aw_269_cast)[name = tensor("op_5077_cast")]; + tensor var_5078_cast = softmax(axis = var_4272, x = aw_271_cast)[name = tensor("op_5078_cast")]; + tensor var_5080_equation_0 = const()[name = tensor("op_5080_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5080_cast = einsum(equation = var_5080_equation_0, values = (var_5008_cast, var_5071_cast))[name = tensor("op_5080_cast")]; + tensor var_5082_equation_0 = const()[name = tensor("op_5082_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5082_cast = einsum(equation = var_5082_equation_0, values = (var_5012_cast, var_5072_cast))[name = tensor("op_5082_cast")]; + tensor var_5084_equation_0 = const()[name = tensor("op_5084_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5084_cast = einsum(equation = var_5084_equation_0, values = (var_5016_cast, var_5073_cast))[name = tensor("op_5084_cast")]; + tensor var_5086_equation_0 = const()[name = tensor("op_5086_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5086_cast = einsum(equation = var_5086_equation_0, values = (var_5020_cast, var_5074_cast))[name = tensor("op_5086_cast")]; + tensor var_5088_equation_0 = const()[name = tensor("op_5088_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5088_cast = einsum(equation = var_5088_equation_0, values = (var_5024_cast, var_5075_cast))[name = tensor("op_5088_cast")]; + tensor var_5090_equation_0 = const()[name = tensor("op_5090_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5090_cast = einsum(equation = var_5090_equation_0, values = (var_5028_cast, var_5076_cast))[name = tensor("op_5090_cast")]; + tensor var_5092_equation_0 = const()[name = tensor("op_5092_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5092_cast = einsum(equation = var_5092_equation_0, values = (var_5032_cast, var_5077_cast))[name = tensor("op_5092_cast")]; + tensor var_5094_equation_0 = const()[name = tensor("op_5094_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5094_cast = einsum(equation = var_5094_equation_0, values = (var_5036_cast, var_5078_cast))[name = tensor("op_5094_cast")]; + tensor input_327_interleave_0 = const()[name = tensor("input_327_interleave_0"), val = tensor(false)]; + tensor input_327_cast = concat(axis = var_4272, interleave = input_327_interleave_0, values = (var_5080_cast, var_5082_cast, var_5084_cast, var_5086_cast, var_5088_cast, var_5090_cast, var_5092_cast, var_5094_cast))[name = tensor("input_327_cast")]; + tensor var_5100 = const()[name = tensor("op_5100"), val = tensor([1, 1])]; + tensor var_5102 = const()[name = tensor("op_5102"), val = tensor([1, 1])]; + tensor var_5104_pad_type_0 = const()[name = tensor("op_5104_pad_type_0"), val = tensor("custom")]; + tensor var_5104_pad_0 = const()[name = tensor("op_5104_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(488289408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489518272))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489518464)))]; + tensor var_5104_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5102, groups = var_4272, pad = var_5104_pad_0, pad_type = var_5104_pad_type_0, strides = var_5100, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_327_cast)[name = tensor("op_5104_cast")]; + tensor inputs_51_cast = add(x = var_5104_cast, y = inputs_49_cast)[name = tensor("inputs_51_cast")]; + tensor var_5108 = const()[name = tensor("op_5108"), val = tensor([1])]; + tensor channels_mean_51_cast = reduce_mean(axes = var_5108, keep_dims = var_4267, x = inputs_51_cast)[name = tensor("channels_mean_51_cast")]; + tensor zero_mean_51_cast = sub(x = inputs_51_cast, y = channels_mean_51_cast)[name = tensor("zero_mean_51_cast")]; + tensor zero_mean_sq_51_cast = mul(x = zero_mean_51_cast, y = zero_mean_51_cast)[name = tensor("zero_mean_sq_51_cast")]; + tensor var_5112 = const()[name = tensor("op_5112"), val = tensor([1])]; + tensor var_5113_cast = reduce_mean(axes = var_5112, keep_dims = var_4267, x = zero_mean_sq_51_cast)[name = tensor("op_5113_cast")]; + tensor var_5114_to_fp16 = const()[name = tensor("op_5114_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5115_cast = add(x = var_5113_cast, y = var_5114_to_fp16)[name = tensor("op_5115_cast")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_5115_cast)[name = tensor("denom_51_cast")]; + tensor out_51_cast = mul(x = zero_mean_51_cast, y = denom_51_cast)[name = tensor("out_51_cast")]; + tensor var_5119_to_fp16 = const()[name = tensor("op_5119_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489521088)))]; + tensor var_5120_cast = add(x = out_51_cast, y = var_5119_to_fp16)[name = tensor("op_5120_cast")]; + tensor var_5122_to_fp16 = const()[name = tensor("op_5122_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489523712)))]; + tensor hidden_states_189_cast = mul(x = var_5120_cast, y = var_5122_to_fp16)[name = tensor("hidden_states_189_cast")]; + tensor var_5129 = const()[name = tensor("op_5129"), val = tensor([1, 1])]; + tensor var_5131 = const()[name = tensor("op_5131"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489526336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(490755200))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_35_cast = conv(dilations = var_5131, groups = var_4272, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_5129, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_189_cast)[name = tensor("q_35_cast")]; + tensor var_5135 = const()[name = tensor("op_5135"), val = tensor([1, 1])]; + tensor var_5137 = const()[name = tensor("op_5137"), val = tensor([1, 1])]; + tensor k_69_pad_type_0 = const()[name = tensor("k_69_pad_type_0"), val = tensor("custom")]; + tensor k_69_pad_0 = const()[name = tensor("k_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(490755392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491492736))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_69_cast = conv(dilations = var_5137, groups = var_4272, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_5135, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_69_cast")]; + tensor var_5141 = const()[name = tensor("op_5141"), val = tensor([1, 1])]; + tensor var_5143 = const()[name = tensor("op_5143"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491492928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492230272))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_35_cast = conv(dilations = var_5143, groups = var_4272, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_5141, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_35_cast")]; + tensor var_5147_begin_0 = const()[name = tensor("op_5147_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5147_end_0 = const()[name = tensor("op_5147_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_5147_end_mask_0 = const()[name = tensor("op_5147_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5147_cast = slice_by_index(begin = var_5147_begin_0, end = var_5147_end_0, end_mask = var_5147_end_mask_0, x = q_35_cast)[name = tensor("op_5147_cast")]; + tensor var_5151_begin_0 = const()[name = tensor("op_5151_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_5151_end_0 = const()[name = tensor("op_5151_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_5151_end_mask_0 = const()[name = tensor("op_5151_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5151_cast = slice_by_index(begin = var_5151_begin_0, end = var_5151_end_0, end_mask = var_5151_end_mask_0, x = q_35_cast)[name = tensor("op_5151_cast")]; + tensor var_5155_begin_0 = const()[name = tensor("op_5155_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_5155_end_0 = const()[name = tensor("op_5155_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_5155_end_mask_0 = const()[name = tensor("op_5155_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5155_cast = slice_by_index(begin = var_5155_begin_0, end = var_5155_end_0, end_mask = var_5155_end_mask_0, x = q_35_cast)[name = tensor("op_5155_cast")]; + tensor var_5159_begin_0 = const()[name = tensor("op_5159_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_5159_end_0 = const()[name = tensor("op_5159_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_5159_end_mask_0 = const()[name = tensor("op_5159_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5159_cast = slice_by_index(begin = var_5159_begin_0, end = var_5159_end_0, end_mask = var_5159_end_mask_0, x = q_35_cast)[name = tensor("op_5159_cast")]; + tensor var_5163_begin_0 = const()[name = tensor("op_5163_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_5163_end_0 = const()[name = tensor("op_5163_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_5163_end_mask_0 = const()[name = tensor("op_5163_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5163_cast = slice_by_index(begin = var_5163_begin_0, end = var_5163_end_0, end_mask = var_5163_end_mask_0, x = q_35_cast)[name = tensor("op_5163_cast")]; + tensor var_5167_begin_0 = const()[name = tensor("op_5167_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_5167_end_0 = const()[name = tensor("op_5167_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_5167_end_mask_0 = const()[name = tensor("op_5167_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5167_cast = slice_by_index(begin = var_5167_begin_0, end = var_5167_end_0, end_mask = var_5167_end_mask_0, x = q_35_cast)[name = tensor("op_5167_cast")]; + tensor var_5171_begin_0 = const()[name = tensor("op_5171_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_5171_end_0 = const()[name = tensor("op_5171_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_5171_end_mask_0 = const()[name = tensor("op_5171_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5171_cast = slice_by_index(begin = var_5171_begin_0, end = var_5171_end_0, end_mask = var_5171_end_mask_0, x = q_35_cast)[name = tensor("op_5171_cast")]; + tensor var_5175_begin_0 = const()[name = tensor("op_5175_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_5175_end_0 = const()[name = tensor("op_5175_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_5175_end_mask_0 = const()[name = tensor("op_5175_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5175_cast = slice_by_index(begin = var_5175_begin_0, end = var_5175_end_0, end_mask = var_5175_end_mask_0, x = q_35_cast)[name = tensor("op_5175_cast")]; + tensor k_71_perm_0 = const()[name = tensor("k_71_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_5182_begin_0 = const()[name = tensor("op_5182_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5182_end_0 = const()[name = tensor("op_5182_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_5182_end_mask_0 = const()[name = tensor("op_5182_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_14 = transpose(perm = k_71_perm_0, x = k_69_cast)[name = tensor("transpose_14")]; + tensor var_5182_cast = slice_by_index(begin = var_5182_begin_0, end = var_5182_end_0, end_mask = var_5182_end_mask_0, x = transpose_14)[name = tensor("op_5182_cast")]; + tensor var_5186_begin_0 = const()[name = tensor("op_5186_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_5186_end_0 = const()[name = tensor("op_5186_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_5186_end_mask_0 = const()[name = tensor("op_5186_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5186_cast = slice_by_index(begin = var_5186_begin_0, end = var_5186_end_0, end_mask = var_5186_end_mask_0, x = transpose_14)[name = tensor("op_5186_cast")]; + tensor var_5190_begin_0 = const()[name = tensor("op_5190_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_5190_end_0 = const()[name = tensor("op_5190_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_5190_end_mask_0 = const()[name = tensor("op_5190_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5190_cast = slice_by_index(begin = var_5190_begin_0, end = var_5190_end_0, end_mask = var_5190_end_mask_0, x = transpose_14)[name = tensor("op_5190_cast")]; + tensor var_5194_begin_0 = const()[name = tensor("op_5194_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_5194_end_0 = const()[name = tensor("op_5194_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_5194_end_mask_0 = const()[name = tensor("op_5194_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5194_cast = slice_by_index(begin = var_5194_begin_0, end = var_5194_end_0, end_mask = var_5194_end_mask_0, x = transpose_14)[name = tensor("op_5194_cast")]; + tensor var_5198_begin_0 = const()[name = tensor("op_5198_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_5198_end_0 = const()[name = tensor("op_5198_end_0"), val = tensor([2, 77, 1, 800])]; + tensor var_5198_end_mask_0 = const()[name = tensor("op_5198_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5198_cast = slice_by_index(begin = var_5198_begin_0, end = var_5198_end_0, end_mask = var_5198_end_mask_0, x = transpose_14)[name = tensor("op_5198_cast")]; + tensor var_5202_begin_0 = const()[name = tensor("op_5202_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_5202_end_0 = const()[name = tensor("op_5202_end_0"), val = tensor([2, 77, 1, 960])]; + tensor var_5202_end_mask_0 = const()[name = tensor("op_5202_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5202_cast = slice_by_index(begin = var_5202_begin_0, end = var_5202_end_0, end_mask = var_5202_end_mask_0, x = transpose_14)[name = tensor("op_5202_cast")]; + tensor var_5206_begin_0 = const()[name = tensor("op_5206_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_5206_end_0 = const()[name = tensor("op_5206_end_0"), val = tensor([2, 77, 1, 1120])]; + tensor var_5206_end_mask_0 = const()[name = tensor("op_5206_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5206_cast = slice_by_index(begin = var_5206_begin_0, end = var_5206_end_0, end_mask = var_5206_end_mask_0, x = transpose_14)[name = tensor("op_5206_cast")]; + tensor var_5210_begin_0 = const()[name = tensor("op_5210_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_5210_end_0 = const()[name = tensor("op_5210_end_0"), val = tensor([2, 77, 1, 1280])]; + tensor var_5210_end_mask_0 = const()[name = tensor("op_5210_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5210_cast = slice_by_index(begin = var_5210_begin_0, end = var_5210_end_0, end_mask = var_5210_end_mask_0, x = transpose_14)[name = tensor("op_5210_cast")]; + tensor var_5212_begin_0 = const()[name = tensor("op_5212_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5212_end_0 = const()[name = tensor("op_5212_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_5212_end_mask_0 = const()[name = tensor("op_5212_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5212_cast = slice_by_index(begin = var_5212_begin_0, end = var_5212_end_0, end_mask = var_5212_end_mask_0, x = v_35_cast)[name = tensor("op_5212_cast")]; + tensor var_5216_begin_0 = const()[name = tensor("op_5216_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_5216_end_0 = const()[name = tensor("op_5216_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_5216_end_mask_0 = const()[name = tensor("op_5216_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5216_cast = slice_by_index(begin = var_5216_begin_0, end = var_5216_end_0, end_mask = var_5216_end_mask_0, x = v_35_cast)[name = tensor("op_5216_cast")]; + tensor var_5220_begin_0 = const()[name = tensor("op_5220_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_5220_end_0 = const()[name = tensor("op_5220_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_5220_end_mask_0 = const()[name = tensor("op_5220_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5220_cast = slice_by_index(begin = var_5220_begin_0, end = var_5220_end_0, end_mask = var_5220_end_mask_0, x = v_35_cast)[name = tensor("op_5220_cast")]; + tensor var_5224_begin_0 = const()[name = tensor("op_5224_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_5224_end_0 = const()[name = tensor("op_5224_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_5224_end_mask_0 = const()[name = tensor("op_5224_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5224_cast = slice_by_index(begin = var_5224_begin_0, end = var_5224_end_0, end_mask = var_5224_end_mask_0, x = v_35_cast)[name = tensor("op_5224_cast")]; + tensor var_5228_begin_0 = const()[name = tensor("op_5228_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_5228_end_0 = const()[name = tensor("op_5228_end_0"), val = tensor([2, 800, 1, 77])]; + tensor var_5228_end_mask_0 = const()[name = tensor("op_5228_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5228_cast = slice_by_index(begin = var_5228_begin_0, end = var_5228_end_0, end_mask = var_5228_end_mask_0, x = v_35_cast)[name = tensor("op_5228_cast")]; + tensor var_5232_begin_0 = const()[name = tensor("op_5232_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_5232_end_0 = const()[name = tensor("op_5232_end_0"), val = tensor([2, 960, 1, 77])]; + tensor var_5232_end_mask_0 = const()[name = tensor("op_5232_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5232_cast = slice_by_index(begin = var_5232_begin_0, end = var_5232_end_0, end_mask = var_5232_end_mask_0, x = v_35_cast)[name = tensor("op_5232_cast")]; + tensor var_5236_begin_0 = const()[name = tensor("op_5236_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_5236_end_0 = const()[name = tensor("op_5236_end_0"), val = tensor([2, 1120, 1, 77])]; + tensor var_5236_end_mask_0 = const()[name = tensor("op_5236_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5236_cast = slice_by_index(begin = var_5236_begin_0, end = var_5236_end_0, end_mask = var_5236_end_mask_0, x = v_35_cast)[name = tensor("op_5236_cast")]; + tensor var_5240_begin_0 = const()[name = tensor("op_5240_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_5240_end_0 = const()[name = tensor("op_5240_end_0"), val = tensor([2, 1280, 1, 77])]; + tensor var_5240_end_mask_0 = const()[name = tensor("op_5240_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5240_cast = slice_by_index(begin = var_5240_begin_0, end = var_5240_end_0, end_mask = var_5240_end_mask_0, x = v_35_cast)[name = tensor("op_5240_cast")]; + tensor var_5244_equation_0 = const()[name = tensor("op_5244_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5244_cast = einsum(equation = var_5244_equation_0, values = (var_5182_cast, var_5147_cast))[name = tensor("op_5244_cast")]; + tensor var_5245_to_fp16 = const()[name = tensor("op_5245_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_273_cast = mul(x = var_5244_cast, y = var_5245_to_fp16)[name = tensor("aw_273_cast")]; + tensor var_5248_equation_0 = const()[name = tensor("op_5248_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5248_cast = einsum(equation = var_5248_equation_0, values = (var_5186_cast, var_5151_cast))[name = tensor("op_5248_cast")]; + tensor var_5249_to_fp16 = const()[name = tensor("op_5249_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_275_cast = mul(x = var_5248_cast, y = var_5249_to_fp16)[name = tensor("aw_275_cast")]; + tensor var_5252_equation_0 = const()[name = tensor("op_5252_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5252_cast = einsum(equation = var_5252_equation_0, values = (var_5190_cast, var_5155_cast))[name = tensor("op_5252_cast")]; + tensor var_5253_to_fp16 = const()[name = tensor("op_5253_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_277_cast = mul(x = var_5252_cast, y = var_5253_to_fp16)[name = tensor("aw_277_cast")]; + tensor var_5256_equation_0 = const()[name = tensor("op_5256_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5256_cast = einsum(equation = var_5256_equation_0, values = (var_5194_cast, var_5159_cast))[name = tensor("op_5256_cast")]; + tensor var_5257_to_fp16 = const()[name = tensor("op_5257_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_279_cast = mul(x = var_5256_cast, y = var_5257_to_fp16)[name = tensor("aw_279_cast")]; + tensor var_5260_equation_0 = const()[name = tensor("op_5260_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5260_cast = einsum(equation = var_5260_equation_0, values = (var_5198_cast, var_5163_cast))[name = tensor("op_5260_cast")]; + tensor var_5261_to_fp16 = const()[name = tensor("op_5261_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_281_cast = mul(x = var_5260_cast, y = var_5261_to_fp16)[name = tensor("aw_281_cast")]; + tensor var_5264_equation_0 = const()[name = tensor("op_5264_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5264_cast = einsum(equation = var_5264_equation_0, values = (var_5202_cast, var_5167_cast))[name = tensor("op_5264_cast")]; + tensor var_5265_to_fp16 = const()[name = tensor("op_5265_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_283_cast = mul(x = var_5264_cast, y = var_5265_to_fp16)[name = tensor("aw_283_cast")]; + tensor var_5268_equation_0 = const()[name = tensor("op_5268_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5268_cast = einsum(equation = var_5268_equation_0, values = (var_5206_cast, var_5171_cast))[name = tensor("op_5268_cast")]; + tensor var_5269_to_fp16 = const()[name = tensor("op_5269_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_285_cast = mul(x = var_5268_cast, y = var_5269_to_fp16)[name = tensor("aw_285_cast")]; + tensor var_5272_equation_0 = const()[name = tensor("op_5272_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5272_cast = einsum(equation = var_5272_equation_0, values = (var_5210_cast, var_5175_cast))[name = tensor("op_5272_cast")]; + tensor var_5273_to_fp16 = const()[name = tensor("op_5273_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_287_cast = mul(x = var_5272_cast, y = var_5273_to_fp16)[name = tensor("aw_287_cast")]; + tensor var_5275_cast = softmax(axis = var_4272, x = aw_273_cast)[name = tensor("op_5275_cast")]; + tensor var_5276_cast = softmax(axis = var_4272, x = aw_275_cast)[name = tensor("op_5276_cast")]; + tensor var_5277_cast = softmax(axis = var_4272, x = aw_277_cast)[name = tensor("op_5277_cast")]; + tensor var_5278_cast = softmax(axis = var_4272, x = aw_279_cast)[name = tensor("op_5278_cast")]; + tensor var_5279_cast = softmax(axis = var_4272, x = aw_281_cast)[name = tensor("op_5279_cast")]; + tensor var_5280_cast = softmax(axis = var_4272, x = aw_283_cast)[name = tensor("op_5280_cast")]; + tensor var_5281_cast = softmax(axis = var_4272, x = aw_285_cast)[name = tensor("op_5281_cast")]; + tensor var_5282_cast = softmax(axis = var_4272, x = aw_287_cast)[name = tensor("op_5282_cast")]; + tensor var_5284_equation_0 = const()[name = tensor("op_5284_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5284_cast = einsum(equation = var_5284_equation_0, values = (var_5212_cast, var_5275_cast))[name = tensor("op_5284_cast")]; + tensor var_5286_equation_0 = const()[name = tensor("op_5286_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5286_cast = einsum(equation = var_5286_equation_0, values = (var_5216_cast, var_5276_cast))[name = tensor("op_5286_cast")]; + tensor var_5288_equation_0 = const()[name = tensor("op_5288_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5288_cast = einsum(equation = var_5288_equation_0, values = (var_5220_cast, var_5277_cast))[name = tensor("op_5288_cast")]; + tensor var_5290_equation_0 = const()[name = tensor("op_5290_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5290_cast = einsum(equation = var_5290_equation_0, values = (var_5224_cast, var_5278_cast))[name = tensor("op_5290_cast")]; + tensor var_5292_equation_0 = const()[name = tensor("op_5292_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5292_cast = einsum(equation = var_5292_equation_0, values = (var_5228_cast, var_5279_cast))[name = tensor("op_5292_cast")]; + tensor var_5294_equation_0 = const()[name = tensor("op_5294_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5294_cast = einsum(equation = var_5294_equation_0, values = (var_5232_cast, var_5280_cast))[name = tensor("op_5294_cast")]; + tensor var_5296_equation_0 = const()[name = tensor("op_5296_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5296_cast = einsum(equation = var_5296_equation_0, values = (var_5236_cast, var_5281_cast))[name = tensor("op_5296_cast")]; + tensor var_5298_equation_0 = const()[name = tensor("op_5298_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5298_cast = einsum(equation = var_5298_equation_0, values = (var_5240_cast, var_5282_cast))[name = tensor("op_5298_cast")]; + tensor input_329_interleave_0 = const()[name = tensor("input_329_interleave_0"), val = tensor(false)]; + tensor input_329_cast = concat(axis = var_4272, interleave = input_329_interleave_0, values = (var_5284_cast, var_5286_cast, var_5288_cast, var_5290_cast, var_5292_cast, var_5294_cast, var_5296_cast, var_5298_cast))[name = tensor("input_329_cast")]; + tensor var_5304 = const()[name = tensor("op_5304"), val = tensor([1, 1])]; + tensor var_5306 = const()[name = tensor("op_5306"), val = tensor([1, 1])]; + tensor var_5308_pad_type_0 = const()[name = tensor("op_5308_pad_type_0"), val = tensor("custom")]; + tensor var_5308_pad_0 = const()[name = tensor("op_5308_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492230464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493459328))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493459520)))]; + tensor var_5308_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5306, groups = var_4272, pad = var_5308_pad_0, pad_type = var_5308_pad_type_0, strides = var_5304, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_329_cast)[name = tensor("op_5308_cast")]; + tensor inputs_53_cast = add(x = var_5308_cast, y = inputs_51_cast)[name = tensor("inputs_53_cast")]; + tensor var_5312 = const()[name = tensor("op_5312"), val = tensor([1])]; + tensor channels_mean_53_cast = reduce_mean(axes = var_5312, keep_dims = var_4267, x = inputs_53_cast)[name = tensor("channels_mean_53_cast")]; + tensor zero_mean_53_cast = sub(x = inputs_53_cast, y = channels_mean_53_cast)[name = tensor("zero_mean_53_cast")]; + tensor zero_mean_sq_53_cast = mul(x = zero_mean_53_cast, y = zero_mean_53_cast)[name = tensor("zero_mean_sq_53_cast")]; + tensor var_5316 = const()[name = tensor("op_5316"), val = tensor([1])]; + tensor var_5317_cast = reduce_mean(axes = var_5316, keep_dims = var_4267, x = zero_mean_sq_53_cast)[name = tensor("op_5317_cast")]; + tensor var_5318_to_fp16 = const()[name = tensor("op_5318_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5319_cast = add(x = var_5317_cast, y = var_5318_to_fp16)[name = tensor("op_5319_cast")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_5319_cast)[name = tensor("denom_53_cast")]; + tensor out_53_cast = mul(x = zero_mean_53_cast, y = denom_53_cast)[name = tensor("out_53_cast")]; + tensor var_5323_to_fp16 = const()[name = tensor("op_5323_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493462144)))]; + tensor var_5324_cast = add(x = out_53_cast, y = var_5323_to_fp16)[name = tensor("op_5324_cast")]; + tensor var_5326_to_fp16 = const()[name = tensor("op_5326_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493464768)))]; + tensor input_331_cast = mul(x = var_5324_cast, y = var_5326_to_fp16)[name = tensor("input_331_cast")]; + tensor var_5334 = const()[name = tensor("op_5334"), val = tensor([1, 1])]; + tensor var_5336 = const()[name = tensor("op_5336"), val = tensor([1, 1])]; + tensor var_5338_pad_type_0 = const()[name = tensor("op_5338_pad_type_0"), val = tensor("custom")]; + tensor var_5338_pad_0 = const()[name = tensor("op_5338_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493467392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503297856))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503298048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503305792))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5338_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5336, groups = var_4272, pad = var_5338_pad_0, pad_type = var_5338_pad_type_0, strides = var_5334, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_331_cast)[name = tensor("op_5338_cast")]; + tensor var_5339_split_sizes_0 = const()[name = tensor("op_5339_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5339_axis_0 = const()[name = tensor("op_5339_axis_0"), val = tensor(1)]; + tensor var_5339_cast_0, tensor var_5339_cast_1 = split(axis = var_5339_axis_0, split_sizes = var_5339_split_sizes_0, x = var_5338_cast)[name = tensor("op_5339_cast")]; + tensor var_5341_mode_0 = const()[name = tensor("op_5341_mode_0"), val = tensor("EXACT")]; + tensor var_5341_cast = gelu(mode = var_5341_mode_0, x = var_5339_cast_1)[name = tensor("op_5341_cast")]; + tensor input_333_cast = mul(x = var_5339_cast_0, y = var_5341_cast)[name = tensor("input_333_cast")]; + tensor var_5345 = const()[name = tensor("op_5345"), val = tensor([1, 1])]; + tensor var_5347 = const()[name = tensor("op_5347"), val = tensor([1, 1])]; + tensor var_5349_pad_type_0 = const()[name = tensor("op_5349_pad_type_0"), val = tensor("custom")]; + tensor var_5349_pad_0 = const()[name = tensor("op_5349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503305984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508221248))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508221440)))]; + tensor var_5349_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5347, groups = var_4272, pad = var_5349_pad_0, pad_type = var_5349_pad_type_0, strides = var_5345, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_333_cast)[name = tensor("op_5349_cast")]; + tensor hidden_states_193_cast = add(x = var_5349_cast, y = inputs_53_cast)[name = tensor("hidden_states_193_cast")]; + tensor var_5351 = const()[name = tensor("op_5351"), val = tensor([2, 1280, 24, 24])]; + tensor input_335_cast = reshape(shape = var_5351, x = hidden_states_193_cast)[name = tensor("input_335_cast")]; + tensor var_5355 = const()[name = tensor("op_5355"), val = tensor([1, 1])]; + tensor var_5357 = const()[name = tensor("op_5357"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_type_0 = const()[name = tensor("hidden_states_195_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_195_pad_0 = const()[name = tensor("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508224064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509452928))), name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509453120)))]; + tensor hidden_states_195_cast = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_5357, groups = var_4272, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_5355, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_335_cast)[name = tensor("hidden_states_195_cast")]; + tensor hidden_states_197_cast = add(x = hidden_states_195_cast, y = hidden_states_183_cast)[name = tensor("hidden_states_197_cast")]; + tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; + tensor input_337_cast = concat(axis = var_4272, interleave = input_337_interleave_0, values = (hidden_states_197_cast, input_117_cast))[name = tensor("input_337_cast")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 60, 24, 24])]; + tensor reshape_156_cast = reshape(shape = reshape_156_shape_0, x = input_337_cast)[name = tensor("reshape_156_cast")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_117_cast")]; + tensor sub_78_cast = sub(x = reshape_156_cast, y = reduce_mean_117_cast)[name = tensor("sub_78_cast")]; + tensor square_39_cast = square(x = sub_78_cast)[name = tensor("square_39_cast")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_119_cast")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast = add(x = reduce_mean_119_cast, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast")]; + tensor sqrt_39_cast = sqrt(x = add_78_cast)[name = tensor("sqrt_39_cast")]; + tensor real_div_39_cast = real_div(x = sub_78_cast, y = sqrt_39_cast)[name = tensor("real_div_39_cast")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 1920, 24, 24])]; + tensor reshape_157_cast = reshape(shape = reshape_157_shape_0, x = real_div_39_cast)[name = tensor("reshape_157_cast")]; + tensor add_79_mean_0_to_fp16 = const()[name = tensor("add_79_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509455744)))]; + tensor add_79_variance_0_to_fp16 = const()[name = tensor("add_79_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509459648)))]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509463552)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509467456)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_79_cast")]; + tensor input_341_cast = silu(x = add_79_cast)[name = tensor("input_341_cast")]; + tensor var_5375 = const()[name = tensor("op_5375"), val = tensor([1, 1])]; + tensor var_5377 = const()[name = tensor("op_5377"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509471360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526060224))), name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 3, 3])]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526060416)))]; + tensor hidden_states_199_cast = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_5377, groups = var_4272, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_5375, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized, x = input_341_cast)[name = tensor("hidden_states_199_cast")]; + tensor var_5383 = const()[name = tensor("op_5383"), val = tensor([1, 1])]; + tensor var_5385 = const()[name = tensor("op_5385"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526063040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527291904))), name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527292096)))]; + tensor temb_31_cast = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_5385, groups = var_4272, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_5383, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_31_cast")]; + tensor input_345_cast = add(x = hidden_states_199_cast, y = temb_31_cast)[name = tensor("input_345_cast")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_160_cast = reshape(shape = reshape_160_shape_0, x = input_345_cast)[name = tensor("reshape_160_cast")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_120_cast")]; + tensor sub_80_cast = sub(x = reshape_160_cast, y = reduce_mean_120_cast)[name = tensor("sub_80_cast")]; + tensor square_40_cast = square(x = sub_80_cast)[name = tensor("square_40_cast")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_122_cast")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast = add(x = reduce_mean_122_cast, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast")]; + tensor sqrt_40_cast = sqrt(x = add_80_cast)[name = tensor("sqrt_40_cast")]; + tensor real_div_40_cast = real_div(x = sub_80_cast, y = sqrt_40_cast)[name = tensor("real_div_40_cast")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_161_cast = reshape(shape = reshape_161_shape_0, x = real_div_40_cast)[name = tensor("reshape_161_cast")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527294720)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527297344)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_81_cast")]; + tensor input_349_cast = silu(x = add_81_cast)[name = tensor("input_349_cast")]; + tensor var_5395 = const()[name = tensor("op_5395"), val = tensor([1, 1])]; + tensor var_5397 = const()[name = tensor("op_5397"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527299968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538359232))), name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538359424)))]; + tensor hidden_states_201_cast = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_5397, groups = var_4272, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_5395, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized, x = input_349_cast)[name = tensor("hidden_states_201_cast")]; + tensor var_5402 = const()[name = tensor("op_5402"), val = tensor([1, 1])]; + tensor var_5404 = const()[name = tensor("op_5404"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538362048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540205312))), name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 1, 1])]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540205504)))]; + tensor x_15_cast = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_5404, groups = var_4272, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_5402, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_337_cast)[name = tensor("x_15_cast")]; + tensor hidden_states_203_cast = add(x = x_15_cast, y = hidden_states_201_cast)[name = tensor("hidden_states_203_cast")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 40, 24, 24])]; + tensor reshape_164_cast = reshape(shape = reshape_164_shape_0, x = hidden_states_203_cast)[name = tensor("reshape_164_cast")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_123_cast")]; + tensor sub_82_cast = sub(x = reshape_164_cast, y = reduce_mean_123_cast)[name = tensor("sub_82_cast")]; + tensor square_41_cast = square(x = sub_82_cast)[name = tensor("square_41_cast")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_125_cast")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_82_cast = add(x = reduce_mean_125_cast, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast")]; + tensor sqrt_41_cast = sqrt(x = add_82_cast)[name = tensor("sqrt_41_cast")]; + tensor real_div_41_cast = real_div(x = sub_82_cast, y = sqrt_41_cast)[name = tensor("real_div_41_cast")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 1280, 24, 24])]; + tensor reshape_165_cast = reshape(shape = reshape_165_shape_0, x = real_div_41_cast)[name = tensor("reshape_165_cast")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540208128)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540210752)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_83_cast")]; + tensor var_5424 = const()[name = tensor("op_5424"), val = tensor([1, 1])]; + tensor var_5426 = const()[name = tensor("op_5426"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_type_0 = const()[name = tensor("hidden_states_205_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_205_pad_0 = const()[name = tensor("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540213376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541442240))), name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541442432)))]; + tensor hidden_states_205_cast = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_5426, groups = var_4272, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_5424, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized, x = add_83_cast)[name = tensor("hidden_states_205_cast")]; + tensor var_5431 = const()[name = tensor("op_5431"), val = tensor([2, 1280, 1, 576])]; + tensor inputs_55_cast = reshape(shape = var_5431, x = hidden_states_205_cast)[name = tensor("inputs_55_cast")]; + tensor var_5441 = const()[name = tensor("op_5441"), val = tensor([1])]; + tensor channels_mean_55_cast = reduce_mean(axes = var_5441, keep_dims = var_4267, x = inputs_55_cast)[name = tensor("channels_mean_55_cast")]; + tensor zero_mean_55_cast = sub(x = inputs_55_cast, y = channels_mean_55_cast)[name = tensor("zero_mean_55_cast")]; + tensor zero_mean_sq_55_cast = mul(x = zero_mean_55_cast, y = zero_mean_55_cast)[name = tensor("zero_mean_sq_55_cast")]; + tensor var_5445 = const()[name = tensor("op_5445"), val = tensor([1])]; + tensor var_5446_cast = reduce_mean(axes = var_5445, keep_dims = var_4267, x = zero_mean_sq_55_cast)[name = tensor("op_5446_cast")]; + tensor var_5447_to_fp16 = const()[name = tensor("op_5447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5448_cast = add(x = var_5446_cast, y = var_5447_to_fp16)[name = tensor("op_5448_cast")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_5448_cast)[name = tensor("denom_55_cast")]; + tensor out_55_cast = mul(x = zero_mean_55_cast, y = denom_55_cast)[name = tensor("out_55_cast")]; + tensor var_5452_to_fp16 = const()[name = tensor("op_5452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541445056)))]; + tensor var_5453_cast = add(x = out_55_cast, y = var_5452_to_fp16)[name = tensor("op_5453_cast")]; + tensor var_5455_to_fp16 = const()[name = tensor("op_5455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541447680)))]; + tensor hidden_states_207_cast = mul(x = var_5453_cast, y = var_5455_to_fp16)[name = tensor("hidden_states_207_cast")]; + tensor var_5462 = const()[name = tensor("op_5462"), val = tensor([1, 1])]; + tensor var_5464 = const()[name = tensor("op_5464"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541450304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(542679168))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_37_cast = conv(dilations = var_5464, groups = var_4272, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_5462, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor("q_37_cast")]; + tensor var_5468 = const()[name = tensor("op_5468"), val = tensor([1, 1])]; + tensor var_5470 = const()[name = tensor("op_5470"), val = tensor([1, 1])]; + tensor k_73_pad_type_0 = const()[name = tensor("k_73_pad_type_0"), val = tensor("custom")]; + tensor k_73_pad_0 = const()[name = tensor("k_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(542679360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543908224))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_73_cast = conv(dilations = var_5470, groups = var_4272, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_5468, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor("k_73_cast")]; + tensor var_5474 = const()[name = tensor("op_5474"), val = tensor([1, 1])]; + tensor var_5476 = const()[name = tensor("op_5476"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543908416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545137280))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_37_cast = conv(dilations = var_5476, groups = var_4272, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_5474, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor("v_37_cast")]; + tensor var_5480_begin_0 = const()[name = tensor("op_5480_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5480_end_0 = const()[name = tensor("op_5480_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_5480_end_mask_0 = const()[name = tensor("op_5480_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5480_cast = slice_by_index(begin = var_5480_begin_0, end = var_5480_end_0, end_mask = var_5480_end_mask_0, x = q_37_cast)[name = tensor("op_5480_cast")]; + tensor var_5484_begin_0 = const()[name = tensor("op_5484_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_5484_end_0 = const()[name = tensor("op_5484_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_5484_end_mask_0 = const()[name = tensor("op_5484_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5484_cast = slice_by_index(begin = var_5484_begin_0, end = var_5484_end_0, end_mask = var_5484_end_mask_0, x = q_37_cast)[name = tensor("op_5484_cast")]; + tensor var_5488_begin_0 = const()[name = tensor("op_5488_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_5488_end_0 = const()[name = tensor("op_5488_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_5488_end_mask_0 = const()[name = tensor("op_5488_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5488_cast = slice_by_index(begin = var_5488_begin_0, end = var_5488_end_0, end_mask = var_5488_end_mask_0, x = q_37_cast)[name = tensor("op_5488_cast")]; + tensor var_5492_begin_0 = const()[name = tensor("op_5492_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_5492_end_0 = const()[name = tensor("op_5492_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_5492_end_mask_0 = const()[name = tensor("op_5492_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5492_cast = slice_by_index(begin = var_5492_begin_0, end = var_5492_end_0, end_mask = var_5492_end_mask_0, x = q_37_cast)[name = tensor("op_5492_cast")]; + tensor var_5496_begin_0 = const()[name = tensor("op_5496_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_5496_end_0 = const()[name = tensor("op_5496_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_5496_end_mask_0 = const()[name = tensor("op_5496_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5496_cast = slice_by_index(begin = var_5496_begin_0, end = var_5496_end_0, end_mask = var_5496_end_mask_0, x = q_37_cast)[name = tensor("op_5496_cast")]; + tensor var_5500_begin_0 = const()[name = tensor("op_5500_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_5500_end_0 = const()[name = tensor("op_5500_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_5500_end_mask_0 = const()[name = tensor("op_5500_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5500_cast = slice_by_index(begin = var_5500_begin_0, end = var_5500_end_0, end_mask = var_5500_end_mask_0, x = q_37_cast)[name = tensor("op_5500_cast")]; + tensor var_5504_begin_0 = const()[name = tensor("op_5504_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_5504_end_0 = const()[name = tensor("op_5504_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_5504_end_mask_0 = const()[name = tensor("op_5504_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5504_cast = slice_by_index(begin = var_5504_begin_0, end = var_5504_end_0, end_mask = var_5504_end_mask_0, x = q_37_cast)[name = tensor("op_5504_cast")]; + tensor var_5508_begin_0 = const()[name = tensor("op_5508_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_5508_end_0 = const()[name = tensor("op_5508_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_5508_end_mask_0 = const()[name = tensor("op_5508_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5508_cast = slice_by_index(begin = var_5508_begin_0, end = var_5508_end_0, end_mask = var_5508_end_mask_0, x = q_37_cast)[name = tensor("op_5508_cast")]; + tensor k_75_perm_0 = const()[name = tensor("k_75_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_5515_begin_0 = const()[name = tensor("op_5515_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5515_end_0 = const()[name = tensor("op_5515_end_0"), val = tensor([2, 576, 1, 160])]; + tensor var_5515_end_mask_0 = const()[name = tensor("op_5515_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_13 = transpose(perm = k_75_perm_0, x = k_73_cast)[name = tensor("transpose_13")]; + tensor var_5515_cast = slice_by_index(begin = var_5515_begin_0, end = var_5515_end_0, end_mask = var_5515_end_mask_0, x = transpose_13)[name = tensor("op_5515_cast")]; + tensor var_5519_begin_0 = const()[name = tensor("op_5519_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_5519_end_0 = const()[name = tensor("op_5519_end_0"), val = tensor([2, 576, 1, 320])]; + tensor var_5519_end_mask_0 = const()[name = tensor("op_5519_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5519_cast = slice_by_index(begin = var_5519_begin_0, end = var_5519_end_0, end_mask = var_5519_end_mask_0, x = transpose_13)[name = tensor("op_5519_cast")]; + tensor var_5523_begin_0 = const()[name = tensor("op_5523_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_5523_end_0 = const()[name = tensor("op_5523_end_0"), val = tensor([2, 576, 1, 480])]; + tensor var_5523_end_mask_0 = const()[name = tensor("op_5523_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5523_cast = slice_by_index(begin = var_5523_begin_0, end = var_5523_end_0, end_mask = var_5523_end_mask_0, x = transpose_13)[name = tensor("op_5523_cast")]; + tensor var_5527_begin_0 = const()[name = tensor("op_5527_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_5527_end_0 = const()[name = tensor("op_5527_end_0"), val = tensor([2, 576, 1, 640])]; + tensor var_5527_end_mask_0 = const()[name = tensor("op_5527_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5527_cast = slice_by_index(begin = var_5527_begin_0, end = var_5527_end_0, end_mask = var_5527_end_mask_0, x = transpose_13)[name = tensor("op_5527_cast")]; + tensor var_5531_begin_0 = const()[name = tensor("op_5531_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_5531_end_0 = const()[name = tensor("op_5531_end_0"), val = tensor([2, 576, 1, 800])]; + tensor var_5531_end_mask_0 = const()[name = tensor("op_5531_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5531_cast = slice_by_index(begin = var_5531_begin_0, end = var_5531_end_0, end_mask = var_5531_end_mask_0, x = transpose_13)[name = tensor("op_5531_cast")]; + tensor var_5535_begin_0 = const()[name = tensor("op_5535_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_5535_end_0 = const()[name = tensor("op_5535_end_0"), val = tensor([2, 576, 1, 960])]; + tensor var_5535_end_mask_0 = const()[name = tensor("op_5535_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5535_cast = slice_by_index(begin = var_5535_begin_0, end = var_5535_end_0, end_mask = var_5535_end_mask_0, x = transpose_13)[name = tensor("op_5535_cast")]; + tensor var_5539_begin_0 = const()[name = tensor("op_5539_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_5539_end_0 = const()[name = tensor("op_5539_end_0"), val = tensor([2, 576, 1, 1120])]; + tensor var_5539_end_mask_0 = const()[name = tensor("op_5539_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5539_cast = slice_by_index(begin = var_5539_begin_0, end = var_5539_end_0, end_mask = var_5539_end_mask_0, x = transpose_13)[name = tensor("op_5539_cast")]; + tensor var_5543_begin_0 = const()[name = tensor("op_5543_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_5543_end_0 = const()[name = tensor("op_5543_end_0"), val = tensor([2, 576, 1, 1280])]; + tensor var_5543_end_mask_0 = const()[name = tensor("op_5543_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5543_cast = slice_by_index(begin = var_5543_begin_0, end = var_5543_end_0, end_mask = var_5543_end_mask_0, x = transpose_13)[name = tensor("op_5543_cast")]; + tensor var_5545_begin_0 = const()[name = tensor("op_5545_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5545_end_0 = const()[name = tensor("op_5545_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_5545_end_mask_0 = const()[name = tensor("op_5545_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5545_cast = slice_by_index(begin = var_5545_begin_0, end = var_5545_end_0, end_mask = var_5545_end_mask_0, x = v_37_cast)[name = tensor("op_5545_cast")]; + tensor var_5549_begin_0 = const()[name = tensor("op_5549_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_5549_end_0 = const()[name = tensor("op_5549_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_5549_end_mask_0 = const()[name = tensor("op_5549_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5549_cast = slice_by_index(begin = var_5549_begin_0, end = var_5549_end_0, end_mask = var_5549_end_mask_0, x = v_37_cast)[name = tensor("op_5549_cast")]; + tensor var_5553_begin_0 = const()[name = tensor("op_5553_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_5553_end_0 = const()[name = tensor("op_5553_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_5553_end_mask_0 = const()[name = tensor("op_5553_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5553_cast = slice_by_index(begin = var_5553_begin_0, end = var_5553_end_0, end_mask = var_5553_end_mask_0, x = v_37_cast)[name = tensor("op_5553_cast")]; + tensor var_5557_begin_0 = const()[name = tensor("op_5557_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_5557_end_0 = const()[name = tensor("op_5557_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_5557_end_mask_0 = const()[name = tensor("op_5557_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5557_cast = slice_by_index(begin = var_5557_begin_0, end = var_5557_end_0, end_mask = var_5557_end_mask_0, x = v_37_cast)[name = tensor("op_5557_cast")]; + tensor var_5561_begin_0 = const()[name = tensor("op_5561_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_5561_end_0 = const()[name = tensor("op_5561_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_5561_end_mask_0 = const()[name = tensor("op_5561_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5561_cast = slice_by_index(begin = var_5561_begin_0, end = var_5561_end_0, end_mask = var_5561_end_mask_0, x = v_37_cast)[name = tensor("op_5561_cast")]; + tensor var_5565_begin_0 = const()[name = tensor("op_5565_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_5565_end_0 = const()[name = tensor("op_5565_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_5565_end_mask_0 = const()[name = tensor("op_5565_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5565_cast = slice_by_index(begin = var_5565_begin_0, end = var_5565_end_0, end_mask = var_5565_end_mask_0, x = v_37_cast)[name = tensor("op_5565_cast")]; + tensor var_5569_begin_0 = const()[name = tensor("op_5569_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_5569_end_0 = const()[name = tensor("op_5569_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_5569_end_mask_0 = const()[name = tensor("op_5569_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5569_cast = slice_by_index(begin = var_5569_begin_0, end = var_5569_end_0, end_mask = var_5569_end_mask_0, x = v_37_cast)[name = tensor("op_5569_cast")]; + tensor var_5573_begin_0 = const()[name = tensor("op_5573_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_5573_end_0 = const()[name = tensor("op_5573_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_5573_end_mask_0 = const()[name = tensor("op_5573_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5573_cast = slice_by_index(begin = var_5573_begin_0, end = var_5573_end_0, end_mask = var_5573_end_mask_0, x = v_37_cast)[name = tensor("op_5573_cast")]; + tensor var_5577_equation_0 = const()[name = tensor("op_5577_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5577_cast = einsum(equation = var_5577_equation_0, values = (var_5515_cast, var_5480_cast))[name = tensor("op_5577_cast")]; + tensor var_5578_to_fp16 = const()[name = tensor("op_5578_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_289_cast = mul(x = var_5577_cast, y = var_5578_to_fp16)[name = tensor("aw_289_cast")]; + tensor var_5581_equation_0 = const()[name = tensor("op_5581_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5581_cast = einsum(equation = var_5581_equation_0, values = (var_5519_cast, var_5484_cast))[name = tensor("op_5581_cast")]; + tensor var_5582_to_fp16 = const()[name = tensor("op_5582_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_291_cast = mul(x = var_5581_cast, y = var_5582_to_fp16)[name = tensor("aw_291_cast")]; + tensor var_5585_equation_0 = const()[name = tensor("op_5585_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5585_cast = einsum(equation = var_5585_equation_0, values = (var_5523_cast, var_5488_cast))[name = tensor("op_5585_cast")]; + tensor var_5586_to_fp16 = const()[name = tensor("op_5586_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_293_cast = mul(x = var_5585_cast, y = var_5586_to_fp16)[name = tensor("aw_293_cast")]; + tensor var_5589_equation_0 = const()[name = tensor("op_5589_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5589_cast = einsum(equation = var_5589_equation_0, values = (var_5527_cast, var_5492_cast))[name = tensor("op_5589_cast")]; + tensor var_5590_to_fp16 = const()[name = tensor("op_5590_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_295_cast = mul(x = var_5589_cast, y = var_5590_to_fp16)[name = tensor("aw_295_cast")]; + tensor var_5593_equation_0 = const()[name = tensor("op_5593_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5593_cast = einsum(equation = var_5593_equation_0, values = (var_5531_cast, var_5496_cast))[name = tensor("op_5593_cast")]; + tensor var_5594_to_fp16 = const()[name = tensor("op_5594_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_297_cast = mul(x = var_5593_cast, y = var_5594_to_fp16)[name = tensor("aw_297_cast")]; + tensor var_5597_equation_0 = const()[name = tensor("op_5597_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5597_cast = einsum(equation = var_5597_equation_0, values = (var_5535_cast, var_5500_cast))[name = tensor("op_5597_cast")]; + tensor var_5598_to_fp16 = const()[name = tensor("op_5598_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_299_cast = mul(x = var_5597_cast, y = var_5598_to_fp16)[name = tensor("aw_299_cast")]; + tensor var_5601_equation_0 = const()[name = tensor("op_5601_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5601_cast = einsum(equation = var_5601_equation_0, values = (var_5539_cast, var_5504_cast))[name = tensor("op_5601_cast")]; + tensor var_5602_to_fp16 = const()[name = tensor("op_5602_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_301_cast = mul(x = var_5601_cast, y = var_5602_to_fp16)[name = tensor("aw_301_cast")]; + tensor var_5605_equation_0 = const()[name = tensor("op_5605_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5605_cast = einsum(equation = var_5605_equation_0, values = (var_5543_cast, var_5508_cast))[name = tensor("op_5605_cast")]; + tensor var_5606_to_fp16 = const()[name = tensor("op_5606_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_303_cast = mul(x = var_5605_cast, y = var_5606_to_fp16)[name = tensor("aw_303_cast")]; + tensor var_5608_cast = softmax(axis = var_4272, x = aw_289_cast)[name = tensor("op_5608_cast")]; + tensor var_5609_cast = softmax(axis = var_4272, x = aw_291_cast)[name = tensor("op_5609_cast")]; + tensor var_5610_cast = softmax(axis = var_4272, x = aw_293_cast)[name = tensor("op_5610_cast")]; + tensor var_5611_cast = softmax(axis = var_4272, x = aw_295_cast)[name = tensor("op_5611_cast")]; + tensor var_5612_cast = softmax(axis = var_4272, x = aw_297_cast)[name = tensor("op_5612_cast")]; + tensor var_5613_cast = softmax(axis = var_4272, x = aw_299_cast)[name = tensor("op_5613_cast")]; + tensor var_5614_cast = softmax(axis = var_4272, x = aw_301_cast)[name = tensor("op_5614_cast")]; + tensor var_5615_cast = softmax(axis = var_4272, x = aw_303_cast)[name = tensor("op_5615_cast")]; + tensor var_5617_equation_0 = const()[name = tensor("op_5617_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5617_cast = einsum(equation = var_5617_equation_0, values = (var_5545_cast, var_5608_cast))[name = tensor("op_5617_cast")]; + tensor var_5619_equation_0 = const()[name = tensor("op_5619_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5619_cast = einsum(equation = var_5619_equation_0, values = (var_5549_cast, var_5609_cast))[name = tensor("op_5619_cast")]; + tensor var_5621_equation_0 = const()[name = tensor("op_5621_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5621_cast = einsum(equation = var_5621_equation_0, values = (var_5553_cast, var_5610_cast))[name = tensor("op_5621_cast")]; + tensor var_5623_equation_0 = const()[name = tensor("op_5623_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5623_cast = einsum(equation = var_5623_equation_0, values = (var_5557_cast, var_5611_cast))[name = tensor("op_5623_cast")]; + tensor var_5625_equation_0 = const()[name = tensor("op_5625_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5625_cast = einsum(equation = var_5625_equation_0, values = (var_5561_cast, var_5612_cast))[name = tensor("op_5625_cast")]; + tensor var_5627_equation_0 = const()[name = tensor("op_5627_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5627_cast = einsum(equation = var_5627_equation_0, values = (var_5565_cast, var_5613_cast))[name = tensor("op_5627_cast")]; + tensor var_5629_equation_0 = const()[name = tensor("op_5629_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5629_cast = einsum(equation = var_5629_equation_0, values = (var_5569_cast, var_5614_cast))[name = tensor("op_5629_cast")]; + tensor var_5631_equation_0 = const()[name = tensor("op_5631_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5631_cast = einsum(equation = var_5631_equation_0, values = (var_5573_cast, var_5615_cast))[name = tensor("op_5631_cast")]; + tensor input_353_interleave_0 = const()[name = tensor("input_353_interleave_0"), val = tensor(false)]; + tensor input_353_cast = concat(axis = var_4272, interleave = input_353_interleave_0, values = (var_5617_cast, var_5619_cast, var_5621_cast, var_5623_cast, var_5625_cast, var_5627_cast, var_5629_cast, var_5631_cast))[name = tensor("input_353_cast")]; + tensor var_5637 = const()[name = tensor("op_5637"), val = tensor([1, 1])]; + tensor var_5639 = const()[name = tensor("op_5639"), val = tensor([1, 1])]; + tensor var_5641_pad_type_0 = const()[name = tensor("op_5641_pad_type_0"), val = tensor("custom")]; + tensor var_5641_pad_0 = const()[name = tensor("op_5641_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545137472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546366336))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546366528)))]; + tensor var_5641_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5639, groups = var_4272, pad = var_5641_pad_0, pad_type = var_5641_pad_type_0, strides = var_5637, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_353_cast)[name = tensor("op_5641_cast")]; + tensor inputs_57_cast = add(x = var_5641_cast, y = inputs_55_cast)[name = tensor("inputs_57_cast")]; + tensor var_5645 = const()[name = tensor("op_5645"), val = tensor([1])]; + tensor channels_mean_57_cast = reduce_mean(axes = var_5645, keep_dims = var_4267, x = inputs_57_cast)[name = tensor("channels_mean_57_cast")]; + tensor zero_mean_57_cast = sub(x = inputs_57_cast, y = channels_mean_57_cast)[name = tensor("zero_mean_57_cast")]; + tensor zero_mean_sq_57_cast = mul(x = zero_mean_57_cast, y = zero_mean_57_cast)[name = tensor("zero_mean_sq_57_cast")]; + tensor var_5649 = const()[name = tensor("op_5649"), val = tensor([1])]; + tensor var_5650_cast = reduce_mean(axes = var_5649, keep_dims = var_4267, x = zero_mean_sq_57_cast)[name = tensor("op_5650_cast")]; + tensor var_5651_to_fp16 = const()[name = tensor("op_5651_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5652_cast = add(x = var_5650_cast, y = var_5651_to_fp16)[name = tensor("op_5652_cast")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_5652_cast)[name = tensor("denom_57_cast")]; + tensor out_57_cast = mul(x = zero_mean_57_cast, y = denom_57_cast)[name = tensor("out_57_cast")]; + tensor var_5656_to_fp16 = const()[name = tensor("op_5656_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546369152)))]; + tensor var_5657_cast = add(x = out_57_cast, y = var_5656_to_fp16)[name = tensor("op_5657_cast")]; + tensor var_5659_to_fp16 = const()[name = tensor("op_5659_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546371776)))]; + tensor hidden_states_209_cast = mul(x = var_5657_cast, y = var_5659_to_fp16)[name = tensor("hidden_states_209_cast")]; + tensor var_5666 = const()[name = tensor("op_5666"), val = tensor([1, 1])]; + tensor var_5668 = const()[name = tensor("op_5668"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546374400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547603264))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_39_cast = conv(dilations = var_5668, groups = var_4272, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_5666, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_209_cast)[name = tensor("q_39_cast")]; + tensor var_5672 = const()[name = tensor("op_5672"), val = tensor([1, 1])]; + tensor var_5674 = const()[name = tensor("op_5674"), val = tensor([1, 1])]; + tensor k_77_pad_type_0 = const()[name = tensor("k_77_pad_type_0"), val = tensor("custom")]; + tensor k_77_pad_0 = const()[name = tensor("k_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547603456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548340800))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_77_cast = conv(dilations = var_5674, groups = var_4272, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_5672, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_77_cast")]; + tensor var_5678 = const()[name = tensor("op_5678"), val = tensor([1, 1])]; + tensor var_5680 = const()[name = tensor("op_5680"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548340992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549078336))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_39_cast = conv(dilations = var_5680, groups = var_4272, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_5678, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_39_cast")]; + tensor var_5684_begin_0 = const()[name = tensor("op_5684_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5684_end_0 = const()[name = tensor("op_5684_end_0"), val = tensor([2, 160, 1, 576])]; + tensor var_5684_end_mask_0 = const()[name = tensor("op_5684_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5684_cast = slice_by_index(begin = var_5684_begin_0, end = var_5684_end_0, end_mask = var_5684_end_mask_0, x = q_39_cast)[name = tensor("op_5684_cast")]; + tensor var_5688_begin_0 = const()[name = tensor("op_5688_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_5688_end_0 = const()[name = tensor("op_5688_end_0"), val = tensor([2, 320, 1, 576])]; + tensor var_5688_end_mask_0 = const()[name = tensor("op_5688_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5688_cast = slice_by_index(begin = var_5688_begin_0, end = var_5688_end_0, end_mask = var_5688_end_mask_0, x = q_39_cast)[name = tensor("op_5688_cast")]; + tensor var_5692_begin_0 = const()[name = tensor("op_5692_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_5692_end_0 = const()[name = tensor("op_5692_end_0"), val = tensor([2, 480, 1, 576])]; + tensor var_5692_end_mask_0 = const()[name = tensor("op_5692_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5692_cast = slice_by_index(begin = var_5692_begin_0, end = var_5692_end_0, end_mask = var_5692_end_mask_0, x = q_39_cast)[name = tensor("op_5692_cast")]; + tensor var_5696_begin_0 = const()[name = tensor("op_5696_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_5696_end_0 = const()[name = tensor("op_5696_end_0"), val = tensor([2, 640, 1, 576])]; + tensor var_5696_end_mask_0 = const()[name = tensor("op_5696_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5696_cast = slice_by_index(begin = var_5696_begin_0, end = var_5696_end_0, end_mask = var_5696_end_mask_0, x = q_39_cast)[name = tensor("op_5696_cast")]; + tensor var_5700_begin_0 = const()[name = tensor("op_5700_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_5700_end_0 = const()[name = tensor("op_5700_end_0"), val = tensor([2, 800, 1, 576])]; + tensor var_5700_end_mask_0 = const()[name = tensor("op_5700_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5700_cast = slice_by_index(begin = var_5700_begin_0, end = var_5700_end_0, end_mask = var_5700_end_mask_0, x = q_39_cast)[name = tensor("op_5700_cast")]; + tensor var_5704_begin_0 = const()[name = tensor("op_5704_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_5704_end_0 = const()[name = tensor("op_5704_end_0"), val = tensor([2, 960, 1, 576])]; + tensor var_5704_end_mask_0 = const()[name = tensor("op_5704_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5704_cast = slice_by_index(begin = var_5704_begin_0, end = var_5704_end_0, end_mask = var_5704_end_mask_0, x = q_39_cast)[name = tensor("op_5704_cast")]; + tensor var_5708_begin_0 = const()[name = tensor("op_5708_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_5708_end_0 = const()[name = tensor("op_5708_end_0"), val = tensor([2, 1120, 1, 576])]; + tensor var_5708_end_mask_0 = const()[name = tensor("op_5708_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5708_cast = slice_by_index(begin = var_5708_begin_0, end = var_5708_end_0, end_mask = var_5708_end_mask_0, x = q_39_cast)[name = tensor("op_5708_cast")]; + tensor var_5712_begin_0 = const()[name = tensor("op_5712_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_5712_end_0 = const()[name = tensor("op_5712_end_0"), val = tensor([2, 1280, 1, 576])]; + tensor var_5712_end_mask_0 = const()[name = tensor("op_5712_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5712_cast = slice_by_index(begin = var_5712_begin_0, end = var_5712_end_0, end_mask = var_5712_end_mask_0, x = q_39_cast)[name = tensor("op_5712_cast")]; + tensor k_79_perm_0 = const()[name = tensor("k_79_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_5719_begin_0 = const()[name = tensor("op_5719_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5719_end_0 = const()[name = tensor("op_5719_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_5719_end_mask_0 = const()[name = tensor("op_5719_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_12 = transpose(perm = k_79_perm_0, x = k_77_cast)[name = tensor("transpose_12")]; + tensor var_5719_cast = slice_by_index(begin = var_5719_begin_0, end = var_5719_end_0, end_mask = var_5719_end_mask_0, x = transpose_12)[name = tensor("op_5719_cast")]; + tensor var_5723_begin_0 = const()[name = tensor("op_5723_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_5723_end_0 = const()[name = tensor("op_5723_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_5723_end_mask_0 = const()[name = tensor("op_5723_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5723_cast = slice_by_index(begin = var_5723_begin_0, end = var_5723_end_0, end_mask = var_5723_end_mask_0, x = transpose_12)[name = tensor("op_5723_cast")]; + tensor var_5727_begin_0 = const()[name = tensor("op_5727_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_5727_end_0 = const()[name = tensor("op_5727_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_5727_end_mask_0 = const()[name = tensor("op_5727_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5727_cast = slice_by_index(begin = var_5727_begin_0, end = var_5727_end_0, end_mask = var_5727_end_mask_0, x = transpose_12)[name = tensor("op_5727_cast")]; + tensor var_5731_begin_0 = const()[name = tensor("op_5731_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_5731_end_0 = const()[name = tensor("op_5731_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_5731_end_mask_0 = const()[name = tensor("op_5731_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5731_cast = slice_by_index(begin = var_5731_begin_0, end = var_5731_end_0, end_mask = var_5731_end_mask_0, x = transpose_12)[name = tensor("op_5731_cast")]; + tensor var_5735_begin_0 = const()[name = tensor("op_5735_begin_0"), val = tensor([0, 0, 0, 640])]; + tensor var_5735_end_0 = const()[name = tensor("op_5735_end_0"), val = tensor([2, 77, 1, 800])]; + tensor var_5735_end_mask_0 = const()[name = tensor("op_5735_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5735_cast = slice_by_index(begin = var_5735_begin_0, end = var_5735_end_0, end_mask = var_5735_end_mask_0, x = transpose_12)[name = tensor("op_5735_cast")]; + tensor var_5739_begin_0 = const()[name = tensor("op_5739_begin_0"), val = tensor([0, 0, 0, 800])]; + tensor var_5739_end_0 = const()[name = tensor("op_5739_end_0"), val = tensor([2, 77, 1, 960])]; + tensor var_5739_end_mask_0 = const()[name = tensor("op_5739_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5739_cast = slice_by_index(begin = var_5739_begin_0, end = var_5739_end_0, end_mask = var_5739_end_mask_0, x = transpose_12)[name = tensor("op_5739_cast")]; + tensor var_5743_begin_0 = const()[name = tensor("op_5743_begin_0"), val = tensor([0, 0, 0, 960])]; + tensor var_5743_end_0 = const()[name = tensor("op_5743_end_0"), val = tensor([2, 77, 1, 1120])]; + tensor var_5743_end_mask_0 = const()[name = tensor("op_5743_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5743_cast = slice_by_index(begin = var_5743_begin_0, end = var_5743_end_0, end_mask = var_5743_end_mask_0, x = transpose_12)[name = tensor("op_5743_cast")]; + tensor var_5747_begin_0 = const()[name = tensor("op_5747_begin_0"), val = tensor([0, 0, 0, 1120])]; + tensor var_5747_end_0 = const()[name = tensor("op_5747_end_0"), val = tensor([2, 77, 1, 1280])]; + tensor var_5747_end_mask_0 = const()[name = tensor("op_5747_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_5747_cast = slice_by_index(begin = var_5747_begin_0, end = var_5747_end_0, end_mask = var_5747_end_mask_0, x = transpose_12)[name = tensor("op_5747_cast")]; + tensor var_5749_begin_0 = const()[name = tensor("op_5749_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_5749_end_0 = const()[name = tensor("op_5749_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_5749_end_mask_0 = const()[name = tensor("op_5749_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5749_cast = slice_by_index(begin = var_5749_begin_0, end = var_5749_end_0, end_mask = var_5749_end_mask_0, x = v_39_cast)[name = tensor("op_5749_cast")]; + tensor var_5753_begin_0 = const()[name = tensor("op_5753_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_5753_end_0 = const()[name = tensor("op_5753_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_5753_end_mask_0 = const()[name = tensor("op_5753_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5753_cast = slice_by_index(begin = var_5753_begin_0, end = var_5753_end_0, end_mask = var_5753_end_mask_0, x = v_39_cast)[name = tensor("op_5753_cast")]; + tensor var_5757_begin_0 = const()[name = tensor("op_5757_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_5757_end_0 = const()[name = tensor("op_5757_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_5757_end_mask_0 = const()[name = tensor("op_5757_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5757_cast = slice_by_index(begin = var_5757_begin_0, end = var_5757_end_0, end_mask = var_5757_end_mask_0, x = v_39_cast)[name = tensor("op_5757_cast")]; + tensor var_5761_begin_0 = const()[name = tensor("op_5761_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_5761_end_0 = const()[name = tensor("op_5761_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_5761_end_mask_0 = const()[name = tensor("op_5761_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5761_cast = slice_by_index(begin = var_5761_begin_0, end = var_5761_end_0, end_mask = var_5761_end_mask_0, x = v_39_cast)[name = tensor("op_5761_cast")]; + tensor var_5765_begin_0 = const()[name = tensor("op_5765_begin_0"), val = tensor([0, 640, 0, 0])]; + tensor var_5765_end_0 = const()[name = tensor("op_5765_end_0"), val = tensor([2, 800, 1, 77])]; + tensor var_5765_end_mask_0 = const()[name = tensor("op_5765_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5765_cast = slice_by_index(begin = var_5765_begin_0, end = var_5765_end_0, end_mask = var_5765_end_mask_0, x = v_39_cast)[name = tensor("op_5765_cast")]; + tensor var_5769_begin_0 = const()[name = tensor("op_5769_begin_0"), val = tensor([0, 800, 0, 0])]; + tensor var_5769_end_0 = const()[name = tensor("op_5769_end_0"), val = tensor([2, 960, 1, 77])]; + tensor var_5769_end_mask_0 = const()[name = tensor("op_5769_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5769_cast = slice_by_index(begin = var_5769_begin_0, end = var_5769_end_0, end_mask = var_5769_end_mask_0, x = v_39_cast)[name = tensor("op_5769_cast")]; + tensor var_5773_begin_0 = const()[name = tensor("op_5773_begin_0"), val = tensor([0, 960, 0, 0])]; + tensor var_5773_end_0 = const()[name = tensor("op_5773_end_0"), val = tensor([2, 1120, 1, 77])]; + tensor var_5773_end_mask_0 = const()[name = tensor("op_5773_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5773_cast = slice_by_index(begin = var_5773_begin_0, end = var_5773_end_0, end_mask = var_5773_end_mask_0, x = v_39_cast)[name = tensor("op_5773_cast")]; + tensor var_5777_begin_0 = const()[name = tensor("op_5777_begin_0"), val = tensor([0, 1120, 0, 0])]; + tensor var_5777_end_0 = const()[name = tensor("op_5777_end_0"), val = tensor([2, 1280, 1, 77])]; + tensor var_5777_end_mask_0 = const()[name = tensor("op_5777_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_5777_cast = slice_by_index(begin = var_5777_begin_0, end = var_5777_end_0, end_mask = var_5777_end_mask_0, x = v_39_cast)[name = tensor("op_5777_cast")]; + tensor var_5781_equation_0 = const()[name = tensor("op_5781_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5781_cast = einsum(equation = var_5781_equation_0, values = (var_5719_cast, var_5684_cast))[name = tensor("op_5781_cast")]; + tensor var_5782_to_fp16 = const()[name = tensor("op_5782_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_305_cast = mul(x = var_5781_cast, y = var_5782_to_fp16)[name = tensor("aw_305_cast")]; + tensor var_5785_equation_0 = const()[name = tensor("op_5785_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5785_cast = einsum(equation = var_5785_equation_0, values = (var_5723_cast, var_5688_cast))[name = tensor("op_5785_cast")]; + tensor var_5786_to_fp16 = const()[name = tensor("op_5786_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_307_cast = mul(x = var_5785_cast, y = var_5786_to_fp16)[name = tensor("aw_307_cast")]; + tensor var_5789_equation_0 = const()[name = tensor("op_5789_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5789_cast = einsum(equation = var_5789_equation_0, values = (var_5727_cast, var_5692_cast))[name = tensor("op_5789_cast")]; + tensor var_5790_to_fp16 = const()[name = tensor("op_5790_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_309_cast = mul(x = var_5789_cast, y = var_5790_to_fp16)[name = tensor("aw_309_cast")]; + tensor var_5793_equation_0 = const()[name = tensor("op_5793_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5793_cast = einsum(equation = var_5793_equation_0, values = (var_5731_cast, var_5696_cast))[name = tensor("op_5793_cast")]; + tensor var_5794_to_fp16 = const()[name = tensor("op_5794_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_311_cast = mul(x = var_5793_cast, y = var_5794_to_fp16)[name = tensor("aw_311_cast")]; + tensor var_5797_equation_0 = const()[name = tensor("op_5797_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5797_cast = einsum(equation = var_5797_equation_0, values = (var_5735_cast, var_5700_cast))[name = tensor("op_5797_cast")]; + tensor var_5798_to_fp16 = const()[name = tensor("op_5798_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_313_cast = mul(x = var_5797_cast, y = var_5798_to_fp16)[name = tensor("aw_313_cast")]; + tensor var_5801_equation_0 = const()[name = tensor("op_5801_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5801_cast = einsum(equation = var_5801_equation_0, values = (var_5739_cast, var_5704_cast))[name = tensor("op_5801_cast")]; + tensor var_5802_to_fp16 = const()[name = tensor("op_5802_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_315_cast = mul(x = var_5801_cast, y = var_5802_to_fp16)[name = tensor("aw_315_cast")]; + tensor var_5805_equation_0 = const()[name = tensor("op_5805_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5805_cast = einsum(equation = var_5805_equation_0, values = (var_5743_cast, var_5708_cast))[name = tensor("op_5805_cast")]; + tensor var_5806_to_fp16 = const()[name = tensor("op_5806_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_317_cast = mul(x = var_5805_cast, y = var_5806_to_fp16)[name = tensor("aw_317_cast")]; + tensor var_5809_equation_0 = const()[name = tensor("op_5809_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_5809_cast = einsum(equation = var_5809_equation_0, values = (var_5747_cast, var_5712_cast))[name = tensor("op_5809_cast")]; + tensor var_5810_to_fp16 = const()[name = tensor("op_5810_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor aw_319_cast = mul(x = var_5809_cast, y = var_5810_to_fp16)[name = tensor("aw_319_cast")]; + tensor var_5812_cast = softmax(axis = var_4272, x = aw_305_cast)[name = tensor("op_5812_cast")]; + tensor var_5813_cast = softmax(axis = var_4272, x = aw_307_cast)[name = tensor("op_5813_cast")]; + tensor var_5814_cast = softmax(axis = var_4272, x = aw_309_cast)[name = tensor("op_5814_cast")]; + tensor var_5815_cast = softmax(axis = var_4272, x = aw_311_cast)[name = tensor("op_5815_cast")]; + tensor var_5816_cast = softmax(axis = var_4272, x = aw_313_cast)[name = tensor("op_5816_cast")]; + tensor var_5817_cast = softmax(axis = var_4272, x = aw_315_cast)[name = tensor("op_5817_cast")]; + tensor var_5818_cast = softmax(axis = var_4272, x = aw_317_cast)[name = tensor("op_5818_cast")]; + tensor var_5819_cast = softmax(axis = var_4272, x = aw_319_cast)[name = tensor("op_5819_cast")]; + tensor var_5821_equation_0 = const()[name = tensor("op_5821_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5821_cast = einsum(equation = var_5821_equation_0, values = (var_5749_cast, var_5812_cast))[name = tensor("op_5821_cast")]; + tensor var_5823_equation_0 = const()[name = tensor("op_5823_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5823_cast = einsum(equation = var_5823_equation_0, values = (var_5753_cast, var_5813_cast))[name = tensor("op_5823_cast")]; + tensor var_5825_equation_0 = const()[name = tensor("op_5825_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5825_cast = einsum(equation = var_5825_equation_0, values = (var_5757_cast, var_5814_cast))[name = tensor("op_5825_cast")]; + tensor var_5827_equation_0 = const()[name = tensor("op_5827_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5827_cast = einsum(equation = var_5827_equation_0, values = (var_5761_cast, var_5815_cast))[name = tensor("op_5827_cast")]; + tensor var_5829_equation_0 = const()[name = tensor("op_5829_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5829_cast = einsum(equation = var_5829_equation_0, values = (var_5765_cast, var_5816_cast))[name = tensor("op_5829_cast")]; + tensor var_5831_equation_0 = const()[name = tensor("op_5831_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5831_cast = einsum(equation = var_5831_equation_0, values = (var_5769_cast, var_5817_cast))[name = tensor("op_5831_cast")]; + tensor var_5833_equation_0 = const()[name = tensor("op_5833_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5833_cast = einsum(equation = var_5833_equation_0, values = (var_5773_cast, var_5818_cast))[name = tensor("op_5833_cast")]; + tensor var_5835_equation_0 = const()[name = tensor("op_5835_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_5835_cast = einsum(equation = var_5835_equation_0, values = (var_5777_cast, var_5819_cast))[name = tensor("op_5835_cast")]; + tensor input_355_interleave_0 = const()[name = tensor("input_355_interleave_0"), val = tensor(false)]; + tensor input_355_cast = concat(axis = var_4272, interleave = input_355_interleave_0, values = (var_5821_cast, var_5823_cast, var_5825_cast, var_5827_cast, var_5829_cast, var_5831_cast, var_5833_cast, var_5835_cast))[name = tensor("input_355_cast")]; + tensor var_5841 = const()[name = tensor("op_5841"), val = tensor([1, 1])]; + tensor var_5843 = const()[name = tensor("op_5843"), val = tensor([1, 1])]; + tensor var_5845_pad_type_0 = const()[name = tensor("op_5845_pad_type_0"), val = tensor("custom")]; + tensor var_5845_pad_0 = const()[name = tensor("op_5845_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549078528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550307392))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550307584)))]; + tensor var_5845_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5843, groups = var_4272, pad = var_5845_pad_0, pad_type = var_5845_pad_type_0, strides = var_5841, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_355_cast)[name = tensor("op_5845_cast")]; + tensor inputs_59_cast = add(x = var_5845_cast, y = inputs_57_cast)[name = tensor("inputs_59_cast")]; + tensor var_5849 = const()[name = tensor("op_5849"), val = tensor([1])]; + tensor channels_mean_59_cast = reduce_mean(axes = var_5849, keep_dims = var_4267, x = inputs_59_cast)[name = tensor("channels_mean_59_cast")]; + tensor zero_mean_59_cast = sub(x = inputs_59_cast, y = channels_mean_59_cast)[name = tensor("zero_mean_59_cast")]; + tensor zero_mean_sq_59_cast = mul(x = zero_mean_59_cast, y = zero_mean_59_cast)[name = tensor("zero_mean_sq_59_cast")]; + tensor var_5853 = const()[name = tensor("op_5853"), val = tensor([1])]; + tensor var_5854_cast = reduce_mean(axes = var_5853, keep_dims = var_4267, x = zero_mean_sq_59_cast)[name = tensor("op_5854_cast")]; + tensor var_5855_to_fp16 = const()[name = tensor("op_5855_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5856_cast = add(x = var_5854_cast, y = var_5855_to_fp16)[name = tensor("op_5856_cast")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_5856_cast)[name = tensor("denom_59_cast")]; + tensor out_59_cast = mul(x = zero_mean_59_cast, y = denom_59_cast)[name = tensor("out_59_cast")]; + tensor var_5860_to_fp16 = const()[name = tensor("op_5860_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550310208)))]; + tensor var_5861_cast = add(x = out_59_cast, y = var_5860_to_fp16)[name = tensor("op_5861_cast")]; + tensor var_5863_to_fp16 = const()[name = tensor("op_5863_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550312832)))]; + tensor input_357_cast = mul(x = var_5861_cast, y = var_5863_to_fp16)[name = tensor("input_357_cast")]; + tensor var_5871 = const()[name = tensor("op_5871"), val = tensor([1, 1])]; + tensor var_5873 = const()[name = tensor("op_5873"), val = tensor([1, 1])]; + tensor var_5875_pad_type_0 = const()[name = tensor("op_5875_pad_type_0"), val = tensor("custom")]; + tensor var_5875_pad_0 = const()[name = tensor("op_5875_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550315456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560145920))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560146112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560153856))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5875_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5873, groups = var_4272, pad = var_5875_pad_0, pad_type = var_5875_pad_type_0, strides = var_5871, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_357_cast)[name = tensor("op_5875_cast")]; + tensor var_5876_split_sizes_0 = const()[name = tensor("op_5876_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5876_axis_0 = const()[name = tensor("op_5876_axis_0"), val = tensor(1)]; + tensor var_5876_cast_0, tensor var_5876_cast_1 = split(axis = var_5876_axis_0, split_sizes = var_5876_split_sizes_0, x = var_5875_cast)[name = tensor("op_5876_cast")]; + tensor var_5878_mode_0 = const()[name = tensor("op_5878_mode_0"), val = tensor("EXACT")]; + tensor var_5878_cast = gelu(mode = var_5878_mode_0, x = var_5876_cast_1)[name = tensor("op_5878_cast")]; + tensor input_359_cast = mul(x = var_5876_cast_0, y = var_5878_cast)[name = tensor("input_359_cast")]; + tensor var_5882 = const()[name = tensor("op_5882"), val = tensor([1, 1])]; + tensor var_5884 = const()[name = tensor("op_5884"), val = tensor([1, 1])]; + tensor var_5886_pad_type_0 = const()[name = tensor("op_5886_pad_type_0"), val = tensor("custom")]; + tensor var_5886_pad_0 = const()[name = tensor("op_5886_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560154048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565069312))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565069504)))]; + tensor var_5886_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5884, groups = var_4272, pad = var_5886_pad_0, pad_type = var_5886_pad_type_0, strides = var_5882, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_359_cast)[name = tensor("op_5886_cast")]; + tensor hidden_states_213_cast = add(x = var_5886_cast, y = inputs_59_cast)[name = tensor("hidden_states_213_cast")]; + tensor var_5888 = const()[name = tensor("op_5888"), val = tensor([2, 1280, 24, 24])]; + tensor input_361_cast = reshape(shape = var_5888, x = hidden_states_213_cast)[name = tensor("input_361_cast")]; + tensor var_5892 = const()[name = tensor("op_5892"), val = tensor([1, 1])]; + tensor var_5894 = const()[name = tensor("op_5894"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_type_0 = const()[name = tensor("hidden_states_215_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_215_pad_0 = const()[name = tensor("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565072128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566300992))), name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566301184)))]; + tensor hidden_states_215_cast = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_5894, groups = var_4272, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_5892, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized, x = input_361_cast)[name = tensor("hidden_states_215_cast")]; + tensor input_363_cast = add(x = hidden_states_215_cast, y = hidden_states_203_cast)[name = tensor("input_363_cast")]; + tensor input_365_scale_factor_height_0 = const()[name = tensor("input_365_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_365_scale_factor_width_0 = const()[name = tensor("input_365_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor 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("input_365_cast")]; + tensor var_5903 = const()[name = tensor("op_5903"), val = tensor([1, 1])]; + tensor var_5905 = const()[name = tensor("op_5905"), val = tensor([1, 1])]; + tensor hidden_states_217_pad_type_0 = const()[name = tensor("hidden_states_217_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_217_pad_0 = const()[name = tensor("hidden_states_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566303808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577363072))), name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577363264)))]; + tensor hidden_states_217_cast = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_5905, groups = var_4272, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_5903, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized, x = input_365_cast)[name = tensor("hidden_states_217_cast")]; + tensor var_5925 = const()[name = tensor("op_5925"), val = tensor(true)]; + tensor var_5930 = const()[name = tensor("op_5930"), val = tensor(1)]; + tensor input_367_interleave_0 = const()[name = tensor("input_367_interleave_0"), val = tensor(false)]; + tensor input_367_cast = concat(axis = var_5930, interleave = input_367_interleave_0, values = (hidden_states_217_cast, input_115_cast))[name = tensor("input_367_cast")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 60, 48, 48])]; + tensor reshape_168_cast = reshape(shape = reshape_168_shape_0, x = input_367_cast)[name = tensor("reshape_168_cast")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_126_cast")]; + tensor sub_84_cast = sub(x = reshape_168_cast, y = reduce_mean_126_cast)[name = tensor("sub_84_cast")]; + tensor square_42_cast = square(x = sub_84_cast)[name = tensor("square_42_cast")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_128_cast")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast = add(x = reduce_mean_128_cast, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast")]; + tensor sqrt_42_cast = sqrt(x = add_84_cast)[name = tensor("sqrt_42_cast")]; + tensor real_div_42_cast = real_div(x = sub_84_cast, y = sqrt_42_cast)[name = tensor("real_div_42_cast")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 1920, 48, 48])]; + tensor reshape_169_cast = reshape(shape = reshape_169_shape_0, x = real_div_42_cast)[name = tensor("reshape_169_cast")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577365888)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577369792)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_85_cast")]; + tensor input_371_cast = silu(x = add_85_cast)[name = tensor("input_371_cast")]; + tensor var_5959 = const()[name = tensor("op_5959"), val = tensor([1, 1])]; + tensor var_5961 = const()[name = tensor("op_5961"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_type_0 = const()[name = tensor("hidden_states_219_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_219_pad_0 = const()[name = tensor("hidden_states_219_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577373696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585668160))), name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1920, 3, 3])]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585668352)))]; + tensor hidden_states_219_cast = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_5961, groups = var_5930, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_5959, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_371_cast)[name = tensor("hidden_states_219_cast")]; + tensor var_5967 = const()[name = tensor("op_5967"), val = tensor([1, 1])]; + tensor var_5969 = const()[name = tensor("op_5969"), val = tensor([1, 1])]; + tensor temb_33_pad_type_0 = const()[name = tensor("temb_33_pad_type_0"), val = tensor("custom")]; + tensor temb_33_pad_0 = const()[name = tensor("temb_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585669696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586284160))), name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586284352)))]; + tensor temb_33_cast = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_5969, groups = var_5930, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_5967, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_33_cast")]; + tensor input_375_cast = add(x = hidden_states_219_cast, y = temb_33_cast)[name = tensor("input_375_cast")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_172_cast = reshape(shape = reshape_172_shape_0, x = input_375_cast)[name = tensor("reshape_172_cast")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_129_cast")]; + tensor sub_86_cast = sub(x = reshape_172_cast, y = reduce_mean_129_cast)[name = tensor("sub_86_cast")]; + tensor square_43_cast = square(x = sub_86_cast)[name = tensor("square_43_cast")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_131_cast")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast = add(x = reduce_mean_131_cast, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast")]; + tensor sqrt_43_cast = sqrt(x = add_86_cast)[name = tensor("sqrt_43_cast")]; + tensor real_div_43_cast = real_div(x = sub_86_cast, y = sqrt_43_cast)[name = tensor("real_div_43_cast")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_173_cast = reshape(shape = reshape_173_shape_0, x = real_div_43_cast)[name = tensor("reshape_173_cast")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586285696)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586287040)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_87_cast")]; + tensor input_379_cast = silu(x = add_87_cast)[name = tensor("input_379_cast")]; + tensor var_5979 = const()[name = tensor("op_5979"), val = tensor([1, 1])]; + tensor var_5981 = const()[name = tensor("op_5981"), val = tensor([1, 1])]; + tensor hidden_states_221_pad_type_0 = const()[name = tensor("hidden_states_221_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_221_pad_0 = const()[name = tensor("hidden_states_221_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586288384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589053248))), name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589053440)))]; + tensor hidden_states_221_cast = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_5981, groups = var_5930, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_5979, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_379_cast)[name = tensor("hidden_states_221_cast")]; + tensor var_5986 = const()[name = tensor("op_5986"), val = tensor([1, 1])]; + tensor var_5988 = const()[name = tensor("op_5988"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589054784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589976448))), name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1920, 1, 1])]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589976640)))]; + tensor x_17_cast = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_5988, groups = var_5930, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_5986, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_367_cast)[name = tensor("x_17_cast")]; + tensor hidden_states_223_cast = add(x = x_17_cast, y = hidden_states_221_cast)[name = tensor("hidden_states_223_cast")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_176_cast = reshape(shape = reshape_176_shape_0, x = hidden_states_223_cast)[name = tensor("reshape_176_cast")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_132_cast")]; + tensor sub_88_cast = sub(x = reshape_176_cast, y = reduce_mean_132_cast)[name = tensor("sub_88_cast")]; + tensor square_44_cast = square(x = sub_88_cast)[name = tensor("square_44_cast")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_134_cast")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_88_cast = add(x = reduce_mean_134_cast, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast")]; + tensor sqrt_44_cast = sqrt(x = add_88_cast)[name = tensor("sqrt_44_cast")]; + tensor real_div_44_cast = real_div(x = sub_88_cast, y = sqrt_44_cast)[name = tensor("real_div_44_cast")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_177_cast = reshape(shape = reshape_177_shape_0, x = real_div_44_cast)[name = tensor("reshape_177_cast")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589977984)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589979328)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_89_cast")]; + tensor var_6008 = const()[name = tensor("op_6008"), val = tensor([1, 1])]; + tensor var_6010 = const()[name = tensor("op_6010"), val = tensor([1, 1])]; + tensor hidden_states_225_pad_type_0 = const()[name = tensor("hidden_states_225_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_225_pad_0 = const()[name = tensor("hidden_states_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589980672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590287936))), name = tensor("up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590288128)))]; + tensor hidden_states_225_cast = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_6010, groups = var_5930, pad = hidden_states_225_pad_0, pad_type = hidden_states_225_pad_type_0, strides = var_6008, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_89_cast)[name = tensor("hidden_states_225_cast")]; + tensor var_6015 = const()[name = tensor("op_6015"), val = tensor([2, 640, 1, 2304])]; + tensor inputs_61_cast = reshape(shape = var_6015, x = hidden_states_225_cast)[name = tensor("inputs_61_cast")]; + tensor var_6025 = const()[name = tensor("op_6025"), val = tensor([1])]; + tensor channels_mean_61_cast = reduce_mean(axes = var_6025, keep_dims = var_5925, x = inputs_61_cast)[name = tensor("channels_mean_61_cast")]; + tensor zero_mean_61_cast = sub(x = inputs_61_cast, y = channels_mean_61_cast)[name = tensor("zero_mean_61_cast")]; + tensor zero_mean_sq_61_cast = mul(x = zero_mean_61_cast, y = zero_mean_61_cast)[name = tensor("zero_mean_sq_61_cast")]; + tensor var_6029 = const()[name = tensor("op_6029"), val = tensor([1])]; + tensor var_6030_cast = reduce_mean(axes = var_6029, keep_dims = var_5925, x = zero_mean_sq_61_cast)[name = tensor("op_6030_cast")]; + tensor var_6031_to_fp16 = const()[name = tensor("op_6031_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6032_cast = add(x = var_6030_cast, y = var_6031_to_fp16)[name = tensor("op_6032_cast")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_6032_cast)[name = tensor("denom_61_cast")]; + tensor out_61_cast = mul(x = zero_mean_61_cast, y = denom_61_cast)[name = tensor("out_61_cast")]; + tensor var_6036_to_fp16 = const()[name = tensor("op_6036_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590289472)))]; + tensor var_6037_cast = add(x = out_61_cast, y = var_6036_to_fp16)[name = tensor("op_6037_cast")]; + tensor var_6039_to_fp16 = const()[name = tensor("op_6039_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590290816)))]; + tensor hidden_states_227_cast = mul(x = var_6037_cast, y = var_6039_to_fp16)[name = tensor("hidden_states_227_cast")]; + tensor var_6046 = const()[name = tensor("op_6046"), val = tensor([1, 1])]; + tensor var_6048 = const()[name = tensor("op_6048"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590292160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590599424))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_41_cast = conv(dilations = var_6048, groups = var_5930, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_6046, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor("q_41_cast")]; + tensor var_6052 = const()[name = tensor("op_6052"), val = tensor([1, 1])]; + tensor var_6054 = const()[name = tensor("op_6054"), val = tensor([1, 1])]; + tensor k_81_pad_type_0 = const()[name = tensor("k_81_pad_type_0"), val = tensor("custom")]; + tensor k_81_pad_0 = const()[name = tensor("k_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590599616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590906880))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_81_cast = conv(dilations = var_6054, groups = var_5930, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_6052, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor("k_81_cast")]; + tensor var_6058 = const()[name = tensor("op_6058"), val = tensor([1, 1])]; + tensor var_6060 = const()[name = tensor("op_6060"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590907072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591214336))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_41_cast = conv(dilations = var_6060, groups = var_5930, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_6058, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor("v_41_cast")]; + tensor var_6064_begin_0 = const()[name = tensor("op_6064_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6064_end_0 = const()[name = tensor("op_6064_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_6064_end_mask_0 = const()[name = tensor("op_6064_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6064_cast = slice_by_index(begin = var_6064_begin_0, end = var_6064_end_0, end_mask = var_6064_end_mask_0, x = q_41_cast)[name = tensor("op_6064_cast")]; + tensor var_6068_begin_0 = const()[name = tensor("op_6068_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6068_end_0 = const()[name = tensor("op_6068_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_6068_end_mask_0 = const()[name = tensor("op_6068_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6068_cast = slice_by_index(begin = var_6068_begin_0, end = var_6068_end_0, end_mask = var_6068_end_mask_0, x = q_41_cast)[name = tensor("op_6068_cast")]; + tensor var_6072_begin_0 = const()[name = tensor("op_6072_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6072_end_0 = const()[name = tensor("op_6072_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_6072_end_mask_0 = const()[name = tensor("op_6072_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6072_cast = slice_by_index(begin = var_6072_begin_0, end = var_6072_end_0, end_mask = var_6072_end_mask_0, x = q_41_cast)[name = tensor("op_6072_cast")]; + tensor var_6076_begin_0 = const()[name = tensor("op_6076_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6076_end_0 = const()[name = tensor("op_6076_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_6076_end_mask_0 = const()[name = tensor("op_6076_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6076_cast = slice_by_index(begin = var_6076_begin_0, end = var_6076_end_0, end_mask = var_6076_end_mask_0, x = q_41_cast)[name = tensor("op_6076_cast")]; + tensor var_6080_begin_0 = const()[name = tensor("op_6080_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6080_end_0 = const()[name = tensor("op_6080_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_6080_end_mask_0 = const()[name = tensor("op_6080_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6080_cast = slice_by_index(begin = var_6080_begin_0, end = var_6080_end_0, end_mask = var_6080_end_mask_0, x = q_41_cast)[name = tensor("op_6080_cast")]; + tensor var_6084_begin_0 = const()[name = tensor("op_6084_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6084_end_0 = const()[name = tensor("op_6084_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_6084_end_mask_0 = const()[name = tensor("op_6084_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6084_cast = slice_by_index(begin = var_6084_begin_0, end = var_6084_end_0, end_mask = var_6084_end_mask_0, x = q_41_cast)[name = tensor("op_6084_cast")]; + tensor var_6088_begin_0 = const()[name = tensor("op_6088_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6088_end_0 = const()[name = tensor("op_6088_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_6088_end_mask_0 = const()[name = tensor("op_6088_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6088_cast = slice_by_index(begin = var_6088_begin_0, end = var_6088_end_0, end_mask = var_6088_end_mask_0, x = q_41_cast)[name = tensor("op_6088_cast")]; + tensor var_6092_begin_0 = const()[name = tensor("op_6092_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6092_end_0 = const()[name = tensor("op_6092_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_6092_end_mask_0 = const()[name = tensor("op_6092_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6092_cast = slice_by_index(begin = var_6092_begin_0, end = var_6092_end_0, end_mask = var_6092_end_mask_0, x = q_41_cast)[name = tensor("op_6092_cast")]; + tensor k_83_perm_0 = const()[name = tensor("k_83_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_6099_begin_0 = const()[name = tensor("op_6099_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6099_end_0 = const()[name = tensor("op_6099_end_0"), val = tensor([2, 2304, 1, 80])]; + tensor var_6099_end_mask_0 = const()[name = tensor("op_6099_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_11 = transpose(perm = k_83_perm_0, x = k_81_cast)[name = tensor("transpose_11")]; + tensor var_6099_cast = slice_by_index(begin = var_6099_begin_0, end = var_6099_end_0, end_mask = var_6099_end_mask_0, x = transpose_11)[name = tensor("op_6099_cast")]; + tensor var_6103_begin_0 = const()[name = tensor("op_6103_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_6103_end_0 = const()[name = tensor("op_6103_end_0"), val = tensor([2, 2304, 1, 160])]; + tensor var_6103_end_mask_0 = const()[name = tensor("op_6103_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6103_cast = slice_by_index(begin = var_6103_begin_0, end = var_6103_end_0, end_mask = var_6103_end_mask_0, x = transpose_11)[name = tensor("op_6103_cast")]; + tensor var_6107_begin_0 = const()[name = tensor("op_6107_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_6107_end_0 = const()[name = tensor("op_6107_end_0"), val = tensor([2, 2304, 1, 240])]; + tensor var_6107_end_mask_0 = const()[name = tensor("op_6107_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6107_cast = slice_by_index(begin = var_6107_begin_0, end = var_6107_end_0, end_mask = var_6107_end_mask_0, x = transpose_11)[name = tensor("op_6107_cast")]; + tensor var_6111_begin_0 = const()[name = tensor("op_6111_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_6111_end_0 = const()[name = tensor("op_6111_end_0"), val = tensor([2, 2304, 1, 320])]; + tensor var_6111_end_mask_0 = const()[name = tensor("op_6111_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6111_cast = slice_by_index(begin = var_6111_begin_0, end = var_6111_end_0, end_mask = var_6111_end_mask_0, x = transpose_11)[name = tensor("op_6111_cast")]; + tensor var_6115_begin_0 = const()[name = tensor("op_6115_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_6115_end_0 = const()[name = tensor("op_6115_end_0"), val = tensor([2, 2304, 1, 400])]; + tensor var_6115_end_mask_0 = const()[name = tensor("op_6115_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6115_cast = slice_by_index(begin = var_6115_begin_0, end = var_6115_end_0, end_mask = var_6115_end_mask_0, x = transpose_11)[name = tensor("op_6115_cast")]; + tensor var_6119_begin_0 = const()[name = tensor("op_6119_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_6119_end_0 = const()[name = tensor("op_6119_end_0"), val = tensor([2, 2304, 1, 480])]; + tensor var_6119_end_mask_0 = const()[name = tensor("op_6119_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6119_cast = slice_by_index(begin = var_6119_begin_0, end = var_6119_end_0, end_mask = var_6119_end_mask_0, x = transpose_11)[name = tensor("op_6119_cast")]; + tensor var_6123_begin_0 = const()[name = tensor("op_6123_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_6123_end_0 = const()[name = tensor("op_6123_end_0"), val = tensor([2, 2304, 1, 560])]; + tensor var_6123_end_mask_0 = const()[name = tensor("op_6123_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6123_cast = slice_by_index(begin = var_6123_begin_0, end = var_6123_end_0, end_mask = var_6123_end_mask_0, x = transpose_11)[name = tensor("op_6123_cast")]; + tensor var_6127_begin_0 = const()[name = tensor("op_6127_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_6127_end_0 = const()[name = tensor("op_6127_end_0"), val = tensor([2, 2304, 1, 640])]; + tensor var_6127_end_mask_0 = const()[name = tensor("op_6127_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6127_cast = slice_by_index(begin = var_6127_begin_0, end = var_6127_end_0, end_mask = var_6127_end_mask_0, x = transpose_11)[name = tensor("op_6127_cast")]; + tensor var_6129_begin_0 = const()[name = tensor("op_6129_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6129_end_0 = const()[name = tensor("op_6129_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_6129_end_mask_0 = const()[name = tensor("op_6129_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6129_cast = slice_by_index(begin = var_6129_begin_0, end = var_6129_end_0, end_mask = var_6129_end_mask_0, x = v_41_cast)[name = tensor("op_6129_cast")]; + tensor var_6133_begin_0 = const()[name = tensor("op_6133_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6133_end_0 = const()[name = tensor("op_6133_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_6133_end_mask_0 = const()[name = tensor("op_6133_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6133_cast = slice_by_index(begin = var_6133_begin_0, end = var_6133_end_0, end_mask = var_6133_end_mask_0, x = v_41_cast)[name = tensor("op_6133_cast")]; + tensor var_6137_begin_0 = const()[name = tensor("op_6137_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6137_end_0 = const()[name = tensor("op_6137_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_6137_end_mask_0 = const()[name = tensor("op_6137_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6137_cast = slice_by_index(begin = var_6137_begin_0, end = var_6137_end_0, end_mask = var_6137_end_mask_0, x = v_41_cast)[name = tensor("op_6137_cast")]; + tensor var_6141_begin_0 = const()[name = tensor("op_6141_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6141_end_0 = const()[name = tensor("op_6141_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_6141_end_mask_0 = const()[name = tensor("op_6141_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6141_cast = slice_by_index(begin = var_6141_begin_0, end = var_6141_end_0, end_mask = var_6141_end_mask_0, x = v_41_cast)[name = tensor("op_6141_cast")]; + tensor var_6145_begin_0 = const()[name = tensor("op_6145_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6145_end_0 = const()[name = tensor("op_6145_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_6145_end_mask_0 = const()[name = tensor("op_6145_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6145_cast = slice_by_index(begin = var_6145_begin_0, end = var_6145_end_0, end_mask = var_6145_end_mask_0, x = v_41_cast)[name = tensor("op_6145_cast")]; + tensor var_6149_begin_0 = const()[name = tensor("op_6149_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6149_end_0 = const()[name = tensor("op_6149_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_6149_end_mask_0 = const()[name = tensor("op_6149_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6149_cast = slice_by_index(begin = var_6149_begin_0, end = var_6149_end_0, end_mask = var_6149_end_mask_0, x = v_41_cast)[name = tensor("op_6149_cast")]; + tensor var_6153_begin_0 = const()[name = tensor("op_6153_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6153_end_0 = const()[name = tensor("op_6153_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_6153_end_mask_0 = const()[name = tensor("op_6153_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6153_cast = slice_by_index(begin = var_6153_begin_0, end = var_6153_end_0, end_mask = var_6153_end_mask_0, x = v_41_cast)[name = tensor("op_6153_cast")]; + tensor var_6157_begin_0 = const()[name = tensor("op_6157_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6157_end_0 = const()[name = tensor("op_6157_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_6157_end_mask_0 = const()[name = tensor("op_6157_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6157_cast = slice_by_index(begin = var_6157_begin_0, end = var_6157_end_0, end_mask = var_6157_end_mask_0, x = v_41_cast)[name = tensor("op_6157_cast")]; + tensor var_6161_equation_0 = const()[name = tensor("op_6161_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6161_cast = einsum(equation = var_6161_equation_0, values = (var_6099_cast, var_6064_cast))[name = tensor("op_6161_cast")]; + tensor var_6162_to_fp16 = const()[name = tensor("op_6162_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_321_cast = mul(x = var_6161_cast, y = var_6162_to_fp16)[name = tensor("aw_321_cast")]; + tensor var_6165_equation_0 = const()[name = tensor("op_6165_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6165_cast = einsum(equation = var_6165_equation_0, values = (var_6103_cast, var_6068_cast))[name = tensor("op_6165_cast")]; + tensor var_6166_to_fp16 = const()[name = tensor("op_6166_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_323_cast = mul(x = var_6165_cast, y = var_6166_to_fp16)[name = tensor("aw_323_cast")]; + tensor var_6169_equation_0 = const()[name = tensor("op_6169_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6169_cast = einsum(equation = var_6169_equation_0, values = (var_6107_cast, var_6072_cast))[name = tensor("op_6169_cast")]; + tensor var_6170_to_fp16 = const()[name = tensor("op_6170_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_325_cast = mul(x = var_6169_cast, y = var_6170_to_fp16)[name = tensor("aw_325_cast")]; + tensor var_6173_equation_0 = const()[name = tensor("op_6173_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6173_cast = einsum(equation = var_6173_equation_0, values = (var_6111_cast, var_6076_cast))[name = tensor("op_6173_cast")]; + tensor var_6174_to_fp16 = const()[name = tensor("op_6174_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_327_cast = mul(x = var_6173_cast, y = var_6174_to_fp16)[name = tensor("aw_327_cast")]; + tensor var_6177_equation_0 = const()[name = tensor("op_6177_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6177_cast = einsum(equation = var_6177_equation_0, values = (var_6115_cast, var_6080_cast))[name = tensor("op_6177_cast")]; + tensor var_6178_to_fp16 = const()[name = tensor("op_6178_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_329_cast = mul(x = var_6177_cast, y = var_6178_to_fp16)[name = tensor("aw_329_cast")]; + tensor var_6181_equation_0 = const()[name = tensor("op_6181_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6181_cast = einsum(equation = var_6181_equation_0, values = (var_6119_cast, var_6084_cast))[name = tensor("op_6181_cast")]; + tensor var_6182_to_fp16 = const()[name = tensor("op_6182_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_331_cast = mul(x = var_6181_cast, y = var_6182_to_fp16)[name = tensor("aw_331_cast")]; + tensor var_6185_equation_0 = const()[name = tensor("op_6185_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6185_cast = einsum(equation = var_6185_equation_0, values = (var_6123_cast, var_6088_cast))[name = tensor("op_6185_cast")]; + tensor var_6186_to_fp16 = const()[name = tensor("op_6186_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_333_cast = mul(x = var_6185_cast, y = var_6186_to_fp16)[name = tensor("aw_333_cast")]; + tensor var_6189_equation_0 = const()[name = tensor("op_6189_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6189_cast = einsum(equation = var_6189_equation_0, values = (var_6127_cast, var_6092_cast))[name = tensor("op_6189_cast")]; + tensor var_6190_to_fp16 = const()[name = tensor("op_6190_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_335_cast = mul(x = var_6189_cast, y = var_6190_to_fp16)[name = tensor("aw_335_cast")]; + tensor var_6192_cast = softmax(axis = var_5930, x = aw_321_cast)[name = tensor("op_6192_cast")]; + tensor var_6193_cast = softmax(axis = var_5930, x = aw_323_cast)[name = tensor("op_6193_cast")]; + tensor var_6194_cast = softmax(axis = var_5930, x = aw_325_cast)[name = tensor("op_6194_cast")]; + tensor var_6195_cast = softmax(axis = var_5930, x = aw_327_cast)[name = tensor("op_6195_cast")]; + tensor var_6196_cast = softmax(axis = var_5930, x = aw_329_cast)[name = tensor("op_6196_cast")]; + tensor var_6197_cast = softmax(axis = var_5930, x = aw_331_cast)[name = tensor("op_6197_cast")]; + tensor var_6198_cast = softmax(axis = var_5930, x = aw_333_cast)[name = tensor("op_6198_cast")]; + tensor var_6199_cast = softmax(axis = var_5930, x = aw_335_cast)[name = tensor("op_6199_cast")]; + tensor var_6201_equation_0 = const()[name = tensor("op_6201_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6201_cast = einsum(equation = var_6201_equation_0, values = (var_6129_cast, var_6192_cast))[name = tensor("op_6201_cast")]; + tensor var_6203_equation_0 = const()[name = tensor("op_6203_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6203_cast = einsum(equation = var_6203_equation_0, values = (var_6133_cast, var_6193_cast))[name = tensor("op_6203_cast")]; + tensor var_6205_equation_0 = const()[name = tensor("op_6205_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6205_cast = einsum(equation = var_6205_equation_0, values = (var_6137_cast, var_6194_cast))[name = tensor("op_6205_cast")]; + tensor var_6207_equation_0 = const()[name = tensor("op_6207_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6207_cast = einsum(equation = var_6207_equation_0, values = (var_6141_cast, var_6195_cast))[name = tensor("op_6207_cast")]; + tensor var_6209_equation_0 = const()[name = tensor("op_6209_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6209_cast = einsum(equation = var_6209_equation_0, values = (var_6145_cast, var_6196_cast))[name = tensor("op_6209_cast")]; + tensor var_6211_equation_0 = const()[name = tensor("op_6211_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6211_cast = einsum(equation = var_6211_equation_0, values = (var_6149_cast, var_6197_cast))[name = tensor("op_6211_cast")]; + tensor var_6213_equation_0 = const()[name = tensor("op_6213_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6213_cast = einsum(equation = var_6213_equation_0, values = (var_6153_cast, var_6198_cast))[name = tensor("op_6213_cast")]; + tensor var_6215_equation_0 = const()[name = tensor("op_6215_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6215_cast = einsum(equation = var_6215_equation_0, values = (var_6157_cast, var_6199_cast))[name = tensor("op_6215_cast")]; + tensor input_383_interleave_0 = const()[name = tensor("input_383_interleave_0"), val = tensor(false)]; + tensor input_383_cast = concat(axis = var_5930, interleave = input_383_interleave_0, values = (var_6201_cast, var_6203_cast, var_6205_cast, var_6207_cast, var_6209_cast, var_6211_cast, var_6213_cast, var_6215_cast))[name = tensor("input_383_cast")]; + tensor var_6221 = const()[name = tensor("op_6221"), val = tensor([1, 1])]; + tensor var_6223 = const()[name = tensor("op_6223"), val = tensor([1, 1])]; + tensor var_6225_pad_type_0 = const()[name = tensor("op_6225_pad_type_0"), val = tensor("custom")]; + tensor var_6225_pad_0 = const()[name = tensor("op_6225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591214528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591521792))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591521984)))]; + tensor var_6225_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_6223, groups = var_5930, pad = var_6225_pad_0, pad_type = var_6225_pad_type_0, strides = var_6221, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_383_cast)[name = tensor("op_6225_cast")]; + tensor inputs_63_cast = add(x = var_6225_cast, y = inputs_61_cast)[name = tensor("inputs_63_cast")]; + tensor var_6229 = const()[name = tensor("op_6229"), val = tensor([1])]; + tensor channels_mean_63_cast = reduce_mean(axes = var_6229, keep_dims = var_5925, x = inputs_63_cast)[name = tensor("channels_mean_63_cast")]; + tensor zero_mean_63_cast = sub(x = inputs_63_cast, y = channels_mean_63_cast)[name = tensor("zero_mean_63_cast")]; + tensor zero_mean_sq_63_cast = mul(x = zero_mean_63_cast, y = zero_mean_63_cast)[name = tensor("zero_mean_sq_63_cast")]; + tensor var_6233 = const()[name = tensor("op_6233"), val = tensor([1])]; + tensor var_6234_cast = reduce_mean(axes = var_6233, keep_dims = var_5925, x = zero_mean_sq_63_cast)[name = tensor("op_6234_cast")]; + tensor var_6235_to_fp16 = const()[name = tensor("op_6235_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6236_cast = add(x = var_6234_cast, y = var_6235_to_fp16)[name = tensor("op_6236_cast")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_6236_cast)[name = tensor("denom_63_cast")]; + tensor out_63_cast = mul(x = zero_mean_63_cast, y = denom_63_cast)[name = tensor("out_63_cast")]; + tensor var_6240_to_fp16 = const()[name = tensor("op_6240_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591523328)))]; + tensor var_6241_cast = add(x = out_63_cast, y = var_6240_to_fp16)[name = tensor("op_6241_cast")]; + tensor var_6243_to_fp16 = const()[name = tensor("op_6243_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591524672)))]; + tensor hidden_states_229_cast = mul(x = var_6241_cast, y = var_6243_to_fp16)[name = tensor("hidden_states_229_cast")]; + tensor var_6250 = const()[name = tensor("op_6250"), val = tensor([1, 1])]; + tensor var_6252 = const()[name = tensor("op_6252"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591526016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591833280))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_43_cast = conv(dilations = var_6252, groups = var_5930, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_6250, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_229_cast)[name = tensor("q_43_cast")]; + tensor var_6256 = const()[name = tensor("op_6256"), val = tensor([1, 1])]; + tensor var_6258 = const()[name = tensor("op_6258"), val = tensor([1, 1])]; + tensor k_85_pad_type_0 = const()[name = tensor("k_85_pad_type_0"), val = tensor("custom")]; + tensor k_85_pad_0 = const()[name = tensor("k_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591833472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592202176))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_85_cast = conv(dilations = var_6258, groups = var_5930, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_6256, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_85_cast")]; + tensor var_6262 = const()[name = tensor("op_6262"), val = tensor([1, 1])]; + tensor var_6264 = const()[name = tensor("op_6264"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592202368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592571072))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_43_cast = conv(dilations = var_6264, groups = var_5930, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_6262, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_43_cast")]; + tensor var_6268_begin_0 = const()[name = tensor("op_6268_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6268_end_0 = const()[name = tensor("op_6268_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_6268_end_mask_0 = const()[name = tensor("op_6268_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6268_cast = slice_by_index(begin = var_6268_begin_0, end = var_6268_end_0, end_mask = var_6268_end_mask_0, x = q_43_cast)[name = tensor("op_6268_cast")]; + tensor var_6272_begin_0 = const()[name = tensor("op_6272_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6272_end_0 = const()[name = tensor("op_6272_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_6272_end_mask_0 = const()[name = tensor("op_6272_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6272_cast = slice_by_index(begin = var_6272_begin_0, end = var_6272_end_0, end_mask = var_6272_end_mask_0, x = q_43_cast)[name = tensor("op_6272_cast")]; + tensor var_6276_begin_0 = const()[name = tensor("op_6276_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6276_end_0 = const()[name = tensor("op_6276_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_6276_end_mask_0 = const()[name = tensor("op_6276_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6276_cast = slice_by_index(begin = var_6276_begin_0, end = var_6276_end_0, end_mask = var_6276_end_mask_0, x = q_43_cast)[name = tensor("op_6276_cast")]; + tensor var_6280_begin_0 = const()[name = tensor("op_6280_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6280_end_0 = const()[name = tensor("op_6280_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_6280_end_mask_0 = const()[name = tensor("op_6280_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6280_cast = slice_by_index(begin = var_6280_begin_0, end = var_6280_end_0, end_mask = var_6280_end_mask_0, x = q_43_cast)[name = tensor("op_6280_cast")]; + tensor var_6284_begin_0 = const()[name = tensor("op_6284_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6284_end_0 = const()[name = tensor("op_6284_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_6284_end_mask_0 = const()[name = tensor("op_6284_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6284_cast = slice_by_index(begin = var_6284_begin_0, end = var_6284_end_0, end_mask = var_6284_end_mask_0, x = q_43_cast)[name = tensor("op_6284_cast")]; + tensor var_6288_begin_0 = const()[name = tensor("op_6288_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6288_end_0 = const()[name = tensor("op_6288_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_6288_end_mask_0 = const()[name = tensor("op_6288_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6288_cast = slice_by_index(begin = var_6288_begin_0, end = var_6288_end_0, end_mask = var_6288_end_mask_0, x = q_43_cast)[name = tensor("op_6288_cast")]; + tensor var_6292_begin_0 = const()[name = tensor("op_6292_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6292_end_0 = const()[name = tensor("op_6292_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_6292_end_mask_0 = const()[name = tensor("op_6292_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6292_cast = slice_by_index(begin = var_6292_begin_0, end = var_6292_end_0, end_mask = var_6292_end_mask_0, x = q_43_cast)[name = tensor("op_6292_cast")]; + tensor var_6296_begin_0 = const()[name = tensor("op_6296_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6296_end_0 = const()[name = tensor("op_6296_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_6296_end_mask_0 = const()[name = tensor("op_6296_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6296_cast = slice_by_index(begin = var_6296_begin_0, end = var_6296_end_0, end_mask = var_6296_end_mask_0, x = q_43_cast)[name = tensor("op_6296_cast")]; + tensor k_87_perm_0 = const()[name = tensor("k_87_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_6303_begin_0 = const()[name = tensor("op_6303_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6303_end_0 = const()[name = tensor("op_6303_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_6303_end_mask_0 = const()[name = tensor("op_6303_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_10 = transpose(perm = k_87_perm_0, x = k_85_cast)[name = tensor("transpose_10")]; + tensor var_6303_cast = slice_by_index(begin = var_6303_begin_0, end = var_6303_end_0, end_mask = var_6303_end_mask_0, x = transpose_10)[name = tensor("op_6303_cast")]; + tensor var_6307_begin_0 = const()[name = tensor("op_6307_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_6307_end_0 = const()[name = tensor("op_6307_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_6307_end_mask_0 = const()[name = tensor("op_6307_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6307_cast = slice_by_index(begin = var_6307_begin_0, end = var_6307_end_0, end_mask = var_6307_end_mask_0, x = transpose_10)[name = tensor("op_6307_cast")]; + tensor var_6311_begin_0 = const()[name = tensor("op_6311_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_6311_end_0 = const()[name = tensor("op_6311_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_6311_end_mask_0 = const()[name = tensor("op_6311_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6311_cast = slice_by_index(begin = var_6311_begin_0, end = var_6311_end_0, end_mask = var_6311_end_mask_0, x = transpose_10)[name = tensor("op_6311_cast")]; + tensor var_6315_begin_0 = const()[name = tensor("op_6315_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_6315_end_0 = const()[name = tensor("op_6315_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_6315_end_mask_0 = const()[name = tensor("op_6315_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6315_cast = slice_by_index(begin = var_6315_begin_0, end = var_6315_end_0, end_mask = var_6315_end_mask_0, x = transpose_10)[name = tensor("op_6315_cast")]; + tensor var_6319_begin_0 = const()[name = tensor("op_6319_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_6319_end_0 = const()[name = tensor("op_6319_end_0"), val = tensor([2, 77, 1, 400])]; + tensor var_6319_end_mask_0 = const()[name = tensor("op_6319_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6319_cast = slice_by_index(begin = var_6319_begin_0, end = var_6319_end_0, end_mask = var_6319_end_mask_0, x = transpose_10)[name = tensor("op_6319_cast")]; + tensor var_6323_begin_0 = const()[name = tensor("op_6323_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_6323_end_0 = const()[name = tensor("op_6323_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_6323_end_mask_0 = const()[name = tensor("op_6323_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6323_cast = slice_by_index(begin = var_6323_begin_0, end = var_6323_end_0, end_mask = var_6323_end_mask_0, x = transpose_10)[name = tensor("op_6323_cast")]; + tensor var_6327_begin_0 = const()[name = tensor("op_6327_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_6327_end_0 = const()[name = tensor("op_6327_end_0"), val = tensor([2, 77, 1, 560])]; + tensor var_6327_end_mask_0 = const()[name = tensor("op_6327_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6327_cast = slice_by_index(begin = var_6327_begin_0, end = var_6327_end_0, end_mask = var_6327_end_mask_0, x = transpose_10)[name = tensor("op_6327_cast")]; + tensor var_6331_begin_0 = const()[name = tensor("op_6331_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_6331_end_0 = const()[name = tensor("op_6331_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_6331_end_mask_0 = const()[name = tensor("op_6331_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6331_cast = slice_by_index(begin = var_6331_begin_0, end = var_6331_end_0, end_mask = var_6331_end_mask_0, x = transpose_10)[name = tensor("op_6331_cast")]; + tensor var_6333_begin_0 = const()[name = tensor("op_6333_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6333_end_0 = const()[name = tensor("op_6333_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_6333_end_mask_0 = const()[name = tensor("op_6333_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6333_cast = slice_by_index(begin = var_6333_begin_0, end = var_6333_end_0, end_mask = var_6333_end_mask_0, x = v_43_cast)[name = tensor("op_6333_cast")]; + tensor var_6337_begin_0 = const()[name = tensor("op_6337_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6337_end_0 = const()[name = tensor("op_6337_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_6337_end_mask_0 = const()[name = tensor("op_6337_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6337_cast = slice_by_index(begin = var_6337_begin_0, end = var_6337_end_0, end_mask = var_6337_end_mask_0, x = v_43_cast)[name = tensor("op_6337_cast")]; + tensor var_6341_begin_0 = const()[name = tensor("op_6341_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6341_end_0 = const()[name = tensor("op_6341_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_6341_end_mask_0 = const()[name = tensor("op_6341_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6341_cast = slice_by_index(begin = var_6341_begin_0, end = var_6341_end_0, end_mask = var_6341_end_mask_0, x = v_43_cast)[name = tensor("op_6341_cast")]; + tensor var_6345_begin_0 = const()[name = tensor("op_6345_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6345_end_0 = const()[name = tensor("op_6345_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_6345_end_mask_0 = const()[name = tensor("op_6345_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6345_cast = slice_by_index(begin = var_6345_begin_0, end = var_6345_end_0, end_mask = var_6345_end_mask_0, x = v_43_cast)[name = tensor("op_6345_cast")]; + tensor var_6349_begin_0 = const()[name = tensor("op_6349_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6349_end_0 = const()[name = tensor("op_6349_end_0"), val = tensor([2, 400, 1, 77])]; + tensor var_6349_end_mask_0 = const()[name = tensor("op_6349_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6349_cast = slice_by_index(begin = var_6349_begin_0, end = var_6349_end_0, end_mask = var_6349_end_mask_0, x = v_43_cast)[name = tensor("op_6349_cast")]; + tensor var_6353_begin_0 = const()[name = tensor("op_6353_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6353_end_0 = const()[name = tensor("op_6353_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_6353_end_mask_0 = const()[name = tensor("op_6353_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6353_cast = slice_by_index(begin = var_6353_begin_0, end = var_6353_end_0, end_mask = var_6353_end_mask_0, x = v_43_cast)[name = tensor("op_6353_cast")]; + tensor var_6357_begin_0 = const()[name = tensor("op_6357_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6357_end_0 = const()[name = tensor("op_6357_end_0"), val = tensor([2, 560, 1, 77])]; + tensor var_6357_end_mask_0 = const()[name = tensor("op_6357_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6357_cast = slice_by_index(begin = var_6357_begin_0, end = var_6357_end_0, end_mask = var_6357_end_mask_0, x = v_43_cast)[name = tensor("op_6357_cast")]; + tensor var_6361_begin_0 = const()[name = tensor("op_6361_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6361_end_0 = const()[name = tensor("op_6361_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_6361_end_mask_0 = const()[name = tensor("op_6361_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6361_cast = slice_by_index(begin = var_6361_begin_0, end = var_6361_end_0, end_mask = var_6361_end_mask_0, x = v_43_cast)[name = tensor("op_6361_cast")]; + tensor var_6365_equation_0 = const()[name = tensor("op_6365_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6365_cast = einsum(equation = var_6365_equation_0, values = (var_6303_cast, var_6268_cast))[name = tensor("op_6365_cast")]; + tensor var_6366_to_fp16 = const()[name = tensor("op_6366_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_337_cast = mul(x = var_6365_cast, y = var_6366_to_fp16)[name = tensor("aw_337_cast")]; + tensor var_6369_equation_0 = const()[name = tensor("op_6369_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6369_cast = einsum(equation = var_6369_equation_0, values = (var_6307_cast, var_6272_cast))[name = tensor("op_6369_cast")]; + tensor var_6370_to_fp16 = const()[name = tensor("op_6370_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_339_cast = mul(x = var_6369_cast, y = var_6370_to_fp16)[name = tensor("aw_339_cast")]; + tensor var_6373_equation_0 = const()[name = tensor("op_6373_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6373_cast = einsum(equation = var_6373_equation_0, values = (var_6311_cast, var_6276_cast))[name = tensor("op_6373_cast")]; + tensor var_6374_to_fp16 = const()[name = tensor("op_6374_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_341_cast = mul(x = var_6373_cast, y = var_6374_to_fp16)[name = tensor("aw_341_cast")]; + tensor var_6377_equation_0 = const()[name = tensor("op_6377_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6377_cast = einsum(equation = var_6377_equation_0, values = (var_6315_cast, var_6280_cast))[name = tensor("op_6377_cast")]; + tensor var_6378_to_fp16 = const()[name = tensor("op_6378_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_343_cast = mul(x = var_6377_cast, y = var_6378_to_fp16)[name = tensor("aw_343_cast")]; + tensor var_6381_equation_0 = const()[name = tensor("op_6381_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6381_cast = einsum(equation = var_6381_equation_0, values = (var_6319_cast, var_6284_cast))[name = tensor("op_6381_cast")]; + tensor var_6382_to_fp16 = const()[name = tensor("op_6382_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_345_cast = mul(x = var_6381_cast, y = var_6382_to_fp16)[name = tensor("aw_345_cast")]; + tensor var_6385_equation_0 = const()[name = tensor("op_6385_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6385_cast = einsum(equation = var_6385_equation_0, values = (var_6323_cast, var_6288_cast))[name = tensor("op_6385_cast")]; + tensor var_6386_to_fp16 = const()[name = tensor("op_6386_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_347_cast = mul(x = var_6385_cast, y = var_6386_to_fp16)[name = tensor("aw_347_cast")]; + tensor var_6389_equation_0 = const()[name = tensor("op_6389_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6389_cast = einsum(equation = var_6389_equation_0, values = (var_6327_cast, var_6292_cast))[name = tensor("op_6389_cast")]; + tensor var_6390_to_fp16 = const()[name = tensor("op_6390_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_349_cast = mul(x = var_6389_cast, y = var_6390_to_fp16)[name = tensor("aw_349_cast")]; + tensor var_6393_equation_0 = const()[name = tensor("op_6393_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6393_cast = einsum(equation = var_6393_equation_0, values = (var_6331_cast, var_6296_cast))[name = tensor("op_6393_cast")]; + tensor var_6394_to_fp16 = const()[name = tensor("op_6394_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_351_cast = mul(x = var_6393_cast, y = var_6394_to_fp16)[name = tensor("aw_351_cast")]; + tensor var_6396_cast = softmax(axis = var_5930, x = aw_337_cast)[name = tensor("op_6396_cast")]; + tensor var_6397_cast = softmax(axis = var_5930, x = aw_339_cast)[name = tensor("op_6397_cast")]; + tensor var_6398_cast = softmax(axis = var_5930, x = aw_341_cast)[name = tensor("op_6398_cast")]; + tensor var_6399_cast = softmax(axis = var_5930, x = aw_343_cast)[name = tensor("op_6399_cast")]; + tensor var_6400_cast = softmax(axis = var_5930, x = aw_345_cast)[name = tensor("op_6400_cast")]; + tensor var_6401_cast = softmax(axis = var_5930, x = aw_347_cast)[name = tensor("op_6401_cast")]; + tensor var_6402_cast = softmax(axis = var_5930, x = aw_349_cast)[name = tensor("op_6402_cast")]; + tensor var_6403_cast = softmax(axis = var_5930, x = aw_351_cast)[name = tensor("op_6403_cast")]; + tensor var_6405_equation_0 = const()[name = tensor("op_6405_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6405_cast = einsum(equation = var_6405_equation_0, values = (var_6333_cast, var_6396_cast))[name = tensor("op_6405_cast")]; + tensor var_6407_equation_0 = const()[name = tensor("op_6407_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6407_cast = einsum(equation = var_6407_equation_0, values = (var_6337_cast, var_6397_cast))[name = tensor("op_6407_cast")]; + tensor var_6409_equation_0 = const()[name = tensor("op_6409_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6409_cast = einsum(equation = var_6409_equation_0, values = (var_6341_cast, var_6398_cast))[name = tensor("op_6409_cast")]; + tensor var_6411_equation_0 = const()[name = tensor("op_6411_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6411_cast = einsum(equation = var_6411_equation_0, values = (var_6345_cast, var_6399_cast))[name = tensor("op_6411_cast")]; + tensor var_6413_equation_0 = const()[name = tensor("op_6413_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6413_cast = einsum(equation = var_6413_equation_0, values = (var_6349_cast, var_6400_cast))[name = tensor("op_6413_cast")]; + tensor var_6415_equation_0 = const()[name = tensor("op_6415_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6415_cast = einsum(equation = var_6415_equation_0, values = (var_6353_cast, var_6401_cast))[name = tensor("op_6415_cast")]; + tensor var_6417_equation_0 = const()[name = tensor("op_6417_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6417_cast = einsum(equation = var_6417_equation_0, values = (var_6357_cast, var_6402_cast))[name = tensor("op_6417_cast")]; + tensor var_6419_equation_0 = const()[name = tensor("op_6419_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6419_cast = einsum(equation = var_6419_equation_0, values = (var_6361_cast, var_6403_cast))[name = tensor("op_6419_cast")]; + tensor input_385_interleave_0 = const()[name = tensor("input_385_interleave_0"), val = tensor(false)]; + tensor input_385_cast = concat(axis = var_5930, interleave = input_385_interleave_0, values = (var_6405_cast, var_6407_cast, var_6409_cast, var_6411_cast, var_6413_cast, var_6415_cast, var_6417_cast, var_6419_cast))[name = tensor("input_385_cast")]; + tensor var_6425 = const()[name = tensor("op_6425"), val = tensor([1, 1])]; + tensor var_6427 = const()[name = tensor("op_6427"), val = tensor([1, 1])]; + tensor var_6429_pad_type_0 = const()[name = tensor("op_6429_pad_type_0"), val = tensor("custom")]; + tensor var_6429_pad_0 = const()[name = tensor("op_6429_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592571264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592878528))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592878720)))]; + tensor var_6429_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_6427, groups = var_5930, pad = var_6429_pad_0, pad_type = var_6429_pad_type_0, strides = var_6425, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_385_cast)[name = tensor("op_6429_cast")]; + tensor inputs_65_cast = add(x = var_6429_cast, y = inputs_63_cast)[name = tensor("inputs_65_cast")]; + tensor var_6433 = const()[name = tensor("op_6433"), val = tensor([1])]; + tensor channels_mean_65_cast = reduce_mean(axes = var_6433, keep_dims = var_5925, x = inputs_65_cast)[name = tensor("channels_mean_65_cast")]; + tensor zero_mean_65_cast = sub(x = inputs_65_cast, y = channels_mean_65_cast)[name = tensor("zero_mean_65_cast")]; + tensor zero_mean_sq_65_cast = mul(x = zero_mean_65_cast, y = zero_mean_65_cast)[name = tensor("zero_mean_sq_65_cast")]; + tensor var_6437 = const()[name = tensor("op_6437"), val = tensor([1])]; + tensor var_6438_cast = reduce_mean(axes = var_6437, keep_dims = var_5925, x = zero_mean_sq_65_cast)[name = tensor("op_6438_cast")]; + tensor var_6439_to_fp16 = const()[name = tensor("op_6439_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6440_cast = add(x = var_6438_cast, y = var_6439_to_fp16)[name = tensor("op_6440_cast")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_6440_cast)[name = tensor("denom_65_cast")]; + tensor out_65_cast = mul(x = zero_mean_65_cast, y = denom_65_cast)[name = tensor("out_65_cast")]; + tensor var_6444_to_fp16 = const()[name = tensor("op_6444_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592880064)))]; + tensor var_6445_cast = add(x = out_65_cast, y = var_6444_to_fp16)[name = tensor("op_6445_cast")]; + tensor var_6447_to_fp16 = const()[name = tensor("op_6447_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592881408)))]; + tensor input_387_cast = mul(x = var_6445_cast, y = var_6447_to_fp16)[name = tensor("input_387_cast")]; + tensor var_6455 = const()[name = tensor("op_6455"), val = tensor([1, 1])]; + tensor var_6457 = const()[name = tensor("op_6457"), val = tensor([1, 1])]; + tensor var_6459_pad_type_0 = const()[name = tensor("op_6459_pad_type_0"), val = tensor("custom")]; + tensor var_6459_pad_0 = const()[name = tensor("op_6459_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592882752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595340416))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595340608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595344512))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_6459_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6457, groups = var_5930, pad = var_6459_pad_0, pad_type = var_6459_pad_type_0, strides = var_6455, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_387_cast)[name = tensor("op_6459_cast")]; + tensor var_6460_split_sizes_0 = const()[name = tensor("op_6460_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_6460_axis_0 = const()[name = tensor("op_6460_axis_0"), val = tensor(1)]; + tensor var_6460_cast_0, tensor var_6460_cast_1 = split(axis = var_6460_axis_0, split_sizes = var_6460_split_sizes_0, x = var_6459_cast)[name = tensor("op_6460_cast")]; + tensor var_6462_mode_0 = const()[name = tensor("op_6462_mode_0"), val = tensor("EXACT")]; + tensor var_6462_cast = gelu(mode = var_6462_mode_0, x = var_6460_cast_1)[name = tensor("op_6462_cast")]; + tensor input_389_cast = mul(x = var_6460_cast_0, y = var_6462_cast)[name = tensor("input_389_cast")]; + tensor var_6466 = const()[name = tensor("op_6466"), val = tensor([1, 1])]; + tensor var_6468 = const()[name = tensor("op_6468"), val = tensor([1, 1])]; + tensor var_6470_pad_type_0 = const()[name = tensor("op_6470_pad_type_0"), val = tensor("custom")]; + tensor var_6470_pad_0 = const()[name = tensor("op_6470_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595344704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596573568))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596573760)))]; + tensor var_6470_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_6468, groups = var_5930, pad = var_6470_pad_0, pad_type = var_6470_pad_type_0, strides = var_6466, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_389_cast)[name = tensor("op_6470_cast")]; + tensor hidden_states_233_cast = add(x = var_6470_cast, y = inputs_65_cast)[name = tensor("hidden_states_233_cast")]; + tensor var_6472 = const()[name = tensor("op_6472"), val = tensor([2, 640, 48, 48])]; + tensor input_391_cast = reshape(shape = var_6472, x = hidden_states_233_cast)[name = tensor("input_391_cast")]; + tensor var_6476 = const()[name = tensor("op_6476"), val = tensor([1, 1])]; + tensor var_6478 = const()[name = tensor("op_6478"), val = tensor([1, 1])]; + tensor hidden_states_235_pad_type_0 = const()[name = tensor("hidden_states_235_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_235_pad_0 = const()[name = tensor("hidden_states_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596575104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596882368))), name = tensor("up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596882560)))]; + tensor hidden_states_235_cast = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_6478, groups = var_5930, pad = hidden_states_235_pad_0, pad_type = hidden_states_235_pad_type_0, strides = var_6476, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_391_cast)[name = tensor("hidden_states_235_cast")]; + tensor hidden_states_237_cast = add(x = hidden_states_235_cast, y = hidden_states_223_cast)[name = tensor("hidden_states_237_cast")]; + tensor input_393_interleave_0 = const()[name = tensor("input_393_interleave_0"), val = tensor(false)]; + tensor input_393_cast = concat(axis = var_5930, interleave = input_393_interleave_0, values = (hidden_states_237_cast, input_89_cast))[name = tensor("input_393_cast")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 40, 48, 48])]; + tensor reshape_180_cast = reshape(shape = reshape_180_shape_0, x = input_393_cast)[name = tensor("reshape_180_cast")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_135_cast")]; + tensor sub_90_cast = sub(x = reshape_180_cast, y = reduce_mean_135_cast)[name = tensor("sub_90_cast")]; + tensor square_45_cast = square(x = sub_90_cast)[name = tensor("square_45_cast")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_137_cast")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast = add(x = reduce_mean_137_cast, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast")]; + tensor sqrt_45_cast = sqrt(x = add_90_cast)[name = tensor("sqrt_45_cast")]; + tensor real_div_45_cast = real_div(x = sub_90_cast, y = sqrt_45_cast)[name = tensor("real_div_45_cast")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 1280, 48, 48])]; + tensor reshape_181_cast = reshape(shape = reshape_181_shape_0, x = real_div_45_cast)[name = tensor("reshape_181_cast")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596883904)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596886528)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_91_cast")]; + tensor input_397_cast = silu(x = add_91_cast)[name = tensor("input_397_cast")]; + tensor var_6496 = const()[name = tensor("op_6496"), val = tensor([1, 1])]; + tensor var_6498 = const()[name = tensor("op_6498"), val = tensor([1, 1])]; + tensor hidden_states_239_pad_type_0 = const()[name = tensor("hidden_states_239_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_239_pad_0 = const()[name = tensor("hidden_states_239_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596889152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602418816))), name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1280, 3, 3])]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602419008)))]; + tensor hidden_states_239_cast = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_6498, groups = var_5930, pad = hidden_states_239_pad_0, pad_type = hidden_states_239_pad_type_0, strides = var_6496, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_397_cast)[name = tensor("hidden_states_239_cast")]; + tensor var_6504 = const()[name = tensor("op_6504"), val = tensor([1, 1])]; + tensor var_6506 = const()[name = tensor("op_6506"), val = tensor([1, 1])]; + tensor temb_35_pad_type_0 = const()[name = tensor("temb_35_pad_type_0"), val = tensor("custom")]; + tensor temb_35_pad_0 = const()[name = tensor("temb_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602420352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603034816))), name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603035008)))]; + tensor temb_35_cast = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_6506, groups = var_5930, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_6504, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_35_cast")]; + tensor input_401_cast = add(x = hidden_states_239_cast, y = temb_35_cast)[name = tensor("input_401_cast")]; + tensor reshape_184_shape_0 = const()[name = tensor("reshape_184_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_184_cast = reshape(shape = reshape_184_shape_0, x = input_401_cast)[name = tensor("reshape_184_cast")]; + tensor reduce_mean_138_axes_0 = const()[name = tensor("reduce_mean_138_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_138_keep_dims_0 = const()[name = tensor("reduce_mean_138_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_138_cast")]; + tensor sub_92_cast = sub(x = reshape_184_cast, y = reduce_mean_138_cast)[name = tensor("sub_92_cast")]; + tensor square_46_cast = square(x = sub_92_cast)[name = tensor("square_46_cast")]; + tensor reduce_mean_140_axes_0 = const()[name = tensor("reduce_mean_140_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_140_keep_dims_0 = const()[name = tensor("reduce_mean_140_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_140_cast")]; + tensor add_92_y_0_to_fp16 = const()[name = tensor("add_92_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_92_cast = add(x = reduce_mean_140_cast, y = add_92_y_0_to_fp16)[name = tensor("add_92_cast")]; + tensor sqrt_46_cast = sqrt(x = add_92_cast)[name = tensor("sqrt_46_cast")]; + tensor real_div_46_cast = real_div(x = sub_92_cast, y = sqrt_46_cast)[name = tensor("real_div_46_cast")]; + tensor reshape_185_shape_0 = const()[name = tensor("reshape_185_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_185_cast = reshape(shape = reshape_185_shape_0, x = real_div_46_cast)[name = tensor("reshape_185_cast")]; + tensor add_93_gamma_0_to_fp16 = const()[name = tensor("add_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603036352)))]; + tensor add_93_beta_0_to_fp16 = const()[name = tensor("add_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603037696)))]; + tensor add_93_epsilon_0_to_fp16 = const()[name = tensor("add_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_93_cast")]; + tensor input_405_cast = silu(x = add_93_cast)[name = tensor("input_405_cast")]; + tensor var_6516 = const()[name = tensor("op_6516"), val = tensor([1, 1])]; + tensor var_6518 = const()[name = tensor("op_6518"), val = tensor([1, 1])]; + tensor hidden_states_241_pad_type_0 = const()[name = tensor("hidden_states_241_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_241_pad_0 = const()[name = tensor("hidden_states_241_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603039040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605803904))), name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605804096)))]; + tensor hidden_states_241_cast = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_6518, groups = var_5930, pad = hidden_states_241_pad_0, pad_type = hidden_states_241_pad_type_0, strides = var_6516, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_405_cast)[name = tensor("hidden_states_241_cast")]; + tensor var_6523 = const()[name = tensor("op_6523"), val = tensor([1, 1])]; + tensor var_6525 = const()[name = tensor("op_6525"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605805440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606419904))), name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606420096)))]; + tensor x_19_cast = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_6525, groups = var_5930, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_6523, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_393_cast)[name = tensor("x_19_cast")]; + tensor hidden_states_243_cast = add(x = x_19_cast, y = hidden_states_241_cast)[name = tensor("hidden_states_243_cast")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_188_cast = reshape(shape = reshape_188_shape_0, x = hidden_states_243_cast)[name = tensor("reshape_188_cast")]; + tensor reduce_mean_141_axes_0 = const()[name = tensor("reduce_mean_141_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_141_keep_dims_0 = const()[name = tensor("reduce_mean_141_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_141_cast")]; + tensor sub_94_cast = sub(x = reshape_188_cast, y = reduce_mean_141_cast)[name = tensor("sub_94_cast")]; + tensor square_47_cast = square(x = sub_94_cast)[name = tensor("square_47_cast")]; + tensor reduce_mean_143_axes_0 = const()[name = tensor("reduce_mean_143_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_143_keep_dims_0 = const()[name = tensor("reduce_mean_143_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_143_cast")]; + tensor add_94_y_0_to_fp16 = const()[name = tensor("add_94_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_94_cast = add(x = reduce_mean_143_cast, y = add_94_y_0_to_fp16)[name = tensor("add_94_cast")]; + tensor sqrt_47_cast = sqrt(x = add_94_cast)[name = tensor("sqrt_47_cast")]; + tensor real_div_47_cast = real_div(x = sub_94_cast, y = sqrt_47_cast)[name = tensor("real_div_47_cast")]; + tensor reshape_189_shape_0 = const()[name = tensor("reshape_189_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_189_cast = reshape(shape = reshape_189_shape_0, x = real_div_47_cast)[name = tensor("reshape_189_cast")]; + tensor add_95_gamma_0_to_fp16 = const()[name = tensor("add_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606421440)))]; + tensor add_95_beta_0_to_fp16 = const()[name = tensor("add_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606422784)))]; + tensor add_95_epsilon_0_to_fp16 = const()[name = tensor("add_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_95_cast")]; + tensor var_6545 = const()[name = tensor("op_6545"), val = tensor([1, 1])]; + tensor var_6547 = const()[name = tensor("op_6547"), val = tensor([1, 1])]; + tensor hidden_states_245_pad_type_0 = const()[name = tensor("hidden_states_245_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_245_pad_0 = const()[name = tensor("hidden_states_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606424128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606731392))), name = tensor("up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606731584)))]; + tensor hidden_states_245_cast = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_6547, groups = var_5930, pad = hidden_states_245_pad_0, pad_type = hidden_states_245_pad_type_0, strides = var_6545, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_95_cast)[name = tensor("hidden_states_245_cast")]; + tensor var_6552 = const()[name = tensor("op_6552"), val = tensor([2, 640, 1, 2304])]; + tensor inputs_67_cast = reshape(shape = var_6552, x = hidden_states_245_cast)[name = tensor("inputs_67_cast")]; + tensor var_6562 = const()[name = tensor("op_6562"), val = tensor([1])]; + tensor channels_mean_67_cast = reduce_mean(axes = var_6562, keep_dims = var_5925, x = inputs_67_cast)[name = tensor("channels_mean_67_cast")]; + tensor zero_mean_67_cast = sub(x = inputs_67_cast, y = channels_mean_67_cast)[name = tensor("zero_mean_67_cast")]; + tensor zero_mean_sq_67_cast = mul(x = zero_mean_67_cast, y = zero_mean_67_cast)[name = tensor("zero_mean_sq_67_cast")]; + tensor var_6566 = const()[name = tensor("op_6566"), val = tensor([1])]; + tensor var_6567_cast = reduce_mean(axes = var_6566, keep_dims = var_5925, x = zero_mean_sq_67_cast)[name = tensor("op_6567_cast")]; + tensor var_6568_to_fp16 = const()[name = tensor("op_6568_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6569_cast = add(x = var_6567_cast, y = var_6568_to_fp16)[name = tensor("op_6569_cast")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_6569_cast)[name = tensor("denom_67_cast")]; + tensor out_67_cast = mul(x = zero_mean_67_cast, y = denom_67_cast)[name = tensor("out_67_cast")]; + tensor var_6573_to_fp16 = const()[name = tensor("op_6573_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606732928)))]; + tensor var_6574_cast = add(x = out_67_cast, y = var_6573_to_fp16)[name = tensor("op_6574_cast")]; + tensor var_6576_to_fp16 = const()[name = tensor("op_6576_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606734272)))]; + tensor hidden_states_247_cast = mul(x = var_6574_cast, y = var_6576_to_fp16)[name = tensor("hidden_states_247_cast")]; + tensor var_6583 = const()[name = tensor("op_6583"), val = tensor([1, 1])]; + tensor var_6585 = const()[name = tensor("op_6585"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606735616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607042880))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_45_cast = conv(dilations = var_6585, groups = var_5930, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_6583, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor("q_45_cast")]; + tensor var_6589 = const()[name = tensor("op_6589"), val = tensor([1, 1])]; + tensor var_6591 = const()[name = tensor("op_6591"), val = tensor([1, 1])]; + tensor k_89_pad_type_0 = const()[name = tensor("k_89_pad_type_0"), val = tensor("custom")]; + tensor k_89_pad_0 = const()[name = tensor("k_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607043072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607350336))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_89_cast = conv(dilations = var_6591, groups = var_5930, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_6589, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor("k_89_cast")]; + tensor var_6595 = const()[name = tensor("op_6595"), val = tensor([1, 1])]; + tensor var_6597 = const()[name = tensor("op_6597"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607350528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607657792))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_45_cast = conv(dilations = var_6597, groups = var_5930, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_6595, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor("v_45_cast")]; + tensor var_6601_begin_0 = const()[name = tensor("op_6601_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6601_end_0 = const()[name = tensor("op_6601_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_6601_end_mask_0 = const()[name = tensor("op_6601_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6601_cast = slice_by_index(begin = var_6601_begin_0, end = var_6601_end_0, end_mask = var_6601_end_mask_0, x = q_45_cast)[name = tensor("op_6601_cast")]; + tensor var_6605_begin_0 = const()[name = tensor("op_6605_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6605_end_0 = const()[name = tensor("op_6605_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_6605_end_mask_0 = const()[name = tensor("op_6605_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6605_cast = slice_by_index(begin = var_6605_begin_0, end = var_6605_end_0, end_mask = var_6605_end_mask_0, x = q_45_cast)[name = tensor("op_6605_cast")]; + tensor var_6609_begin_0 = const()[name = tensor("op_6609_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6609_end_0 = const()[name = tensor("op_6609_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_6609_end_mask_0 = const()[name = tensor("op_6609_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6609_cast = slice_by_index(begin = var_6609_begin_0, end = var_6609_end_0, end_mask = var_6609_end_mask_0, x = q_45_cast)[name = tensor("op_6609_cast")]; + tensor var_6613_begin_0 = const()[name = tensor("op_6613_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6613_end_0 = const()[name = tensor("op_6613_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_6613_end_mask_0 = const()[name = tensor("op_6613_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6613_cast = slice_by_index(begin = var_6613_begin_0, end = var_6613_end_0, end_mask = var_6613_end_mask_0, x = q_45_cast)[name = tensor("op_6613_cast")]; + tensor var_6617_begin_0 = const()[name = tensor("op_6617_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6617_end_0 = const()[name = tensor("op_6617_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_6617_end_mask_0 = const()[name = tensor("op_6617_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6617_cast = slice_by_index(begin = var_6617_begin_0, end = var_6617_end_0, end_mask = var_6617_end_mask_0, x = q_45_cast)[name = tensor("op_6617_cast")]; + tensor var_6621_begin_0 = const()[name = tensor("op_6621_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6621_end_0 = const()[name = tensor("op_6621_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_6621_end_mask_0 = const()[name = tensor("op_6621_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6621_cast = slice_by_index(begin = var_6621_begin_0, end = var_6621_end_0, end_mask = var_6621_end_mask_0, x = q_45_cast)[name = tensor("op_6621_cast")]; + tensor var_6625_begin_0 = const()[name = tensor("op_6625_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6625_end_0 = const()[name = tensor("op_6625_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_6625_end_mask_0 = const()[name = tensor("op_6625_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6625_cast = slice_by_index(begin = var_6625_begin_0, end = var_6625_end_0, end_mask = var_6625_end_mask_0, x = q_45_cast)[name = tensor("op_6625_cast")]; + tensor var_6629_begin_0 = const()[name = tensor("op_6629_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6629_end_0 = const()[name = tensor("op_6629_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_6629_end_mask_0 = const()[name = tensor("op_6629_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6629_cast = slice_by_index(begin = var_6629_begin_0, end = var_6629_end_0, end_mask = var_6629_end_mask_0, x = q_45_cast)[name = tensor("op_6629_cast")]; + tensor k_91_perm_0 = const()[name = tensor("k_91_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_6636_begin_0 = const()[name = tensor("op_6636_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6636_end_0 = const()[name = tensor("op_6636_end_0"), val = tensor([2, 2304, 1, 80])]; + tensor var_6636_end_mask_0 = const()[name = tensor("op_6636_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_9 = transpose(perm = k_91_perm_0, x = k_89_cast)[name = tensor("transpose_9")]; + tensor var_6636_cast = slice_by_index(begin = var_6636_begin_0, end = var_6636_end_0, end_mask = var_6636_end_mask_0, x = transpose_9)[name = tensor("op_6636_cast")]; + tensor var_6640_begin_0 = const()[name = tensor("op_6640_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_6640_end_0 = const()[name = tensor("op_6640_end_0"), val = tensor([2, 2304, 1, 160])]; + tensor var_6640_end_mask_0 = const()[name = tensor("op_6640_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6640_cast = slice_by_index(begin = var_6640_begin_0, end = var_6640_end_0, end_mask = var_6640_end_mask_0, x = transpose_9)[name = tensor("op_6640_cast")]; + tensor var_6644_begin_0 = const()[name = tensor("op_6644_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_6644_end_0 = const()[name = tensor("op_6644_end_0"), val = tensor([2, 2304, 1, 240])]; + tensor var_6644_end_mask_0 = const()[name = tensor("op_6644_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6644_cast = slice_by_index(begin = var_6644_begin_0, end = var_6644_end_0, end_mask = var_6644_end_mask_0, x = transpose_9)[name = tensor("op_6644_cast")]; + tensor var_6648_begin_0 = const()[name = tensor("op_6648_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_6648_end_0 = const()[name = tensor("op_6648_end_0"), val = tensor([2, 2304, 1, 320])]; + tensor var_6648_end_mask_0 = const()[name = tensor("op_6648_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6648_cast = slice_by_index(begin = var_6648_begin_0, end = var_6648_end_0, end_mask = var_6648_end_mask_0, x = transpose_9)[name = tensor("op_6648_cast")]; + tensor var_6652_begin_0 = const()[name = tensor("op_6652_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_6652_end_0 = const()[name = tensor("op_6652_end_0"), val = tensor([2, 2304, 1, 400])]; + tensor var_6652_end_mask_0 = const()[name = tensor("op_6652_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6652_cast = slice_by_index(begin = var_6652_begin_0, end = var_6652_end_0, end_mask = var_6652_end_mask_0, x = transpose_9)[name = tensor("op_6652_cast")]; + tensor var_6656_begin_0 = const()[name = tensor("op_6656_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_6656_end_0 = const()[name = tensor("op_6656_end_0"), val = tensor([2, 2304, 1, 480])]; + tensor var_6656_end_mask_0 = const()[name = tensor("op_6656_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6656_cast = slice_by_index(begin = var_6656_begin_0, end = var_6656_end_0, end_mask = var_6656_end_mask_0, x = transpose_9)[name = tensor("op_6656_cast")]; + tensor var_6660_begin_0 = const()[name = tensor("op_6660_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_6660_end_0 = const()[name = tensor("op_6660_end_0"), val = tensor([2, 2304, 1, 560])]; + tensor var_6660_end_mask_0 = const()[name = tensor("op_6660_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6660_cast = slice_by_index(begin = var_6660_begin_0, end = var_6660_end_0, end_mask = var_6660_end_mask_0, x = transpose_9)[name = tensor("op_6660_cast")]; + tensor var_6664_begin_0 = const()[name = tensor("op_6664_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_6664_end_0 = const()[name = tensor("op_6664_end_0"), val = tensor([2, 2304, 1, 640])]; + tensor var_6664_end_mask_0 = const()[name = tensor("op_6664_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6664_cast = slice_by_index(begin = var_6664_begin_0, end = var_6664_end_0, end_mask = var_6664_end_mask_0, x = transpose_9)[name = tensor("op_6664_cast")]; + tensor var_6666_begin_0 = const()[name = tensor("op_6666_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6666_end_0 = const()[name = tensor("op_6666_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_6666_end_mask_0 = const()[name = tensor("op_6666_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6666_cast = slice_by_index(begin = var_6666_begin_0, end = var_6666_end_0, end_mask = var_6666_end_mask_0, x = v_45_cast)[name = tensor("op_6666_cast")]; + tensor var_6670_begin_0 = const()[name = tensor("op_6670_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6670_end_0 = const()[name = tensor("op_6670_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_6670_end_mask_0 = const()[name = tensor("op_6670_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6670_cast = slice_by_index(begin = var_6670_begin_0, end = var_6670_end_0, end_mask = var_6670_end_mask_0, x = v_45_cast)[name = tensor("op_6670_cast")]; + tensor var_6674_begin_0 = const()[name = tensor("op_6674_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6674_end_0 = const()[name = tensor("op_6674_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_6674_end_mask_0 = const()[name = tensor("op_6674_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6674_cast = slice_by_index(begin = var_6674_begin_0, end = var_6674_end_0, end_mask = var_6674_end_mask_0, x = v_45_cast)[name = tensor("op_6674_cast")]; + tensor var_6678_begin_0 = const()[name = tensor("op_6678_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6678_end_0 = const()[name = tensor("op_6678_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_6678_end_mask_0 = const()[name = tensor("op_6678_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6678_cast = slice_by_index(begin = var_6678_begin_0, end = var_6678_end_0, end_mask = var_6678_end_mask_0, x = v_45_cast)[name = tensor("op_6678_cast")]; + tensor var_6682_begin_0 = const()[name = tensor("op_6682_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6682_end_0 = const()[name = tensor("op_6682_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_6682_end_mask_0 = const()[name = tensor("op_6682_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6682_cast = slice_by_index(begin = var_6682_begin_0, end = var_6682_end_0, end_mask = var_6682_end_mask_0, x = v_45_cast)[name = tensor("op_6682_cast")]; + tensor var_6686_begin_0 = const()[name = tensor("op_6686_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6686_end_0 = const()[name = tensor("op_6686_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_6686_end_mask_0 = const()[name = tensor("op_6686_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6686_cast = slice_by_index(begin = var_6686_begin_0, end = var_6686_end_0, end_mask = var_6686_end_mask_0, x = v_45_cast)[name = tensor("op_6686_cast")]; + tensor var_6690_begin_0 = const()[name = tensor("op_6690_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6690_end_0 = const()[name = tensor("op_6690_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_6690_end_mask_0 = const()[name = tensor("op_6690_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6690_cast = slice_by_index(begin = var_6690_begin_0, end = var_6690_end_0, end_mask = var_6690_end_mask_0, x = v_45_cast)[name = tensor("op_6690_cast")]; + tensor var_6694_begin_0 = const()[name = tensor("op_6694_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6694_end_0 = const()[name = tensor("op_6694_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_6694_end_mask_0 = const()[name = tensor("op_6694_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6694_cast = slice_by_index(begin = var_6694_begin_0, end = var_6694_end_0, end_mask = var_6694_end_mask_0, x = v_45_cast)[name = tensor("op_6694_cast")]; + tensor var_6698_equation_0 = const()[name = tensor("op_6698_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6698_cast = einsum(equation = var_6698_equation_0, values = (var_6636_cast, var_6601_cast))[name = tensor("op_6698_cast")]; + tensor var_6699_to_fp16 = const()[name = tensor("op_6699_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_353_cast = mul(x = var_6698_cast, y = var_6699_to_fp16)[name = tensor("aw_353_cast")]; + tensor var_6702_equation_0 = const()[name = tensor("op_6702_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6702_cast = einsum(equation = var_6702_equation_0, values = (var_6640_cast, var_6605_cast))[name = tensor("op_6702_cast")]; + tensor var_6703_to_fp16 = const()[name = tensor("op_6703_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_355_cast = mul(x = var_6702_cast, y = var_6703_to_fp16)[name = tensor("aw_355_cast")]; + tensor var_6706_equation_0 = const()[name = tensor("op_6706_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6706_cast = einsum(equation = var_6706_equation_0, values = (var_6644_cast, var_6609_cast))[name = tensor("op_6706_cast")]; + tensor var_6707_to_fp16 = const()[name = tensor("op_6707_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_357_cast = mul(x = var_6706_cast, y = var_6707_to_fp16)[name = tensor("aw_357_cast")]; + tensor var_6710_equation_0 = const()[name = tensor("op_6710_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6710_cast = einsum(equation = var_6710_equation_0, values = (var_6648_cast, var_6613_cast))[name = tensor("op_6710_cast")]; + tensor var_6711_to_fp16 = const()[name = tensor("op_6711_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_359_cast = mul(x = var_6710_cast, y = var_6711_to_fp16)[name = tensor("aw_359_cast")]; + tensor var_6714_equation_0 = const()[name = tensor("op_6714_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6714_cast = einsum(equation = var_6714_equation_0, values = (var_6652_cast, var_6617_cast))[name = tensor("op_6714_cast")]; + tensor var_6715_to_fp16 = const()[name = tensor("op_6715_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_361_cast = mul(x = var_6714_cast, y = var_6715_to_fp16)[name = tensor("aw_361_cast")]; + tensor var_6718_equation_0 = const()[name = tensor("op_6718_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6718_cast = einsum(equation = var_6718_equation_0, values = (var_6656_cast, var_6621_cast))[name = tensor("op_6718_cast")]; + tensor var_6719_to_fp16 = const()[name = tensor("op_6719_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_363_cast = mul(x = var_6718_cast, y = var_6719_to_fp16)[name = tensor("aw_363_cast")]; + tensor var_6722_equation_0 = const()[name = tensor("op_6722_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6722_cast = einsum(equation = var_6722_equation_0, values = (var_6660_cast, var_6625_cast))[name = tensor("op_6722_cast")]; + tensor var_6723_to_fp16 = const()[name = tensor("op_6723_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_365_cast = mul(x = var_6722_cast, y = var_6723_to_fp16)[name = tensor("aw_365_cast")]; + tensor var_6726_equation_0 = const()[name = tensor("op_6726_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6726_cast = einsum(equation = var_6726_equation_0, values = (var_6664_cast, var_6629_cast))[name = tensor("op_6726_cast")]; + tensor var_6727_to_fp16 = const()[name = tensor("op_6727_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_367_cast = mul(x = var_6726_cast, y = var_6727_to_fp16)[name = tensor("aw_367_cast")]; + tensor var_6729_cast = softmax(axis = var_5930, x = aw_353_cast)[name = tensor("op_6729_cast")]; + tensor var_6730_cast = softmax(axis = var_5930, x = aw_355_cast)[name = tensor("op_6730_cast")]; + tensor var_6731_cast = softmax(axis = var_5930, x = aw_357_cast)[name = tensor("op_6731_cast")]; + tensor var_6732_cast = softmax(axis = var_5930, x = aw_359_cast)[name = tensor("op_6732_cast")]; + tensor var_6733_cast = softmax(axis = var_5930, x = aw_361_cast)[name = tensor("op_6733_cast")]; + tensor var_6734_cast = softmax(axis = var_5930, x = aw_363_cast)[name = tensor("op_6734_cast")]; + tensor var_6735_cast = softmax(axis = var_5930, x = aw_365_cast)[name = tensor("op_6735_cast")]; + tensor var_6736_cast = softmax(axis = var_5930, x = aw_367_cast)[name = tensor("op_6736_cast")]; + tensor var_6738_equation_0 = const()[name = tensor("op_6738_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6738_cast = einsum(equation = var_6738_equation_0, values = (var_6666_cast, var_6729_cast))[name = tensor("op_6738_cast")]; + tensor var_6740_equation_0 = const()[name = tensor("op_6740_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6740_cast = einsum(equation = var_6740_equation_0, values = (var_6670_cast, var_6730_cast))[name = tensor("op_6740_cast")]; + tensor var_6742_equation_0 = const()[name = tensor("op_6742_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6742_cast = einsum(equation = var_6742_equation_0, values = (var_6674_cast, var_6731_cast))[name = tensor("op_6742_cast")]; + tensor var_6744_equation_0 = const()[name = tensor("op_6744_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6744_cast = einsum(equation = var_6744_equation_0, values = (var_6678_cast, var_6732_cast))[name = tensor("op_6744_cast")]; + tensor var_6746_equation_0 = const()[name = tensor("op_6746_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6746_cast = einsum(equation = var_6746_equation_0, values = (var_6682_cast, var_6733_cast))[name = tensor("op_6746_cast")]; + tensor var_6748_equation_0 = const()[name = tensor("op_6748_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6748_cast = einsum(equation = var_6748_equation_0, values = (var_6686_cast, var_6734_cast))[name = tensor("op_6748_cast")]; + tensor var_6750_equation_0 = const()[name = tensor("op_6750_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6750_cast = einsum(equation = var_6750_equation_0, values = (var_6690_cast, var_6735_cast))[name = tensor("op_6750_cast")]; + tensor var_6752_equation_0 = const()[name = tensor("op_6752_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6752_cast = einsum(equation = var_6752_equation_0, values = (var_6694_cast, var_6736_cast))[name = tensor("op_6752_cast")]; + tensor input_409_interleave_0 = const()[name = tensor("input_409_interleave_0"), val = tensor(false)]; + tensor input_409_cast = concat(axis = var_5930, interleave = input_409_interleave_0, values = (var_6738_cast, var_6740_cast, var_6742_cast, var_6744_cast, var_6746_cast, var_6748_cast, var_6750_cast, var_6752_cast))[name = tensor("input_409_cast")]; + tensor var_6758 = const()[name = tensor("op_6758"), val = tensor([1, 1])]; + tensor var_6760 = const()[name = tensor("op_6760"), val = tensor([1, 1])]; + tensor var_6762_pad_type_0 = const()[name = tensor("op_6762_pad_type_0"), val = tensor("custom")]; + tensor var_6762_pad_0 = const()[name = tensor("op_6762_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607657984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607965248))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607965440)))]; + tensor var_6762_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_6760, groups = var_5930, pad = var_6762_pad_0, pad_type = var_6762_pad_type_0, strides = var_6758, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_409_cast)[name = tensor("op_6762_cast")]; + tensor inputs_69_cast = add(x = var_6762_cast, y = inputs_67_cast)[name = tensor("inputs_69_cast")]; + tensor var_6766 = const()[name = tensor("op_6766"), val = tensor([1])]; + tensor channels_mean_69_cast = reduce_mean(axes = var_6766, keep_dims = var_5925, x = inputs_69_cast)[name = tensor("channels_mean_69_cast")]; + tensor zero_mean_69_cast = sub(x = inputs_69_cast, y = channels_mean_69_cast)[name = tensor("zero_mean_69_cast")]; + tensor zero_mean_sq_69_cast = mul(x = zero_mean_69_cast, y = zero_mean_69_cast)[name = tensor("zero_mean_sq_69_cast")]; + tensor var_6770 = const()[name = tensor("op_6770"), val = tensor([1])]; + tensor var_6771_cast = reduce_mean(axes = var_6770, keep_dims = var_5925, x = zero_mean_sq_69_cast)[name = tensor("op_6771_cast")]; + tensor var_6772_to_fp16 = const()[name = tensor("op_6772_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6773_cast = add(x = var_6771_cast, y = var_6772_to_fp16)[name = tensor("op_6773_cast")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_6773_cast)[name = tensor("denom_69_cast")]; + tensor out_69_cast = mul(x = zero_mean_69_cast, y = denom_69_cast)[name = tensor("out_69_cast")]; + tensor var_6777_to_fp16 = const()[name = tensor("op_6777_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607966784)))]; + tensor var_6778_cast = add(x = out_69_cast, y = var_6777_to_fp16)[name = tensor("op_6778_cast")]; + tensor var_6780_to_fp16 = const()[name = tensor("op_6780_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607968128)))]; + tensor hidden_states_249_cast = mul(x = var_6778_cast, y = var_6780_to_fp16)[name = tensor("hidden_states_249_cast")]; + tensor var_6787 = const()[name = tensor("op_6787"), val = tensor([1, 1])]; + tensor var_6789 = const()[name = tensor("op_6789"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607969472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608276736))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_47_cast = conv(dilations = var_6789, groups = var_5930, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_6787, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_249_cast)[name = tensor("q_47_cast")]; + tensor var_6793 = const()[name = tensor("op_6793"), val = tensor([1, 1])]; + tensor var_6795 = const()[name = tensor("op_6795"), val = tensor([1, 1])]; + tensor k_93_pad_type_0 = const()[name = tensor("k_93_pad_type_0"), val = tensor("custom")]; + tensor k_93_pad_0 = const()[name = tensor("k_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608276928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608645632))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_93_cast = conv(dilations = var_6795, groups = var_5930, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_6793, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_93_cast")]; + tensor var_6799 = const()[name = tensor("op_6799"), val = tensor([1, 1])]; + tensor var_6801 = const()[name = tensor("op_6801"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608645824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609014528))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_47_cast = conv(dilations = var_6801, groups = var_5930, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_6799, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_47_cast")]; + tensor var_6805_begin_0 = const()[name = tensor("op_6805_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6805_end_0 = const()[name = tensor("op_6805_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_6805_end_mask_0 = const()[name = tensor("op_6805_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6805_cast = slice_by_index(begin = var_6805_begin_0, end = var_6805_end_0, end_mask = var_6805_end_mask_0, x = q_47_cast)[name = tensor("op_6805_cast")]; + tensor var_6809_begin_0 = const()[name = tensor("op_6809_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6809_end_0 = const()[name = tensor("op_6809_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_6809_end_mask_0 = const()[name = tensor("op_6809_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6809_cast = slice_by_index(begin = var_6809_begin_0, end = var_6809_end_0, end_mask = var_6809_end_mask_0, x = q_47_cast)[name = tensor("op_6809_cast")]; + tensor var_6813_begin_0 = const()[name = tensor("op_6813_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6813_end_0 = const()[name = tensor("op_6813_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_6813_end_mask_0 = const()[name = tensor("op_6813_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6813_cast = slice_by_index(begin = var_6813_begin_0, end = var_6813_end_0, end_mask = var_6813_end_mask_0, x = q_47_cast)[name = tensor("op_6813_cast")]; + tensor var_6817_begin_0 = const()[name = tensor("op_6817_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6817_end_0 = const()[name = tensor("op_6817_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_6817_end_mask_0 = const()[name = tensor("op_6817_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6817_cast = slice_by_index(begin = var_6817_begin_0, end = var_6817_end_0, end_mask = var_6817_end_mask_0, x = q_47_cast)[name = tensor("op_6817_cast")]; + tensor var_6821_begin_0 = const()[name = tensor("op_6821_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6821_end_0 = const()[name = tensor("op_6821_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_6821_end_mask_0 = const()[name = tensor("op_6821_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6821_cast = slice_by_index(begin = var_6821_begin_0, end = var_6821_end_0, end_mask = var_6821_end_mask_0, x = q_47_cast)[name = tensor("op_6821_cast")]; + tensor var_6825_begin_0 = const()[name = tensor("op_6825_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6825_end_0 = const()[name = tensor("op_6825_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_6825_end_mask_0 = const()[name = tensor("op_6825_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6825_cast = slice_by_index(begin = var_6825_begin_0, end = var_6825_end_0, end_mask = var_6825_end_mask_0, x = q_47_cast)[name = tensor("op_6825_cast")]; + tensor var_6829_begin_0 = const()[name = tensor("op_6829_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6829_end_0 = const()[name = tensor("op_6829_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_6829_end_mask_0 = const()[name = tensor("op_6829_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6829_cast = slice_by_index(begin = var_6829_begin_0, end = var_6829_end_0, end_mask = var_6829_end_mask_0, x = q_47_cast)[name = tensor("op_6829_cast")]; + tensor var_6833_begin_0 = const()[name = tensor("op_6833_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6833_end_0 = const()[name = tensor("op_6833_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_6833_end_mask_0 = const()[name = tensor("op_6833_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6833_cast = slice_by_index(begin = var_6833_begin_0, end = var_6833_end_0, end_mask = var_6833_end_mask_0, x = q_47_cast)[name = tensor("op_6833_cast")]; + tensor k_95_perm_0 = const()[name = tensor("k_95_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_6840_begin_0 = const()[name = tensor("op_6840_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6840_end_0 = const()[name = tensor("op_6840_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_6840_end_mask_0 = const()[name = tensor("op_6840_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_8 = transpose(perm = k_95_perm_0, x = k_93_cast)[name = tensor("transpose_8")]; + tensor var_6840_cast = slice_by_index(begin = var_6840_begin_0, end = var_6840_end_0, end_mask = var_6840_end_mask_0, x = transpose_8)[name = tensor("op_6840_cast")]; + tensor var_6844_begin_0 = const()[name = tensor("op_6844_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_6844_end_0 = const()[name = tensor("op_6844_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_6844_end_mask_0 = const()[name = tensor("op_6844_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6844_cast = slice_by_index(begin = var_6844_begin_0, end = var_6844_end_0, end_mask = var_6844_end_mask_0, x = transpose_8)[name = tensor("op_6844_cast")]; + tensor var_6848_begin_0 = const()[name = tensor("op_6848_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_6848_end_0 = const()[name = tensor("op_6848_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_6848_end_mask_0 = const()[name = tensor("op_6848_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6848_cast = slice_by_index(begin = var_6848_begin_0, end = var_6848_end_0, end_mask = var_6848_end_mask_0, x = transpose_8)[name = tensor("op_6848_cast")]; + tensor var_6852_begin_0 = const()[name = tensor("op_6852_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_6852_end_0 = const()[name = tensor("op_6852_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_6852_end_mask_0 = const()[name = tensor("op_6852_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6852_cast = slice_by_index(begin = var_6852_begin_0, end = var_6852_end_0, end_mask = var_6852_end_mask_0, x = transpose_8)[name = tensor("op_6852_cast")]; + tensor var_6856_begin_0 = const()[name = tensor("op_6856_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_6856_end_0 = const()[name = tensor("op_6856_end_0"), val = tensor([2, 77, 1, 400])]; + tensor var_6856_end_mask_0 = const()[name = tensor("op_6856_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6856_cast = slice_by_index(begin = var_6856_begin_0, end = var_6856_end_0, end_mask = var_6856_end_mask_0, x = transpose_8)[name = tensor("op_6856_cast")]; + tensor var_6860_begin_0 = const()[name = tensor("op_6860_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_6860_end_0 = const()[name = tensor("op_6860_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_6860_end_mask_0 = const()[name = tensor("op_6860_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6860_cast = slice_by_index(begin = var_6860_begin_0, end = var_6860_end_0, end_mask = var_6860_end_mask_0, x = transpose_8)[name = tensor("op_6860_cast")]; + tensor var_6864_begin_0 = const()[name = tensor("op_6864_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_6864_end_0 = const()[name = tensor("op_6864_end_0"), val = tensor([2, 77, 1, 560])]; + tensor var_6864_end_mask_0 = const()[name = tensor("op_6864_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6864_cast = slice_by_index(begin = var_6864_begin_0, end = var_6864_end_0, end_mask = var_6864_end_mask_0, x = transpose_8)[name = tensor("op_6864_cast")]; + tensor var_6868_begin_0 = const()[name = tensor("op_6868_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_6868_end_0 = const()[name = tensor("op_6868_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_6868_end_mask_0 = const()[name = tensor("op_6868_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_6868_cast = slice_by_index(begin = var_6868_begin_0, end = var_6868_end_0, end_mask = var_6868_end_mask_0, x = transpose_8)[name = tensor("op_6868_cast")]; + tensor var_6870_begin_0 = const()[name = tensor("op_6870_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_6870_end_0 = const()[name = tensor("op_6870_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_6870_end_mask_0 = const()[name = tensor("op_6870_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6870_cast = slice_by_index(begin = var_6870_begin_0, end = var_6870_end_0, end_mask = var_6870_end_mask_0, x = v_47_cast)[name = tensor("op_6870_cast")]; + tensor var_6874_begin_0 = const()[name = tensor("op_6874_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_6874_end_0 = const()[name = tensor("op_6874_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_6874_end_mask_0 = const()[name = tensor("op_6874_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6874_cast = slice_by_index(begin = var_6874_begin_0, end = var_6874_end_0, end_mask = var_6874_end_mask_0, x = v_47_cast)[name = tensor("op_6874_cast")]; + tensor var_6878_begin_0 = const()[name = tensor("op_6878_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_6878_end_0 = const()[name = tensor("op_6878_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_6878_end_mask_0 = const()[name = tensor("op_6878_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6878_cast = slice_by_index(begin = var_6878_begin_0, end = var_6878_end_0, end_mask = var_6878_end_mask_0, x = v_47_cast)[name = tensor("op_6878_cast")]; + tensor var_6882_begin_0 = const()[name = tensor("op_6882_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_6882_end_0 = const()[name = tensor("op_6882_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_6882_end_mask_0 = const()[name = tensor("op_6882_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6882_cast = slice_by_index(begin = var_6882_begin_0, end = var_6882_end_0, end_mask = var_6882_end_mask_0, x = v_47_cast)[name = tensor("op_6882_cast")]; + tensor var_6886_begin_0 = const()[name = tensor("op_6886_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_6886_end_0 = const()[name = tensor("op_6886_end_0"), val = tensor([2, 400, 1, 77])]; + tensor var_6886_end_mask_0 = const()[name = tensor("op_6886_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6886_cast = slice_by_index(begin = var_6886_begin_0, end = var_6886_end_0, end_mask = var_6886_end_mask_0, x = v_47_cast)[name = tensor("op_6886_cast")]; + tensor var_6890_begin_0 = const()[name = tensor("op_6890_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_6890_end_0 = const()[name = tensor("op_6890_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_6890_end_mask_0 = const()[name = tensor("op_6890_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6890_cast = slice_by_index(begin = var_6890_begin_0, end = var_6890_end_0, end_mask = var_6890_end_mask_0, x = v_47_cast)[name = tensor("op_6890_cast")]; + tensor var_6894_begin_0 = const()[name = tensor("op_6894_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_6894_end_0 = const()[name = tensor("op_6894_end_0"), val = tensor([2, 560, 1, 77])]; + tensor var_6894_end_mask_0 = const()[name = tensor("op_6894_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6894_cast = slice_by_index(begin = var_6894_begin_0, end = var_6894_end_0, end_mask = var_6894_end_mask_0, x = v_47_cast)[name = tensor("op_6894_cast")]; + tensor var_6898_begin_0 = const()[name = tensor("op_6898_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_6898_end_0 = const()[name = tensor("op_6898_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_6898_end_mask_0 = const()[name = tensor("op_6898_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_6898_cast = slice_by_index(begin = var_6898_begin_0, end = var_6898_end_0, end_mask = var_6898_end_mask_0, x = v_47_cast)[name = tensor("op_6898_cast")]; + tensor var_6902_equation_0 = const()[name = tensor("op_6902_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6902_cast = einsum(equation = var_6902_equation_0, values = (var_6840_cast, var_6805_cast))[name = tensor("op_6902_cast")]; + tensor var_6903_to_fp16 = const()[name = tensor("op_6903_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_369_cast = mul(x = var_6902_cast, y = var_6903_to_fp16)[name = tensor("aw_369_cast")]; + tensor var_6906_equation_0 = const()[name = tensor("op_6906_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6906_cast = einsum(equation = var_6906_equation_0, values = (var_6844_cast, var_6809_cast))[name = tensor("op_6906_cast")]; + tensor var_6907_to_fp16 = const()[name = tensor("op_6907_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_371_cast = mul(x = var_6906_cast, y = var_6907_to_fp16)[name = tensor("aw_371_cast")]; + tensor var_6910_equation_0 = const()[name = tensor("op_6910_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6910_cast = einsum(equation = var_6910_equation_0, values = (var_6848_cast, var_6813_cast))[name = tensor("op_6910_cast")]; + tensor var_6911_to_fp16 = const()[name = tensor("op_6911_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_373_cast = mul(x = var_6910_cast, y = var_6911_to_fp16)[name = tensor("aw_373_cast")]; + tensor var_6914_equation_0 = const()[name = tensor("op_6914_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6914_cast = einsum(equation = var_6914_equation_0, values = (var_6852_cast, var_6817_cast))[name = tensor("op_6914_cast")]; + tensor var_6915_to_fp16 = const()[name = tensor("op_6915_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_375_cast = mul(x = var_6914_cast, y = var_6915_to_fp16)[name = tensor("aw_375_cast")]; + tensor var_6918_equation_0 = const()[name = tensor("op_6918_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6918_cast = einsum(equation = var_6918_equation_0, values = (var_6856_cast, var_6821_cast))[name = tensor("op_6918_cast")]; + tensor var_6919_to_fp16 = const()[name = tensor("op_6919_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_377_cast = mul(x = var_6918_cast, y = var_6919_to_fp16)[name = tensor("aw_377_cast")]; + tensor var_6922_equation_0 = const()[name = tensor("op_6922_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6922_cast = einsum(equation = var_6922_equation_0, values = (var_6860_cast, var_6825_cast))[name = tensor("op_6922_cast")]; + tensor var_6923_to_fp16 = const()[name = tensor("op_6923_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_379_cast = mul(x = var_6922_cast, y = var_6923_to_fp16)[name = tensor("aw_379_cast")]; + tensor var_6926_equation_0 = const()[name = tensor("op_6926_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6926_cast = einsum(equation = var_6926_equation_0, values = (var_6864_cast, var_6829_cast))[name = tensor("op_6926_cast")]; + tensor var_6927_to_fp16 = const()[name = tensor("op_6927_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_381_cast = mul(x = var_6926_cast, y = var_6927_to_fp16)[name = tensor("aw_381_cast")]; + tensor var_6930_equation_0 = const()[name = tensor("op_6930_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_6930_cast = einsum(equation = var_6930_equation_0, values = (var_6868_cast, var_6833_cast))[name = tensor("op_6930_cast")]; + tensor var_6931_to_fp16 = const()[name = tensor("op_6931_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_383_cast = mul(x = var_6930_cast, y = var_6931_to_fp16)[name = tensor("aw_383_cast")]; + tensor var_6933_cast = softmax(axis = var_5930, x = aw_369_cast)[name = tensor("op_6933_cast")]; + tensor var_6934_cast = softmax(axis = var_5930, x = aw_371_cast)[name = tensor("op_6934_cast")]; + tensor var_6935_cast = softmax(axis = var_5930, x = aw_373_cast)[name = tensor("op_6935_cast")]; + tensor var_6936_cast = softmax(axis = var_5930, x = aw_375_cast)[name = tensor("op_6936_cast")]; + tensor var_6937_cast = softmax(axis = var_5930, x = aw_377_cast)[name = tensor("op_6937_cast")]; + tensor var_6938_cast = softmax(axis = var_5930, x = aw_379_cast)[name = tensor("op_6938_cast")]; + tensor var_6939_cast = softmax(axis = var_5930, x = aw_381_cast)[name = tensor("op_6939_cast")]; + tensor var_6940_cast = softmax(axis = var_5930, x = aw_383_cast)[name = tensor("op_6940_cast")]; + tensor var_6942_equation_0 = const()[name = tensor("op_6942_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6942_cast = einsum(equation = var_6942_equation_0, values = (var_6870_cast, var_6933_cast))[name = tensor("op_6942_cast")]; + tensor var_6944_equation_0 = const()[name = tensor("op_6944_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6944_cast = einsum(equation = var_6944_equation_0, values = (var_6874_cast, var_6934_cast))[name = tensor("op_6944_cast")]; + tensor var_6946_equation_0 = const()[name = tensor("op_6946_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6946_cast = einsum(equation = var_6946_equation_0, values = (var_6878_cast, var_6935_cast))[name = tensor("op_6946_cast")]; + tensor var_6948_equation_0 = const()[name = tensor("op_6948_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6948_cast = einsum(equation = var_6948_equation_0, values = (var_6882_cast, var_6936_cast))[name = tensor("op_6948_cast")]; + tensor var_6950_equation_0 = const()[name = tensor("op_6950_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6950_cast = einsum(equation = var_6950_equation_0, values = (var_6886_cast, var_6937_cast))[name = tensor("op_6950_cast")]; + tensor var_6952_equation_0 = const()[name = tensor("op_6952_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6952_cast = einsum(equation = var_6952_equation_0, values = (var_6890_cast, var_6938_cast))[name = tensor("op_6952_cast")]; + tensor var_6954_equation_0 = const()[name = tensor("op_6954_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6954_cast = einsum(equation = var_6954_equation_0, values = (var_6894_cast, var_6939_cast))[name = tensor("op_6954_cast")]; + tensor var_6956_equation_0 = const()[name = tensor("op_6956_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_6956_cast = einsum(equation = var_6956_equation_0, values = (var_6898_cast, var_6940_cast))[name = tensor("op_6956_cast")]; + tensor input_411_interleave_0 = const()[name = tensor("input_411_interleave_0"), val = tensor(false)]; + tensor input_411_cast = concat(axis = var_5930, interleave = input_411_interleave_0, values = (var_6942_cast, var_6944_cast, var_6946_cast, var_6948_cast, var_6950_cast, var_6952_cast, var_6954_cast, var_6956_cast))[name = tensor("input_411_cast")]; + tensor var_6962 = const()[name = tensor("op_6962"), val = tensor([1, 1])]; + tensor var_6964 = const()[name = tensor("op_6964"), val = tensor([1, 1])]; + tensor var_6966_pad_type_0 = const()[name = tensor("op_6966_pad_type_0"), val = tensor("custom")]; + tensor var_6966_pad_0 = const()[name = tensor("op_6966_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609014720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609321984))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609322176)))]; + tensor var_6966_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_6964, groups = var_5930, pad = var_6966_pad_0, pad_type = var_6966_pad_type_0, strides = var_6962, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_411_cast)[name = tensor("op_6966_cast")]; + tensor inputs_71_cast = add(x = var_6966_cast, y = inputs_69_cast)[name = tensor("inputs_71_cast")]; + tensor var_6970 = const()[name = tensor("op_6970"), val = tensor([1])]; + tensor channels_mean_71_cast = reduce_mean(axes = var_6970, keep_dims = var_5925, x = inputs_71_cast)[name = tensor("channels_mean_71_cast")]; + tensor zero_mean_71_cast = sub(x = inputs_71_cast, y = channels_mean_71_cast)[name = tensor("zero_mean_71_cast")]; + tensor zero_mean_sq_71_cast = mul(x = zero_mean_71_cast, y = zero_mean_71_cast)[name = tensor("zero_mean_sq_71_cast")]; + tensor var_6974 = const()[name = tensor("op_6974"), val = tensor([1])]; + tensor var_6975_cast = reduce_mean(axes = var_6974, keep_dims = var_5925, x = zero_mean_sq_71_cast)[name = tensor("op_6975_cast")]; + tensor var_6976_to_fp16 = const()[name = tensor("op_6976_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6977_cast = add(x = var_6975_cast, y = var_6976_to_fp16)[name = tensor("op_6977_cast")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_6977_cast)[name = tensor("denom_71_cast")]; + tensor out_71_cast = mul(x = zero_mean_71_cast, y = denom_71_cast)[name = tensor("out_71_cast")]; + tensor var_6981_to_fp16 = const()[name = tensor("op_6981_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609323520)))]; + tensor var_6982_cast = add(x = out_71_cast, y = var_6981_to_fp16)[name = tensor("op_6982_cast")]; + tensor var_6984_to_fp16 = const()[name = tensor("op_6984_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609324864)))]; + tensor input_413_cast = mul(x = var_6982_cast, y = var_6984_to_fp16)[name = tensor("input_413_cast")]; + tensor var_6992 = const()[name = tensor("op_6992"), val = tensor([1, 1])]; + tensor var_6994 = const()[name = tensor("op_6994"), val = tensor([1, 1])]; + tensor var_6996_pad_type_0 = const()[name = tensor("op_6996_pad_type_0"), val = tensor("custom")]; + tensor var_6996_pad_0 = const()[name = tensor("op_6996_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609326208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611783872))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611784064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611787968))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_6996_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6994, groups = var_5930, pad = var_6996_pad_0, pad_type = var_6996_pad_type_0, strides = var_6992, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_413_cast)[name = tensor("op_6996_cast")]; + tensor var_6997_split_sizes_0 = const()[name = tensor("op_6997_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_6997_axis_0 = const()[name = tensor("op_6997_axis_0"), val = tensor(1)]; + tensor var_6997_cast_0, tensor var_6997_cast_1 = split(axis = var_6997_axis_0, split_sizes = var_6997_split_sizes_0, x = var_6996_cast)[name = tensor("op_6997_cast")]; + tensor var_6999_mode_0 = const()[name = tensor("op_6999_mode_0"), val = tensor("EXACT")]; + tensor var_6999_cast = gelu(mode = var_6999_mode_0, x = var_6997_cast_1)[name = tensor("op_6999_cast")]; + tensor input_415_cast = mul(x = var_6997_cast_0, y = var_6999_cast)[name = tensor("input_415_cast")]; + tensor var_7003 = const()[name = tensor("op_7003"), val = tensor([1, 1])]; + tensor var_7005 = const()[name = tensor("op_7005"), val = tensor([1, 1])]; + tensor var_7007_pad_type_0 = const()[name = tensor("op_7007_pad_type_0"), val = tensor("custom")]; + tensor var_7007_pad_0 = const()[name = tensor("op_7007_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611788160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613017024))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613017216)))]; + tensor var_7007_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7005, groups = var_5930, pad = var_7007_pad_0, pad_type = var_7007_pad_type_0, strides = var_7003, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_415_cast)[name = tensor("op_7007_cast")]; + tensor hidden_states_253_cast = add(x = var_7007_cast, y = inputs_71_cast)[name = tensor("hidden_states_253_cast")]; + tensor var_7009 = const()[name = tensor("op_7009"), val = tensor([2, 640, 48, 48])]; + tensor input_417_cast = reshape(shape = var_7009, x = hidden_states_253_cast)[name = tensor("input_417_cast")]; + tensor var_7013 = const()[name = tensor("op_7013"), val = tensor([1, 1])]; + tensor var_7015 = const()[name = tensor("op_7015"), val = tensor([1, 1])]; + tensor hidden_states_255_pad_type_0 = const()[name = tensor("hidden_states_255_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_255_pad_0 = const()[name = tensor("hidden_states_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613018560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613325824))), name = tensor("up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613326016)))]; + tensor hidden_states_255_cast = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_7015, groups = var_5930, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_7013, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_417_cast)[name = tensor("hidden_states_255_cast")]; + tensor hidden_states_257_cast = add(x = hidden_states_255_cast, y = hidden_states_243_cast)[name = tensor("hidden_states_257_cast")]; + tensor input_419_interleave_0 = const()[name = tensor("input_419_interleave_0"), val = tensor(false)]; + tensor input_419_cast = concat(axis = var_5930, interleave = input_419_interleave_0, values = (hidden_states_257_cast, input_63_cast))[name = tensor("input_419_cast")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([2, 32, 30, 48, 48])]; + tensor reshape_192_cast = reshape(shape = reshape_192_shape_0, x = input_419_cast)[name = tensor("reshape_192_cast")]; + tensor reduce_mean_144_axes_0 = const()[name = tensor("reduce_mean_144_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_144_keep_dims_0 = const()[name = tensor("reduce_mean_144_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_144_cast")]; + tensor sub_96_cast = sub(x = reshape_192_cast, y = reduce_mean_144_cast)[name = tensor("sub_96_cast")]; + tensor square_48_cast = square(x = sub_96_cast)[name = tensor("square_48_cast")]; + tensor reduce_mean_146_axes_0 = const()[name = tensor("reduce_mean_146_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_146_keep_dims_0 = const()[name = tensor("reduce_mean_146_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_146_cast")]; + tensor add_96_y_0_to_fp16 = const()[name = tensor("add_96_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_96_cast = add(x = reduce_mean_146_cast, y = add_96_y_0_to_fp16)[name = tensor("add_96_cast")]; + tensor sqrt_48_cast = sqrt(x = add_96_cast)[name = tensor("sqrt_48_cast")]; + tensor real_div_48_cast = real_div(x = sub_96_cast, y = sqrt_48_cast)[name = tensor("real_div_48_cast")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([2, 960, 48, 48])]; + tensor reshape_193_cast = reshape(shape = reshape_193_shape_0, x = real_div_48_cast)[name = tensor("reshape_193_cast")]; + tensor add_97_mean_0_to_fp16 = const()[name = tensor("add_97_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613327360)))]; + tensor add_97_variance_0_to_fp16 = const()[name = tensor("add_97_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613329344)))]; + tensor add_97_gamma_0_to_fp16 = const()[name = tensor("add_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613331328)))]; + tensor add_97_beta_0_to_fp16 = const()[name = tensor("add_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613333312)))]; + tensor add_97_epsilon_0_to_fp16 = const()[name = tensor("add_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_97_cast")]; + tensor input_423_cast = silu(x = add_97_cast)[name = tensor("input_423_cast")]; + tensor var_7033 = const()[name = tensor("op_7033"), val = tensor([1, 1])]; + tensor var_7035 = const()[name = tensor("op_7035"), val = tensor([1, 1])]; + tensor hidden_states_259_pad_type_0 = const()[name = tensor("hidden_states_259_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_259_pad_0 = const()[name = tensor("hidden_states_259_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613335296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617482560))), name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([640, 960, 3, 3])]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617482752)))]; + tensor hidden_states_259_cast = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_7035, groups = var_5930, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_7033, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized, x = input_423_cast)[name = tensor("hidden_states_259_cast")]; + tensor var_7041 = const()[name = tensor("op_7041"), val = tensor([1, 1])]; + tensor var_7043 = const()[name = tensor("op_7043"), val = tensor([1, 1])]; + tensor temb_37_pad_type_0 = const()[name = tensor("temb_37_pad_type_0"), val = tensor("custom")]; + tensor temb_37_pad_0 = const()[name = tensor("temb_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617484096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618098560))), name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618098752)))]; + tensor temb_37_cast = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_7043, groups = var_5930, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_7041, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_37_cast")]; + tensor input_427_cast = add(x = hidden_states_259_cast, y = temb_37_cast)[name = tensor("input_427_cast")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_196_cast = reshape(shape = reshape_196_shape_0, x = input_427_cast)[name = tensor("reshape_196_cast")]; + tensor reduce_mean_147_axes_0 = const()[name = tensor("reduce_mean_147_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_147_keep_dims_0 = const()[name = tensor("reduce_mean_147_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_147_cast")]; + tensor sub_98_cast = sub(x = reshape_196_cast, y = reduce_mean_147_cast)[name = tensor("sub_98_cast")]; + tensor square_49_cast = square(x = sub_98_cast)[name = tensor("square_49_cast")]; + tensor reduce_mean_149_axes_0 = const()[name = tensor("reduce_mean_149_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_149_keep_dims_0 = const()[name = tensor("reduce_mean_149_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_149_cast")]; + tensor add_98_y_0_to_fp16 = const()[name = tensor("add_98_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_98_cast = add(x = reduce_mean_149_cast, y = add_98_y_0_to_fp16)[name = tensor("add_98_cast")]; + tensor sqrt_49_cast = sqrt(x = add_98_cast)[name = tensor("sqrt_49_cast")]; + tensor real_div_49_cast = real_div(x = sub_98_cast, y = sqrt_49_cast)[name = tensor("real_div_49_cast")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_197_cast = reshape(shape = reshape_197_shape_0, x = real_div_49_cast)[name = tensor("reshape_197_cast")]; + tensor add_99_gamma_0_to_fp16 = const()[name = tensor("add_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618100096)))]; + tensor add_99_beta_0_to_fp16 = const()[name = tensor("add_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618101440)))]; + tensor add_99_epsilon_0_to_fp16 = const()[name = tensor("add_99_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_99_cast")]; + tensor input_431_cast = silu(x = add_99_cast)[name = tensor("input_431_cast")]; + tensor var_7053 = const()[name = tensor("op_7053"), val = tensor([1, 1])]; + tensor var_7055 = const()[name = tensor("op_7055"), val = tensor([1, 1])]; + tensor hidden_states_261_pad_type_0 = const()[name = tensor("hidden_states_261_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_261_pad_0 = const()[name = tensor("hidden_states_261_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618102784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620867648))), name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620867840)))]; + tensor hidden_states_261_cast = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_7055, groups = var_5930, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_7053, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized, x = input_431_cast)[name = tensor("hidden_states_261_cast")]; + tensor var_7060 = const()[name = tensor("op_7060"), val = tensor([1, 1])]; + tensor var_7062 = const()[name = tensor("op_7062"), val = tensor([1, 1])]; + tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("custom")]; + tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620869184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621330048))), name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 960, 1, 1])]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621330240)))]; + tensor x_21_cast = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_7062, groups = var_5930, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_7060, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_419_cast)[name = tensor("x_21_cast")]; + tensor hidden_states_263_cast = add(x = x_21_cast, y = hidden_states_261_cast)[name = tensor("hidden_states_263_cast")]; + tensor reshape_200_shape_0 = const()[name = tensor("reshape_200_shape_0"), val = tensor([2, 32, 20, 48, 48])]; + tensor reshape_200_cast = reshape(shape = reshape_200_shape_0, x = hidden_states_263_cast)[name = tensor("reshape_200_cast")]; + tensor reduce_mean_150_axes_0 = const()[name = tensor("reduce_mean_150_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_150_keep_dims_0 = const()[name = tensor("reduce_mean_150_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_150_cast")]; + tensor sub_100_cast = sub(x = reshape_200_cast, y = reduce_mean_150_cast)[name = tensor("sub_100_cast")]; + tensor square_50_cast = square(x = sub_100_cast)[name = tensor("square_50_cast")]; + tensor reduce_mean_152_axes_0 = const()[name = tensor("reduce_mean_152_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_152_keep_dims_0 = const()[name = tensor("reduce_mean_152_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_152_cast")]; + tensor add_100_y_0_to_fp16 = const()[name = tensor("add_100_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_100_cast = add(x = reduce_mean_152_cast, y = add_100_y_0_to_fp16)[name = tensor("add_100_cast")]; + tensor sqrt_50_cast = sqrt(x = add_100_cast)[name = tensor("sqrt_50_cast")]; + tensor real_div_50_cast = real_div(x = sub_100_cast, y = sqrt_50_cast)[name = tensor("real_div_50_cast")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([2, 640, 48, 48])]; + tensor reshape_201_cast = reshape(shape = reshape_201_shape_0, x = real_div_50_cast)[name = tensor("reshape_201_cast")]; + tensor add_101_gamma_0_to_fp16 = const()[name = tensor("add_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621331584)))]; + tensor add_101_beta_0_to_fp16 = const()[name = tensor("add_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621332928)))]; + tensor add_101_epsilon_0_to_fp16 = const()[name = tensor("add_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_101_cast")]; + tensor var_7082 = const()[name = tensor("op_7082"), val = tensor([1, 1])]; + tensor var_7084 = const()[name = tensor("op_7084"), val = tensor([1, 1])]; + tensor hidden_states_265_pad_type_0 = const()[name = tensor("hidden_states_265_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_265_pad_0 = const()[name = tensor("hidden_states_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621334272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621641536))), name = tensor("up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621641728)))]; + tensor hidden_states_265_cast = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_7084, groups = var_5930, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_7082, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized, x = add_101_cast)[name = tensor("hidden_states_265_cast")]; + tensor var_7089 = const()[name = tensor("op_7089"), val = tensor([2, 640, 1, 2304])]; + tensor inputs_73_cast = reshape(shape = var_7089, x = hidden_states_265_cast)[name = tensor("inputs_73_cast")]; + tensor var_7099 = const()[name = tensor("op_7099"), val = tensor([1])]; + tensor channels_mean_73_cast = reduce_mean(axes = var_7099, keep_dims = var_5925, x = inputs_73_cast)[name = tensor("channels_mean_73_cast")]; + tensor zero_mean_73_cast = sub(x = inputs_73_cast, y = channels_mean_73_cast)[name = tensor("zero_mean_73_cast")]; + tensor zero_mean_sq_73_cast = mul(x = zero_mean_73_cast, y = zero_mean_73_cast)[name = tensor("zero_mean_sq_73_cast")]; + tensor var_7103 = const()[name = tensor("op_7103"), val = tensor([1])]; + tensor var_7104_cast = reduce_mean(axes = var_7103, keep_dims = var_5925, x = zero_mean_sq_73_cast)[name = tensor("op_7104_cast")]; + tensor var_7105_to_fp16 = const()[name = tensor("op_7105_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7106_cast = add(x = var_7104_cast, y = var_7105_to_fp16)[name = tensor("op_7106_cast")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_7106_cast)[name = tensor("denom_73_cast")]; + tensor out_73_cast = mul(x = zero_mean_73_cast, y = denom_73_cast)[name = tensor("out_73_cast")]; + tensor var_7110_to_fp16 = const()[name = tensor("op_7110_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621643072)))]; + tensor var_7111_cast = add(x = out_73_cast, y = var_7110_to_fp16)[name = tensor("op_7111_cast")]; + tensor var_7113_to_fp16 = const()[name = tensor("op_7113_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621644416)))]; + tensor hidden_states_267_cast = mul(x = var_7111_cast, y = var_7113_to_fp16)[name = tensor("hidden_states_267_cast")]; + tensor var_7120 = const()[name = tensor("op_7120"), val = tensor([1, 1])]; + tensor var_7122 = const()[name = tensor("op_7122"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621645760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621953024))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_49_cast = conv(dilations = var_7122, groups = var_5930, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_7120, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor("q_49_cast")]; + tensor var_7126 = const()[name = tensor("op_7126"), val = tensor([1, 1])]; + tensor var_7128 = const()[name = tensor("op_7128"), val = tensor([1, 1])]; + tensor k_97_pad_type_0 = const()[name = tensor("k_97_pad_type_0"), val = tensor("custom")]; + tensor k_97_pad_0 = const()[name = tensor("k_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621953216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622260480))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_97_cast = conv(dilations = var_7128, groups = var_5930, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_7126, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor("k_97_cast")]; + tensor var_7132 = const()[name = tensor("op_7132"), val = tensor([1, 1])]; + tensor var_7134 = const()[name = tensor("op_7134"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622260672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622567936))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_49_cast = conv(dilations = var_7134, groups = var_5930, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_7132, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor("v_49_cast")]; + tensor var_7138_begin_0 = const()[name = tensor("op_7138_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7138_end_0 = const()[name = tensor("op_7138_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_7138_end_mask_0 = const()[name = tensor("op_7138_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7138_cast = slice_by_index(begin = var_7138_begin_0, end = var_7138_end_0, end_mask = var_7138_end_mask_0, x = q_49_cast)[name = tensor("op_7138_cast")]; + tensor var_7142_begin_0 = const()[name = tensor("op_7142_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7142_end_0 = const()[name = tensor("op_7142_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_7142_end_mask_0 = const()[name = tensor("op_7142_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7142_cast = slice_by_index(begin = var_7142_begin_0, end = var_7142_end_0, end_mask = var_7142_end_mask_0, x = q_49_cast)[name = tensor("op_7142_cast")]; + tensor var_7146_begin_0 = const()[name = tensor("op_7146_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_7146_end_0 = const()[name = tensor("op_7146_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_7146_end_mask_0 = const()[name = tensor("op_7146_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7146_cast = slice_by_index(begin = var_7146_begin_0, end = var_7146_end_0, end_mask = var_7146_end_mask_0, x = q_49_cast)[name = tensor("op_7146_cast")]; + tensor var_7150_begin_0 = const()[name = tensor("op_7150_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_7150_end_0 = const()[name = tensor("op_7150_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_7150_end_mask_0 = const()[name = tensor("op_7150_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7150_cast = slice_by_index(begin = var_7150_begin_0, end = var_7150_end_0, end_mask = var_7150_end_mask_0, x = q_49_cast)[name = tensor("op_7150_cast")]; + tensor var_7154_begin_0 = const()[name = tensor("op_7154_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_7154_end_0 = const()[name = tensor("op_7154_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_7154_end_mask_0 = const()[name = tensor("op_7154_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7154_cast = slice_by_index(begin = var_7154_begin_0, end = var_7154_end_0, end_mask = var_7154_end_mask_0, x = q_49_cast)[name = tensor("op_7154_cast")]; + tensor var_7158_begin_0 = const()[name = tensor("op_7158_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_7158_end_0 = const()[name = tensor("op_7158_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_7158_end_mask_0 = const()[name = tensor("op_7158_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7158_cast = slice_by_index(begin = var_7158_begin_0, end = var_7158_end_0, end_mask = var_7158_end_mask_0, x = q_49_cast)[name = tensor("op_7158_cast")]; + tensor var_7162_begin_0 = const()[name = tensor("op_7162_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_7162_end_0 = const()[name = tensor("op_7162_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_7162_end_mask_0 = const()[name = tensor("op_7162_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7162_cast = slice_by_index(begin = var_7162_begin_0, end = var_7162_end_0, end_mask = var_7162_end_mask_0, x = q_49_cast)[name = tensor("op_7162_cast")]; + tensor var_7166_begin_0 = const()[name = tensor("op_7166_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_7166_end_0 = const()[name = tensor("op_7166_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_7166_end_mask_0 = const()[name = tensor("op_7166_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7166_cast = slice_by_index(begin = var_7166_begin_0, end = var_7166_end_0, end_mask = var_7166_end_mask_0, x = q_49_cast)[name = tensor("op_7166_cast")]; + tensor k_99_perm_0 = const()[name = tensor("k_99_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_7173_begin_0 = const()[name = tensor("op_7173_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7173_end_0 = const()[name = tensor("op_7173_end_0"), val = tensor([2, 2304, 1, 80])]; + tensor var_7173_end_mask_0 = const()[name = tensor("op_7173_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_7 = transpose(perm = k_99_perm_0, x = k_97_cast)[name = tensor("transpose_7")]; + tensor var_7173_cast = slice_by_index(begin = var_7173_begin_0, end = var_7173_end_0, end_mask = var_7173_end_mask_0, x = transpose_7)[name = tensor("op_7173_cast")]; + tensor var_7177_begin_0 = const()[name = tensor("op_7177_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_7177_end_0 = const()[name = tensor("op_7177_end_0"), val = tensor([2, 2304, 1, 160])]; + tensor var_7177_end_mask_0 = const()[name = tensor("op_7177_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7177_cast = slice_by_index(begin = var_7177_begin_0, end = var_7177_end_0, end_mask = var_7177_end_mask_0, x = transpose_7)[name = tensor("op_7177_cast")]; + tensor var_7181_begin_0 = const()[name = tensor("op_7181_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_7181_end_0 = const()[name = tensor("op_7181_end_0"), val = tensor([2, 2304, 1, 240])]; + tensor var_7181_end_mask_0 = const()[name = tensor("op_7181_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7181_cast = slice_by_index(begin = var_7181_begin_0, end = var_7181_end_0, end_mask = var_7181_end_mask_0, x = transpose_7)[name = tensor("op_7181_cast")]; + tensor var_7185_begin_0 = const()[name = tensor("op_7185_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_7185_end_0 = const()[name = tensor("op_7185_end_0"), val = tensor([2, 2304, 1, 320])]; + tensor var_7185_end_mask_0 = const()[name = tensor("op_7185_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7185_cast = slice_by_index(begin = var_7185_begin_0, end = var_7185_end_0, end_mask = var_7185_end_mask_0, x = transpose_7)[name = tensor("op_7185_cast")]; + tensor var_7189_begin_0 = const()[name = tensor("op_7189_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_7189_end_0 = const()[name = tensor("op_7189_end_0"), val = tensor([2, 2304, 1, 400])]; + tensor var_7189_end_mask_0 = const()[name = tensor("op_7189_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7189_cast = slice_by_index(begin = var_7189_begin_0, end = var_7189_end_0, end_mask = var_7189_end_mask_0, x = transpose_7)[name = tensor("op_7189_cast")]; + tensor var_7193_begin_0 = const()[name = tensor("op_7193_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_7193_end_0 = const()[name = tensor("op_7193_end_0"), val = tensor([2, 2304, 1, 480])]; + tensor var_7193_end_mask_0 = const()[name = tensor("op_7193_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7193_cast = slice_by_index(begin = var_7193_begin_0, end = var_7193_end_0, end_mask = var_7193_end_mask_0, x = transpose_7)[name = tensor("op_7193_cast")]; + tensor var_7197_begin_0 = const()[name = tensor("op_7197_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_7197_end_0 = const()[name = tensor("op_7197_end_0"), val = tensor([2, 2304, 1, 560])]; + tensor var_7197_end_mask_0 = const()[name = tensor("op_7197_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7197_cast = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = transpose_7)[name = tensor("op_7197_cast")]; + tensor var_7201_begin_0 = const()[name = tensor("op_7201_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_7201_end_0 = const()[name = tensor("op_7201_end_0"), val = tensor([2, 2304, 1, 640])]; + tensor var_7201_end_mask_0 = const()[name = tensor("op_7201_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7201_cast = slice_by_index(begin = var_7201_begin_0, end = var_7201_end_0, end_mask = var_7201_end_mask_0, x = transpose_7)[name = tensor("op_7201_cast")]; + tensor var_7203_begin_0 = const()[name = tensor("op_7203_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7203_end_0 = const()[name = tensor("op_7203_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_7203_end_mask_0 = const()[name = tensor("op_7203_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7203_cast = slice_by_index(begin = var_7203_begin_0, end = var_7203_end_0, end_mask = var_7203_end_mask_0, x = v_49_cast)[name = tensor("op_7203_cast")]; + tensor var_7207_begin_0 = const()[name = tensor("op_7207_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7207_end_0 = const()[name = tensor("op_7207_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_7207_end_mask_0 = const()[name = tensor("op_7207_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7207_cast = slice_by_index(begin = var_7207_begin_0, end = var_7207_end_0, end_mask = var_7207_end_mask_0, x = v_49_cast)[name = tensor("op_7207_cast")]; + tensor var_7211_begin_0 = const()[name = tensor("op_7211_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_7211_end_0 = const()[name = tensor("op_7211_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_7211_end_mask_0 = const()[name = tensor("op_7211_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7211_cast = slice_by_index(begin = var_7211_begin_0, end = var_7211_end_0, end_mask = var_7211_end_mask_0, x = v_49_cast)[name = tensor("op_7211_cast")]; + tensor var_7215_begin_0 = const()[name = tensor("op_7215_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_7215_end_0 = const()[name = tensor("op_7215_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_7215_end_mask_0 = const()[name = tensor("op_7215_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7215_cast = slice_by_index(begin = var_7215_begin_0, end = var_7215_end_0, end_mask = var_7215_end_mask_0, x = v_49_cast)[name = tensor("op_7215_cast")]; + tensor var_7219_begin_0 = const()[name = tensor("op_7219_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_7219_end_0 = const()[name = tensor("op_7219_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_7219_end_mask_0 = const()[name = tensor("op_7219_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7219_cast = slice_by_index(begin = var_7219_begin_0, end = var_7219_end_0, end_mask = var_7219_end_mask_0, x = v_49_cast)[name = tensor("op_7219_cast")]; + tensor var_7223_begin_0 = const()[name = tensor("op_7223_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_7223_end_0 = const()[name = tensor("op_7223_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_7223_end_mask_0 = const()[name = tensor("op_7223_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7223_cast = slice_by_index(begin = var_7223_begin_0, end = var_7223_end_0, end_mask = var_7223_end_mask_0, x = v_49_cast)[name = tensor("op_7223_cast")]; + tensor var_7227_begin_0 = const()[name = tensor("op_7227_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_7227_end_0 = const()[name = tensor("op_7227_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_7227_end_mask_0 = const()[name = tensor("op_7227_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7227_cast = slice_by_index(begin = var_7227_begin_0, end = var_7227_end_0, end_mask = var_7227_end_mask_0, x = v_49_cast)[name = tensor("op_7227_cast")]; + tensor var_7231_begin_0 = const()[name = tensor("op_7231_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_7231_end_0 = const()[name = tensor("op_7231_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_7231_end_mask_0 = const()[name = tensor("op_7231_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7231_cast = slice_by_index(begin = var_7231_begin_0, end = var_7231_end_0, end_mask = var_7231_end_mask_0, x = v_49_cast)[name = tensor("op_7231_cast")]; + tensor var_7235_equation_0 = const()[name = tensor("op_7235_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7235_cast = einsum(equation = var_7235_equation_0, values = (var_7173_cast, var_7138_cast))[name = tensor("op_7235_cast")]; + tensor var_7236_to_fp16 = const()[name = tensor("op_7236_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_385_cast = mul(x = var_7235_cast, y = var_7236_to_fp16)[name = tensor("aw_385_cast")]; + tensor var_7239_equation_0 = const()[name = tensor("op_7239_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7239_cast = einsum(equation = var_7239_equation_0, values = (var_7177_cast, var_7142_cast))[name = tensor("op_7239_cast")]; + tensor var_7240_to_fp16 = const()[name = tensor("op_7240_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_387_cast = mul(x = var_7239_cast, y = var_7240_to_fp16)[name = tensor("aw_387_cast")]; + tensor var_7243_equation_0 = const()[name = tensor("op_7243_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7243_cast = einsum(equation = var_7243_equation_0, values = (var_7181_cast, var_7146_cast))[name = tensor("op_7243_cast")]; + tensor var_7244_to_fp16 = const()[name = tensor("op_7244_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_389_cast = mul(x = var_7243_cast, y = var_7244_to_fp16)[name = tensor("aw_389_cast")]; + tensor var_7247_equation_0 = const()[name = tensor("op_7247_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7247_cast = einsum(equation = var_7247_equation_0, values = (var_7185_cast, var_7150_cast))[name = tensor("op_7247_cast")]; + tensor var_7248_to_fp16 = const()[name = tensor("op_7248_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_391_cast = mul(x = var_7247_cast, y = var_7248_to_fp16)[name = tensor("aw_391_cast")]; + tensor var_7251_equation_0 = const()[name = tensor("op_7251_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7251_cast = einsum(equation = var_7251_equation_0, values = (var_7189_cast, var_7154_cast))[name = tensor("op_7251_cast")]; + tensor var_7252_to_fp16 = const()[name = tensor("op_7252_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_393_cast = mul(x = var_7251_cast, y = var_7252_to_fp16)[name = tensor("aw_393_cast")]; + tensor var_7255_equation_0 = const()[name = tensor("op_7255_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7255_cast = einsum(equation = var_7255_equation_0, values = (var_7193_cast, var_7158_cast))[name = tensor("op_7255_cast")]; + tensor var_7256_to_fp16 = const()[name = tensor("op_7256_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_395_cast = mul(x = var_7255_cast, y = var_7256_to_fp16)[name = tensor("aw_395_cast")]; + tensor var_7259_equation_0 = const()[name = tensor("op_7259_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7259_cast = einsum(equation = var_7259_equation_0, values = (var_7197_cast, var_7162_cast))[name = tensor("op_7259_cast")]; + tensor var_7260_to_fp16 = const()[name = tensor("op_7260_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_397_cast = mul(x = var_7259_cast, y = var_7260_to_fp16)[name = tensor("aw_397_cast")]; + tensor var_7263_equation_0 = const()[name = tensor("op_7263_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7263_cast = einsum(equation = var_7263_equation_0, values = (var_7201_cast, var_7166_cast))[name = tensor("op_7263_cast")]; + tensor var_7264_to_fp16 = const()[name = tensor("op_7264_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_399_cast = mul(x = var_7263_cast, y = var_7264_to_fp16)[name = tensor("aw_399_cast")]; + tensor var_7266_cast = softmax(axis = var_5930, x = aw_385_cast)[name = tensor("op_7266_cast")]; + tensor var_7267_cast = softmax(axis = var_5930, x = aw_387_cast)[name = tensor("op_7267_cast")]; + tensor var_7268_cast = softmax(axis = var_5930, x = aw_389_cast)[name = tensor("op_7268_cast")]; + tensor var_7269_cast = softmax(axis = var_5930, x = aw_391_cast)[name = tensor("op_7269_cast")]; + tensor var_7270_cast = softmax(axis = var_5930, x = aw_393_cast)[name = tensor("op_7270_cast")]; + tensor var_7271_cast = softmax(axis = var_5930, x = aw_395_cast)[name = tensor("op_7271_cast")]; + tensor var_7272_cast = softmax(axis = var_5930, x = aw_397_cast)[name = tensor("op_7272_cast")]; + tensor var_7273_cast = softmax(axis = var_5930, x = aw_399_cast)[name = tensor("op_7273_cast")]; + tensor var_7275_equation_0 = const()[name = tensor("op_7275_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7275_cast = einsum(equation = var_7275_equation_0, values = (var_7203_cast, var_7266_cast))[name = tensor("op_7275_cast")]; + tensor var_7277_equation_0 = const()[name = tensor("op_7277_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7277_cast = einsum(equation = var_7277_equation_0, values = (var_7207_cast, var_7267_cast))[name = tensor("op_7277_cast")]; + tensor var_7279_equation_0 = const()[name = tensor("op_7279_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7279_cast = einsum(equation = var_7279_equation_0, values = (var_7211_cast, var_7268_cast))[name = tensor("op_7279_cast")]; + tensor var_7281_equation_0 = const()[name = tensor("op_7281_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7281_cast = einsum(equation = var_7281_equation_0, values = (var_7215_cast, var_7269_cast))[name = tensor("op_7281_cast")]; + tensor var_7283_equation_0 = const()[name = tensor("op_7283_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7283_cast = einsum(equation = var_7283_equation_0, values = (var_7219_cast, var_7270_cast))[name = tensor("op_7283_cast")]; + tensor var_7285_equation_0 = const()[name = tensor("op_7285_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7285_cast = einsum(equation = var_7285_equation_0, values = (var_7223_cast, var_7271_cast))[name = tensor("op_7285_cast")]; + tensor var_7287_equation_0 = const()[name = tensor("op_7287_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7287_cast = einsum(equation = var_7287_equation_0, values = (var_7227_cast, var_7272_cast))[name = tensor("op_7287_cast")]; + tensor var_7289_equation_0 = const()[name = tensor("op_7289_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7289_cast = einsum(equation = var_7289_equation_0, values = (var_7231_cast, var_7273_cast))[name = tensor("op_7289_cast")]; + tensor input_435_interleave_0 = const()[name = tensor("input_435_interleave_0"), val = tensor(false)]; + tensor input_435_cast = concat(axis = var_5930, interleave = input_435_interleave_0, values = (var_7275_cast, var_7277_cast, var_7279_cast, var_7281_cast, var_7283_cast, var_7285_cast, var_7287_cast, var_7289_cast))[name = tensor("input_435_cast")]; + tensor var_7295 = const()[name = tensor("op_7295"), val = tensor([1, 1])]; + tensor var_7297 = const()[name = tensor("op_7297"), val = tensor([1, 1])]; + tensor var_7299_pad_type_0 = const()[name = tensor("op_7299_pad_type_0"), val = tensor("custom")]; + tensor var_7299_pad_0 = const()[name = tensor("op_7299_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622568128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622875392))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622875584)))]; + tensor var_7299_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_7297, groups = var_5930, pad = var_7299_pad_0, pad_type = var_7299_pad_type_0, strides = var_7295, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_435_cast)[name = tensor("op_7299_cast")]; + tensor inputs_75_cast = add(x = var_7299_cast, y = inputs_73_cast)[name = tensor("inputs_75_cast")]; + tensor var_7303 = const()[name = tensor("op_7303"), val = tensor([1])]; + tensor channels_mean_75_cast = reduce_mean(axes = var_7303, keep_dims = var_5925, x = inputs_75_cast)[name = tensor("channels_mean_75_cast")]; + tensor zero_mean_75_cast = sub(x = inputs_75_cast, y = channels_mean_75_cast)[name = tensor("zero_mean_75_cast")]; + tensor zero_mean_sq_75_cast = mul(x = zero_mean_75_cast, y = zero_mean_75_cast)[name = tensor("zero_mean_sq_75_cast")]; + tensor var_7307 = const()[name = tensor("op_7307"), val = tensor([1])]; + tensor var_7308_cast = reduce_mean(axes = var_7307, keep_dims = var_5925, x = zero_mean_sq_75_cast)[name = tensor("op_7308_cast")]; + tensor var_7309_to_fp16 = const()[name = tensor("op_7309_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7310_cast = add(x = var_7308_cast, y = var_7309_to_fp16)[name = tensor("op_7310_cast")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_7310_cast)[name = tensor("denom_75_cast")]; + tensor out_75_cast = mul(x = zero_mean_75_cast, y = denom_75_cast)[name = tensor("out_75_cast")]; + tensor var_7314_to_fp16 = const()[name = tensor("op_7314_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622876928)))]; + tensor var_7315_cast = add(x = out_75_cast, y = var_7314_to_fp16)[name = tensor("op_7315_cast")]; + tensor var_7317_to_fp16 = const()[name = tensor("op_7317_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622878272)))]; + tensor hidden_states_269_cast = mul(x = var_7315_cast, y = var_7317_to_fp16)[name = tensor("hidden_states_269_cast")]; + tensor var_7324 = const()[name = tensor("op_7324"), val = tensor([1, 1])]; + tensor var_7326 = const()[name = tensor("op_7326"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622879616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623186880))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_51_cast = conv(dilations = var_7326, groups = var_5930, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_7324, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_269_cast)[name = tensor("q_51_cast")]; + tensor var_7330 = const()[name = tensor("op_7330"), val = tensor([1, 1])]; + tensor var_7332 = const()[name = tensor("op_7332"), val = tensor([1, 1])]; + tensor k_101_pad_type_0 = const()[name = tensor("k_101_pad_type_0"), val = tensor("custom")]; + tensor k_101_pad_0 = const()[name = tensor("k_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623187072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623555776))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_101_cast = conv(dilations = var_7332, groups = var_5930, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_7330, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_101_cast")]; + tensor var_7336 = const()[name = tensor("op_7336"), val = tensor([1, 1])]; + tensor var_7338 = const()[name = tensor("op_7338"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623555968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623924672))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_51_cast = conv(dilations = var_7338, groups = var_5930, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_7336, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_51_cast")]; + tensor var_7342_begin_0 = const()[name = tensor("op_7342_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7342_end_0 = const()[name = tensor("op_7342_end_0"), val = tensor([2, 80, 1, 2304])]; + tensor var_7342_end_mask_0 = const()[name = tensor("op_7342_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7342_cast = slice_by_index(begin = var_7342_begin_0, end = var_7342_end_0, end_mask = var_7342_end_mask_0, x = q_51_cast)[name = tensor("op_7342_cast")]; + tensor var_7346_begin_0 = const()[name = tensor("op_7346_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7346_end_0 = const()[name = tensor("op_7346_end_0"), val = tensor([2, 160, 1, 2304])]; + tensor var_7346_end_mask_0 = const()[name = tensor("op_7346_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7346_cast = slice_by_index(begin = var_7346_begin_0, end = var_7346_end_0, end_mask = var_7346_end_mask_0, x = q_51_cast)[name = tensor("op_7346_cast")]; + tensor var_7350_begin_0 = const()[name = tensor("op_7350_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_7350_end_0 = const()[name = tensor("op_7350_end_0"), val = tensor([2, 240, 1, 2304])]; + tensor var_7350_end_mask_0 = const()[name = tensor("op_7350_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7350_cast = slice_by_index(begin = var_7350_begin_0, end = var_7350_end_0, end_mask = var_7350_end_mask_0, x = q_51_cast)[name = tensor("op_7350_cast")]; + tensor var_7354_begin_0 = const()[name = tensor("op_7354_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_7354_end_0 = const()[name = tensor("op_7354_end_0"), val = tensor([2, 320, 1, 2304])]; + tensor var_7354_end_mask_0 = const()[name = tensor("op_7354_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7354_cast = slice_by_index(begin = var_7354_begin_0, end = var_7354_end_0, end_mask = var_7354_end_mask_0, x = q_51_cast)[name = tensor("op_7354_cast")]; + tensor var_7358_begin_0 = const()[name = tensor("op_7358_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_7358_end_0 = const()[name = tensor("op_7358_end_0"), val = tensor([2, 400, 1, 2304])]; + tensor var_7358_end_mask_0 = const()[name = tensor("op_7358_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7358_cast = slice_by_index(begin = var_7358_begin_0, end = var_7358_end_0, end_mask = var_7358_end_mask_0, x = q_51_cast)[name = tensor("op_7358_cast")]; + tensor var_7362_begin_0 = const()[name = tensor("op_7362_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_7362_end_0 = const()[name = tensor("op_7362_end_0"), val = tensor([2, 480, 1, 2304])]; + tensor var_7362_end_mask_0 = const()[name = tensor("op_7362_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7362_cast = slice_by_index(begin = var_7362_begin_0, end = var_7362_end_0, end_mask = var_7362_end_mask_0, x = q_51_cast)[name = tensor("op_7362_cast")]; + tensor var_7366_begin_0 = const()[name = tensor("op_7366_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_7366_end_0 = const()[name = tensor("op_7366_end_0"), val = tensor([2, 560, 1, 2304])]; + tensor var_7366_end_mask_0 = const()[name = tensor("op_7366_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7366_cast = slice_by_index(begin = var_7366_begin_0, end = var_7366_end_0, end_mask = var_7366_end_mask_0, x = q_51_cast)[name = tensor("op_7366_cast")]; + tensor var_7370_begin_0 = const()[name = tensor("op_7370_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_7370_end_0 = const()[name = tensor("op_7370_end_0"), val = tensor([2, 640, 1, 2304])]; + tensor var_7370_end_mask_0 = const()[name = tensor("op_7370_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7370_cast = slice_by_index(begin = var_7370_begin_0, end = var_7370_end_0, end_mask = var_7370_end_mask_0, x = q_51_cast)[name = tensor("op_7370_cast")]; + tensor k_103_perm_0 = const()[name = tensor("k_103_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_7377_begin_0 = const()[name = tensor("op_7377_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7377_end_0 = const()[name = tensor("op_7377_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_7377_end_mask_0 = const()[name = tensor("op_7377_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_6 = transpose(perm = k_103_perm_0, x = k_101_cast)[name = tensor("transpose_6")]; + tensor var_7377_cast = slice_by_index(begin = var_7377_begin_0, end = var_7377_end_0, end_mask = var_7377_end_mask_0, x = transpose_6)[name = tensor("op_7377_cast")]; + tensor var_7381_begin_0 = const()[name = tensor("op_7381_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_7381_end_0 = const()[name = tensor("op_7381_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_7381_end_mask_0 = const()[name = tensor("op_7381_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7381_cast = slice_by_index(begin = var_7381_begin_0, end = var_7381_end_0, end_mask = var_7381_end_mask_0, x = transpose_6)[name = tensor("op_7381_cast")]; + tensor var_7385_begin_0 = const()[name = tensor("op_7385_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_7385_end_0 = const()[name = tensor("op_7385_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_7385_end_mask_0 = const()[name = tensor("op_7385_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7385_cast = slice_by_index(begin = var_7385_begin_0, end = var_7385_end_0, end_mask = var_7385_end_mask_0, x = transpose_6)[name = tensor("op_7385_cast")]; + tensor var_7389_begin_0 = const()[name = tensor("op_7389_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_7389_end_0 = const()[name = tensor("op_7389_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_7389_end_mask_0 = const()[name = tensor("op_7389_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7389_cast = slice_by_index(begin = var_7389_begin_0, end = var_7389_end_0, end_mask = var_7389_end_mask_0, x = transpose_6)[name = tensor("op_7389_cast")]; + tensor var_7393_begin_0 = const()[name = tensor("op_7393_begin_0"), val = tensor([0, 0, 0, 320])]; + tensor var_7393_end_0 = const()[name = tensor("op_7393_end_0"), val = tensor([2, 77, 1, 400])]; + tensor var_7393_end_mask_0 = const()[name = tensor("op_7393_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7393_cast = slice_by_index(begin = var_7393_begin_0, end = var_7393_end_0, end_mask = var_7393_end_mask_0, x = transpose_6)[name = tensor("op_7393_cast")]; + tensor var_7397_begin_0 = const()[name = tensor("op_7397_begin_0"), val = tensor([0, 0, 0, 400])]; + tensor var_7397_end_0 = const()[name = tensor("op_7397_end_0"), val = tensor([2, 77, 1, 480])]; + tensor var_7397_end_mask_0 = const()[name = tensor("op_7397_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7397_cast = slice_by_index(begin = var_7397_begin_0, end = var_7397_end_0, end_mask = var_7397_end_mask_0, x = transpose_6)[name = tensor("op_7397_cast")]; + tensor var_7401_begin_0 = const()[name = tensor("op_7401_begin_0"), val = tensor([0, 0, 0, 480])]; + tensor var_7401_end_0 = const()[name = tensor("op_7401_end_0"), val = tensor([2, 77, 1, 560])]; + tensor var_7401_end_mask_0 = const()[name = tensor("op_7401_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7401_cast = slice_by_index(begin = var_7401_begin_0, end = var_7401_end_0, end_mask = var_7401_end_mask_0, x = transpose_6)[name = tensor("op_7401_cast")]; + tensor var_7405_begin_0 = const()[name = tensor("op_7405_begin_0"), val = tensor([0, 0, 0, 560])]; + tensor var_7405_end_0 = const()[name = tensor("op_7405_end_0"), val = tensor([2, 77, 1, 640])]; + tensor var_7405_end_mask_0 = const()[name = tensor("op_7405_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7405_cast = slice_by_index(begin = var_7405_begin_0, end = var_7405_end_0, end_mask = var_7405_end_mask_0, x = transpose_6)[name = tensor("op_7405_cast")]; + tensor var_7407_begin_0 = const()[name = tensor("op_7407_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7407_end_0 = const()[name = tensor("op_7407_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_7407_end_mask_0 = const()[name = tensor("op_7407_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7407_cast = slice_by_index(begin = var_7407_begin_0, end = var_7407_end_0, end_mask = var_7407_end_mask_0, x = v_51_cast)[name = tensor("op_7407_cast")]; + tensor var_7411_begin_0 = const()[name = tensor("op_7411_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7411_end_0 = const()[name = tensor("op_7411_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_7411_end_mask_0 = const()[name = tensor("op_7411_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7411_cast = slice_by_index(begin = var_7411_begin_0, end = var_7411_end_0, end_mask = var_7411_end_mask_0, x = v_51_cast)[name = tensor("op_7411_cast")]; + tensor var_7415_begin_0 = const()[name = tensor("op_7415_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_7415_end_0 = const()[name = tensor("op_7415_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_7415_end_mask_0 = const()[name = tensor("op_7415_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7415_cast = slice_by_index(begin = var_7415_begin_0, end = var_7415_end_0, end_mask = var_7415_end_mask_0, x = v_51_cast)[name = tensor("op_7415_cast")]; + tensor var_7419_begin_0 = const()[name = tensor("op_7419_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_7419_end_0 = const()[name = tensor("op_7419_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_7419_end_mask_0 = const()[name = tensor("op_7419_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7419_cast = slice_by_index(begin = var_7419_begin_0, end = var_7419_end_0, end_mask = var_7419_end_mask_0, x = v_51_cast)[name = tensor("op_7419_cast")]; + tensor var_7423_begin_0 = const()[name = tensor("op_7423_begin_0"), val = tensor([0, 320, 0, 0])]; + tensor var_7423_end_0 = const()[name = tensor("op_7423_end_0"), val = tensor([2, 400, 1, 77])]; + tensor var_7423_end_mask_0 = const()[name = tensor("op_7423_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7423_cast = slice_by_index(begin = var_7423_begin_0, end = var_7423_end_0, end_mask = var_7423_end_mask_0, x = v_51_cast)[name = tensor("op_7423_cast")]; + tensor var_7427_begin_0 = const()[name = tensor("op_7427_begin_0"), val = tensor([0, 400, 0, 0])]; + tensor var_7427_end_0 = const()[name = tensor("op_7427_end_0"), val = tensor([2, 480, 1, 77])]; + tensor var_7427_end_mask_0 = const()[name = tensor("op_7427_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7427_cast = slice_by_index(begin = var_7427_begin_0, end = var_7427_end_0, end_mask = var_7427_end_mask_0, x = v_51_cast)[name = tensor("op_7427_cast")]; + tensor var_7431_begin_0 = const()[name = tensor("op_7431_begin_0"), val = tensor([0, 480, 0, 0])]; + tensor var_7431_end_0 = const()[name = tensor("op_7431_end_0"), val = tensor([2, 560, 1, 77])]; + tensor var_7431_end_mask_0 = const()[name = tensor("op_7431_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7431_cast = slice_by_index(begin = var_7431_begin_0, end = var_7431_end_0, end_mask = var_7431_end_mask_0, x = v_51_cast)[name = tensor("op_7431_cast")]; + tensor var_7435_begin_0 = const()[name = tensor("op_7435_begin_0"), val = tensor([0, 560, 0, 0])]; + tensor var_7435_end_0 = const()[name = tensor("op_7435_end_0"), val = tensor([2, 640, 1, 77])]; + tensor var_7435_end_mask_0 = const()[name = tensor("op_7435_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7435_cast = slice_by_index(begin = var_7435_begin_0, end = var_7435_end_0, end_mask = var_7435_end_mask_0, x = v_51_cast)[name = tensor("op_7435_cast")]; + tensor var_7439_equation_0 = const()[name = tensor("op_7439_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7439_cast = einsum(equation = var_7439_equation_0, values = (var_7377_cast, var_7342_cast))[name = tensor("op_7439_cast")]; + tensor var_7440_to_fp16 = const()[name = tensor("op_7440_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_401_cast = mul(x = var_7439_cast, y = var_7440_to_fp16)[name = tensor("aw_401_cast")]; + tensor var_7443_equation_0 = const()[name = tensor("op_7443_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7443_cast = einsum(equation = var_7443_equation_0, values = (var_7381_cast, var_7346_cast))[name = tensor("op_7443_cast")]; + tensor var_7444_to_fp16 = const()[name = tensor("op_7444_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_403_cast = mul(x = var_7443_cast, y = var_7444_to_fp16)[name = tensor("aw_403_cast")]; + tensor var_7447_equation_0 = const()[name = tensor("op_7447_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7447_cast = einsum(equation = var_7447_equation_0, values = (var_7385_cast, var_7350_cast))[name = tensor("op_7447_cast")]; + tensor var_7448_to_fp16 = const()[name = tensor("op_7448_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_405_cast = mul(x = var_7447_cast, y = var_7448_to_fp16)[name = tensor("aw_405_cast")]; + tensor var_7451_equation_0 = const()[name = tensor("op_7451_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7451_cast = einsum(equation = var_7451_equation_0, values = (var_7389_cast, var_7354_cast))[name = tensor("op_7451_cast")]; + tensor var_7452_to_fp16 = const()[name = tensor("op_7452_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_407_cast = mul(x = var_7451_cast, y = var_7452_to_fp16)[name = tensor("aw_407_cast")]; + tensor var_7455_equation_0 = const()[name = tensor("op_7455_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7455_cast = einsum(equation = var_7455_equation_0, values = (var_7393_cast, var_7358_cast))[name = tensor("op_7455_cast")]; + tensor var_7456_to_fp16 = const()[name = tensor("op_7456_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_409_cast = mul(x = var_7455_cast, y = var_7456_to_fp16)[name = tensor("aw_409_cast")]; + tensor var_7459_equation_0 = const()[name = tensor("op_7459_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7459_cast = einsum(equation = var_7459_equation_0, values = (var_7397_cast, var_7362_cast))[name = tensor("op_7459_cast")]; + tensor var_7460_to_fp16 = const()[name = tensor("op_7460_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_411_cast = mul(x = var_7459_cast, y = var_7460_to_fp16)[name = tensor("aw_411_cast")]; + tensor var_7463_equation_0 = const()[name = tensor("op_7463_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7463_cast = einsum(equation = var_7463_equation_0, values = (var_7401_cast, var_7366_cast))[name = tensor("op_7463_cast")]; + tensor var_7464_to_fp16 = const()[name = tensor("op_7464_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_413_cast = mul(x = var_7463_cast, y = var_7464_to_fp16)[name = tensor("aw_413_cast")]; + tensor var_7467_equation_0 = const()[name = tensor("op_7467_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7467_cast = einsum(equation = var_7467_equation_0, values = (var_7405_cast, var_7370_cast))[name = tensor("op_7467_cast")]; + tensor var_7468_to_fp16 = const()[name = tensor("op_7468_to_fp16"), val = tensor(0x1.cap-4)]; + tensor aw_415_cast = mul(x = var_7467_cast, y = var_7468_to_fp16)[name = tensor("aw_415_cast")]; + tensor var_7470_cast = softmax(axis = var_5930, x = aw_401_cast)[name = tensor("op_7470_cast")]; + tensor var_7471_cast = softmax(axis = var_5930, x = aw_403_cast)[name = tensor("op_7471_cast")]; + tensor var_7472_cast = softmax(axis = var_5930, x = aw_405_cast)[name = tensor("op_7472_cast")]; + tensor var_7473_cast = softmax(axis = var_5930, x = aw_407_cast)[name = tensor("op_7473_cast")]; + tensor var_7474_cast = softmax(axis = var_5930, x = aw_409_cast)[name = tensor("op_7474_cast")]; + tensor var_7475_cast = softmax(axis = var_5930, x = aw_411_cast)[name = tensor("op_7475_cast")]; + tensor var_7476_cast = softmax(axis = var_5930, x = aw_413_cast)[name = tensor("op_7476_cast")]; + tensor var_7477_cast = softmax(axis = var_5930, x = aw_415_cast)[name = tensor("op_7477_cast")]; + tensor var_7479_equation_0 = const()[name = tensor("op_7479_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7479_cast = einsum(equation = var_7479_equation_0, values = (var_7407_cast, var_7470_cast))[name = tensor("op_7479_cast")]; + tensor var_7481_equation_0 = const()[name = tensor("op_7481_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7481_cast = einsum(equation = var_7481_equation_0, values = (var_7411_cast, var_7471_cast))[name = tensor("op_7481_cast")]; + tensor var_7483_equation_0 = const()[name = tensor("op_7483_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7483_cast = einsum(equation = var_7483_equation_0, values = (var_7415_cast, var_7472_cast))[name = tensor("op_7483_cast")]; + tensor var_7485_equation_0 = const()[name = tensor("op_7485_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7485_cast = einsum(equation = var_7485_equation_0, values = (var_7419_cast, var_7473_cast))[name = tensor("op_7485_cast")]; + tensor var_7487_equation_0 = const()[name = tensor("op_7487_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7487_cast = einsum(equation = var_7487_equation_0, values = (var_7423_cast, var_7474_cast))[name = tensor("op_7487_cast")]; + tensor var_7489_equation_0 = const()[name = tensor("op_7489_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7489_cast = einsum(equation = var_7489_equation_0, values = (var_7427_cast, var_7475_cast))[name = tensor("op_7489_cast")]; + tensor var_7491_equation_0 = const()[name = tensor("op_7491_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7491_cast = einsum(equation = var_7491_equation_0, values = (var_7431_cast, var_7476_cast))[name = tensor("op_7491_cast")]; + tensor var_7493_equation_0 = const()[name = tensor("op_7493_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7493_cast = einsum(equation = var_7493_equation_0, values = (var_7435_cast, var_7477_cast))[name = tensor("op_7493_cast")]; + tensor input_437_interleave_0 = const()[name = tensor("input_437_interleave_0"), val = tensor(false)]; + tensor input_437_cast = concat(axis = var_5930, interleave = input_437_interleave_0, values = (var_7479_cast, var_7481_cast, var_7483_cast, var_7485_cast, var_7487_cast, var_7489_cast, var_7491_cast, var_7493_cast))[name = tensor("input_437_cast")]; + tensor var_7499 = const()[name = tensor("op_7499"), val = tensor([1, 1])]; + tensor var_7501 = const()[name = tensor("op_7501"), val = tensor([1, 1])]; + tensor var_7503_pad_type_0 = const()[name = tensor("op_7503_pad_type_0"), val = tensor("custom")]; + tensor var_7503_pad_0 = const()[name = tensor("op_7503_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623924864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624232128))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624232320)))]; + tensor var_7503_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_7501, groups = var_5930, pad = var_7503_pad_0, pad_type = var_7503_pad_type_0, strides = var_7499, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_437_cast)[name = tensor("op_7503_cast")]; + tensor inputs_77_cast = add(x = var_7503_cast, y = inputs_75_cast)[name = tensor("inputs_77_cast")]; + tensor var_7507 = const()[name = tensor("op_7507"), val = tensor([1])]; + tensor channels_mean_77_cast = reduce_mean(axes = var_7507, keep_dims = var_5925, x = inputs_77_cast)[name = tensor("channels_mean_77_cast")]; + tensor zero_mean_77_cast = sub(x = inputs_77_cast, y = channels_mean_77_cast)[name = tensor("zero_mean_77_cast")]; + tensor zero_mean_sq_77_cast = mul(x = zero_mean_77_cast, y = zero_mean_77_cast)[name = tensor("zero_mean_sq_77_cast")]; + tensor var_7511 = const()[name = tensor("op_7511"), val = tensor([1])]; + tensor var_7512_cast = reduce_mean(axes = var_7511, keep_dims = var_5925, x = zero_mean_sq_77_cast)[name = tensor("op_7512_cast")]; + tensor var_7513_to_fp16 = const()[name = tensor("op_7513_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7514_cast = add(x = var_7512_cast, y = var_7513_to_fp16)[name = tensor("op_7514_cast")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_7514_cast)[name = tensor("denom_77_cast")]; + tensor out_77_cast = mul(x = zero_mean_77_cast, y = denom_77_cast)[name = tensor("out_77_cast")]; + tensor var_7518_to_fp16 = const()[name = tensor("op_7518_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624233664)))]; + tensor var_7519_cast = add(x = out_77_cast, y = var_7518_to_fp16)[name = tensor("op_7519_cast")]; + tensor var_7521_to_fp16 = const()[name = tensor("op_7521_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624235008)))]; + tensor input_439_cast = mul(x = var_7519_cast, y = var_7521_to_fp16)[name = tensor("input_439_cast")]; + tensor var_7529 = const()[name = tensor("op_7529"), val = tensor([1, 1])]; + tensor var_7531 = const()[name = tensor("op_7531"), val = tensor([1, 1])]; + tensor var_7533_pad_type_0 = const()[name = tensor("op_7533_pad_type_0"), val = tensor("custom")]; + tensor var_7533_pad_0 = const()[name = tensor("op_7533_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624236352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626694016))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626694208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626698112))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_7533_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7531, groups = var_5930, pad = var_7533_pad_0, pad_type = var_7533_pad_type_0, strides = var_7529, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_439_cast)[name = tensor("op_7533_cast")]; + tensor var_7534_split_sizes_0 = const()[name = tensor("op_7534_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_7534_axis_0 = const()[name = tensor("op_7534_axis_0"), val = tensor(1)]; + tensor var_7534_cast_0, tensor var_7534_cast_1 = split(axis = var_7534_axis_0, split_sizes = var_7534_split_sizes_0, x = var_7533_cast)[name = tensor("op_7534_cast")]; + tensor var_7536_mode_0 = const()[name = tensor("op_7536_mode_0"), val = tensor("EXACT")]; + tensor var_7536_cast = gelu(mode = var_7536_mode_0, x = var_7534_cast_1)[name = tensor("op_7536_cast")]; + tensor input_441_cast = mul(x = var_7534_cast_0, y = var_7536_cast)[name = tensor("input_441_cast")]; + tensor var_7540 = const()[name = tensor("op_7540"), val = tensor([1, 1])]; + tensor var_7542 = const()[name = tensor("op_7542"), val = tensor([1, 1])]; + tensor var_7544_pad_type_0 = const()[name = tensor("op_7544_pad_type_0"), val = tensor("custom")]; + tensor var_7544_pad_0 = const()[name = tensor("op_7544_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626698304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627927168))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627927360)))]; + tensor var_7544_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7542, groups = var_5930, pad = var_7544_pad_0, pad_type = var_7544_pad_type_0, strides = var_7540, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_441_cast)[name = tensor("op_7544_cast")]; + tensor hidden_states_273_cast = add(x = var_7544_cast, y = inputs_77_cast)[name = tensor("hidden_states_273_cast")]; + tensor var_7546 = const()[name = tensor("op_7546"), val = tensor([2, 640, 48, 48])]; + tensor input_443_cast = reshape(shape = var_7546, x = hidden_states_273_cast)[name = tensor("input_443_cast")]; + tensor var_7550 = const()[name = tensor("op_7550"), val = tensor([1, 1])]; + tensor var_7552 = const()[name = tensor("op_7552"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627928704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628235968))), name = tensor("up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628236160)))]; + tensor hidden_states_275_cast = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_7552, groups = var_5930, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_7550, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized, x = input_443_cast)[name = tensor("hidden_states_275_cast")]; + tensor input_445_cast = add(x = hidden_states_275_cast, y = hidden_states_263_cast)[name = tensor("input_445_cast")]; + tensor input_447_scale_factor_height_0 = const()[name = tensor("input_447_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_447_scale_factor_width_0 = const()[name = tensor("input_447_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor 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("input_447_cast")]; + tensor var_7561 = const()[name = tensor("op_7561"), val = tensor([1, 1])]; + tensor var_7563 = const()[name = tensor("op_7563"), val = tensor([1, 1])]; + tensor hidden_states_277_pad_type_0 = const()[name = tensor("hidden_states_277_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_277_pad_0 = const()[name = tensor("hidden_states_277_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628237504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631002368))), name = tensor("up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631002560)))]; + tensor hidden_states_277_cast = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_7563, groups = var_5930, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = var_7561, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized, x = input_447_cast)[name = tensor("hidden_states_277_cast")]; + tensor var_7582 = const()[name = tensor("op_7582"), val = tensor(true)]; + tensor var_7587 = const()[name = tensor("op_7587"), val = tensor(1)]; + tensor input_449_interleave_0 = const()[name = tensor("input_449_interleave_0"), val = tensor(false)]; + tensor input_449_cast = concat(axis = var_7587, interleave = input_449_interleave_0, values = (hidden_states_277_cast, input_61_cast))[name = tensor("input_449_cast")]; + tensor reshape_204_shape_0 = const()[name = tensor("reshape_204_shape_0"), val = tensor([2, 32, 30, 96, 96])]; + tensor reshape_204_cast = reshape(shape = reshape_204_shape_0, x = input_449_cast)[name = tensor("reshape_204_cast")]; + tensor reduce_mean_153_axes_0 = const()[name = tensor("reduce_mean_153_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_153_keep_dims_0 = const()[name = tensor("reduce_mean_153_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_153_cast")]; + tensor sub_102_cast = sub(x = reshape_204_cast, y = reduce_mean_153_cast)[name = tensor("sub_102_cast")]; + tensor square_51_cast = square(x = sub_102_cast)[name = tensor("square_51_cast")]; + tensor reduce_mean_155_axes_0 = const()[name = tensor("reduce_mean_155_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_155_keep_dims_0 = const()[name = tensor("reduce_mean_155_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_155_cast")]; + tensor add_102_y_0_to_fp16 = const()[name = tensor("add_102_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_102_cast = add(x = reduce_mean_155_cast, y = add_102_y_0_to_fp16)[name = tensor("add_102_cast")]; + tensor sqrt_51_cast = sqrt(x = add_102_cast)[name = tensor("sqrt_51_cast")]; + tensor real_div_51_cast = real_div(x = sub_102_cast, y = sqrt_51_cast)[name = tensor("real_div_51_cast")]; + tensor reshape_205_shape_0 = const()[name = tensor("reshape_205_shape_0"), val = tensor([2, 960, 96, 96])]; + tensor reshape_205_cast = reshape(shape = reshape_205_shape_0, x = real_div_51_cast)[name = tensor("reshape_205_cast")]; + tensor add_103_gamma_0_to_fp16 = const()[name = tensor("add_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631003904)))]; + tensor add_103_beta_0_to_fp16 = const()[name = tensor("add_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631005888)))]; + tensor add_103_epsilon_0_to_fp16 = const()[name = tensor("add_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_103_cast")]; + tensor input_453_cast = silu(x = add_103_cast)[name = tensor("input_453_cast")]; + tensor var_7614 = const()[name = tensor("op_7614"), val = tensor([1, 1])]; + tensor var_7616 = const()[name = tensor("op_7616"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631007872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633081536))), name = tensor("up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 960, 3, 3])]; + tensor up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633081728)))]; + tensor hidden_states_279_cast = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_7616, groups = var_7587, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_7614, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_453_cast)[name = tensor("hidden_states_279_cast")]; + tensor var_7622 = const()[name = tensor("op_7622"), val = tensor([1, 1])]; + tensor var_7624 = const()[name = tensor("op_7624"), val = tensor([1, 1])]; + tensor temb_39_pad_type_0 = const()[name = tensor("temb_39_pad_type_0"), val = tensor("custom")]; + tensor temb_39_pad_0 = const()[name = tensor("temb_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633082432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633389696))), name = tensor("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633389888)))]; + tensor temb_39_cast = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_7624, groups = var_7587, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_7622, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_39_cast")]; + tensor input_457_cast = add(x = hidden_states_279_cast, y = temb_39_cast)[name = tensor("input_457_cast")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_208_cast = reshape(shape = reshape_208_shape_0, x = input_457_cast)[name = tensor("reshape_208_cast")]; + tensor reduce_mean_156_axes_0 = const()[name = tensor("reduce_mean_156_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_156_keep_dims_0 = const()[name = tensor("reduce_mean_156_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_156_cast")]; + tensor sub_104_cast = sub(x = reshape_208_cast, y = reduce_mean_156_cast)[name = tensor("sub_104_cast")]; + tensor square_52_cast = square(x = sub_104_cast)[name = tensor("square_52_cast")]; + tensor reduce_mean_158_axes_0 = const()[name = tensor("reduce_mean_158_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_158_keep_dims_0 = const()[name = tensor("reduce_mean_158_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_158_cast")]; + tensor add_104_y_0_to_fp16 = const()[name = tensor("add_104_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_104_cast = add(x = reduce_mean_158_cast, y = add_104_y_0_to_fp16)[name = tensor("add_104_cast")]; + tensor sqrt_52_cast = sqrt(x = add_104_cast)[name = tensor("sqrt_52_cast")]; + tensor real_div_52_cast = real_div(x = sub_104_cast, y = sqrt_52_cast)[name = tensor("real_div_52_cast")]; + tensor reshape_209_shape_0 = const()[name = tensor("reshape_209_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_209_cast = reshape(shape = reshape_209_shape_0, x = real_div_52_cast)[name = tensor("reshape_209_cast")]; + tensor add_105_gamma_0_to_fp16 = const()[name = tensor("add_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633390592)))]; + tensor add_105_beta_0_to_fp16 = const()[name = tensor("add_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633391296)))]; + tensor add_105_epsilon_0_to_fp16 = const()[name = tensor("add_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_105_cast")]; + tensor input_461_cast = silu(x = add_105_cast)[name = tensor("input_461_cast")]; + tensor var_7634 = const()[name = tensor("op_7634"), val = tensor([1, 1])]; + tensor var_7636 = const()[name = tensor("op_7636"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633392000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634083264))), name = tensor("up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634083456)))]; + tensor hidden_states_281_cast = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_7636, groups = var_7587, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_7634, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_461_cast)[name = tensor("hidden_states_281_cast")]; + tensor var_7641 = const()[name = tensor("op_7641"), val = tensor([1, 1])]; + tensor var_7643 = const()[name = tensor("op_7643"), val = tensor([1, 1])]; + tensor x_23_pad_type_0 = const()[name = tensor("x_23_pad_type_0"), val = tensor("custom")]; + tensor x_23_pad_0 = const()[name = tensor("x_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634084160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634314624))), name = tensor("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 960, 1, 1])]; + tensor up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634314816)))]; + tensor x_23_cast = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_7643, groups = var_7587, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_7641, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_449_cast)[name = tensor("x_23_cast")]; + tensor hidden_states_283_cast = add(x = x_23_cast, y = hidden_states_281_cast)[name = tensor("hidden_states_283_cast")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_212_cast = reshape(shape = reshape_212_shape_0, x = hidden_states_283_cast)[name = tensor("reshape_212_cast")]; + tensor reduce_mean_159_axes_0 = const()[name = tensor("reduce_mean_159_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_159_keep_dims_0 = const()[name = tensor("reduce_mean_159_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_159_cast")]; + tensor sub_106_cast = sub(x = reshape_212_cast, y = reduce_mean_159_cast)[name = tensor("sub_106_cast")]; + tensor square_53_cast = square(x = sub_106_cast)[name = tensor("square_53_cast")]; + tensor reduce_mean_161_axes_0 = const()[name = tensor("reduce_mean_161_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_161_keep_dims_0 = const()[name = tensor("reduce_mean_161_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_161_cast")]; + tensor add_106_y_0_to_fp16 = const()[name = tensor("add_106_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_106_cast = add(x = reduce_mean_161_cast, y = add_106_y_0_to_fp16)[name = tensor("add_106_cast")]; + tensor sqrt_53_cast = sqrt(x = add_106_cast)[name = tensor("sqrt_53_cast")]; + tensor real_div_53_cast = real_div(x = sub_106_cast, y = sqrt_53_cast)[name = tensor("real_div_53_cast")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_213_cast = reshape(shape = reshape_213_shape_0, x = real_div_53_cast)[name = tensor("reshape_213_cast")]; + tensor add_107_gamma_0_to_fp16 = const()[name = tensor("add_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634315520)))]; + tensor add_107_beta_0_to_fp16 = const()[name = tensor("add_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634316224)))]; + tensor add_107_epsilon_0_to_fp16 = const()[name = tensor("add_107_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_107_cast")]; + tensor var_7663 = const()[name = tensor("op_7663"), val = tensor([1, 1])]; + tensor var_7665 = const()[name = tensor("op_7665"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634316928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634393792))), name = tensor("up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634393984)))]; + tensor hidden_states_285_cast = conv(bias = up_blocks_3_attentions_0_proj_in_bias_to_fp16, dilations = var_7665, groups = var_7587, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_7663, weight = up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized, x = add_107_cast)[name = tensor("hidden_states_285_cast")]; + tensor var_7670 = const()[name = tensor("op_7670"), val = tensor([2, 320, 1, 9216])]; + tensor inputs_79_cast = reshape(shape = var_7670, x = hidden_states_285_cast)[name = tensor("inputs_79_cast")]; + tensor var_7680 = const()[name = tensor("op_7680"), val = tensor([1])]; + tensor channels_mean_79_cast = reduce_mean(axes = var_7680, keep_dims = var_7582, x = inputs_79_cast)[name = tensor("channels_mean_79_cast")]; + tensor zero_mean_79_cast = sub(x = inputs_79_cast, y = channels_mean_79_cast)[name = tensor("zero_mean_79_cast")]; + tensor zero_mean_sq_79_cast = mul(x = zero_mean_79_cast, y = zero_mean_79_cast)[name = tensor("zero_mean_sq_79_cast")]; + tensor var_7684 = const()[name = tensor("op_7684"), val = tensor([1])]; + tensor var_7685_cast = reduce_mean(axes = var_7684, keep_dims = var_7582, x = zero_mean_sq_79_cast)[name = tensor("op_7685_cast")]; + tensor var_7686_to_fp16 = const()[name = tensor("op_7686_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7687_cast = add(x = var_7685_cast, y = var_7686_to_fp16)[name = tensor("op_7687_cast")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_7687_cast)[name = tensor("denom_79_cast")]; + tensor out_79_cast = mul(x = zero_mean_79_cast, y = denom_79_cast)[name = tensor("out_79_cast")]; + tensor var_7691_to_fp16 = const()[name = tensor("op_7691_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634394688)))]; + tensor var_7692_cast = add(x = out_79_cast, y = var_7691_to_fp16)[name = tensor("op_7692_cast")]; + tensor var_7694_to_fp16 = const()[name = tensor("op_7694_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634395392)))]; + tensor hidden_states_287_cast = mul(x = var_7692_cast, y = var_7694_to_fp16)[name = tensor("hidden_states_287_cast")]; + tensor var_7701 = const()[name = tensor("op_7701"), val = tensor([1, 1])]; + tensor var_7703 = const()[name = tensor("op_7703"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634396096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634472960))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_53_cast = conv(dilations = var_7703, groups = var_7587, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_7701, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor("q_53_cast")]; + tensor var_7707 = const()[name = tensor("op_7707"), val = tensor([1, 1])]; + tensor var_7709 = const()[name = tensor("op_7709"), val = tensor([1, 1])]; + tensor k_105_pad_type_0 = const()[name = tensor("k_105_pad_type_0"), val = tensor("custom")]; + tensor k_105_pad_0 = const()[name = tensor("k_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634473152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634550016))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_105_cast = conv(dilations = var_7709, groups = var_7587, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_7707, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor("k_105_cast")]; + tensor var_7713 = const()[name = tensor("op_7713"), val = tensor([1, 1])]; + tensor var_7715 = const()[name = tensor("op_7715"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634550208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634627072))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_53_cast = conv(dilations = var_7715, groups = var_7587, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_7713, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor("v_53_cast")]; + tensor var_7719_begin_0 = const()[name = tensor("op_7719_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7719_end_0 = const()[name = tensor("op_7719_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_7719_end_mask_0 = const()[name = tensor("op_7719_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7719_cast = slice_by_index(begin = var_7719_begin_0, end = var_7719_end_0, end_mask = var_7719_end_mask_0, x = q_53_cast)[name = tensor("op_7719_cast")]; + tensor var_7723_begin_0 = const()[name = tensor("op_7723_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_7723_end_0 = const()[name = tensor("op_7723_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_7723_end_mask_0 = const()[name = tensor("op_7723_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7723_cast = slice_by_index(begin = var_7723_begin_0, end = var_7723_end_0, end_mask = var_7723_end_mask_0, x = q_53_cast)[name = tensor("op_7723_cast")]; + tensor var_7727_begin_0 = const()[name = tensor("op_7727_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7727_end_0 = const()[name = tensor("op_7727_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_7727_end_mask_0 = const()[name = tensor("op_7727_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7727_cast = slice_by_index(begin = var_7727_begin_0, end = var_7727_end_0, end_mask = var_7727_end_mask_0, x = q_53_cast)[name = tensor("op_7727_cast")]; + tensor var_7731_begin_0 = const()[name = tensor("op_7731_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_7731_end_0 = const()[name = tensor("op_7731_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_7731_end_mask_0 = const()[name = tensor("op_7731_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7731_cast = slice_by_index(begin = var_7731_begin_0, end = var_7731_end_0, end_mask = var_7731_end_mask_0, x = q_53_cast)[name = tensor("op_7731_cast")]; + tensor var_7735_begin_0 = const()[name = tensor("op_7735_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_7735_end_0 = const()[name = tensor("op_7735_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_7735_end_mask_0 = const()[name = tensor("op_7735_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7735_cast = slice_by_index(begin = var_7735_begin_0, end = var_7735_end_0, end_mask = var_7735_end_mask_0, x = q_53_cast)[name = tensor("op_7735_cast")]; + tensor var_7739_begin_0 = const()[name = tensor("op_7739_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_7739_end_0 = const()[name = tensor("op_7739_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_7739_end_mask_0 = const()[name = tensor("op_7739_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7739_cast = slice_by_index(begin = var_7739_begin_0, end = var_7739_end_0, end_mask = var_7739_end_mask_0, x = q_53_cast)[name = tensor("op_7739_cast")]; + tensor var_7743_begin_0 = const()[name = tensor("op_7743_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_7743_end_0 = const()[name = tensor("op_7743_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_7743_end_mask_0 = const()[name = tensor("op_7743_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7743_cast = slice_by_index(begin = var_7743_begin_0, end = var_7743_end_0, end_mask = var_7743_end_mask_0, x = q_53_cast)[name = tensor("op_7743_cast")]; + tensor var_7747_begin_0 = const()[name = tensor("op_7747_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_7747_end_0 = const()[name = tensor("op_7747_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_7747_end_mask_0 = const()[name = tensor("op_7747_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7747_cast = slice_by_index(begin = var_7747_begin_0, end = var_7747_end_0, end_mask = var_7747_end_mask_0, x = q_53_cast)[name = tensor("op_7747_cast")]; + tensor k_107_perm_0 = const()[name = tensor("k_107_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_7754_begin_0 = const()[name = tensor("op_7754_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7754_end_0 = const()[name = tensor("op_7754_end_0"), val = tensor([2, 9216, 1, 40])]; + tensor var_7754_end_mask_0 = const()[name = tensor("op_7754_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_5 = transpose(perm = k_107_perm_0, x = k_105_cast)[name = tensor("transpose_5")]; + tensor var_7754_cast = slice_by_index(begin = var_7754_begin_0, end = var_7754_end_0, end_mask = var_7754_end_mask_0, x = transpose_5)[name = tensor("op_7754_cast")]; + tensor var_7758_begin_0 = const()[name = tensor("op_7758_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_7758_end_0 = const()[name = tensor("op_7758_end_0"), val = tensor([2, 9216, 1, 80])]; + tensor var_7758_end_mask_0 = const()[name = tensor("op_7758_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7758_cast = slice_by_index(begin = var_7758_begin_0, end = var_7758_end_0, end_mask = var_7758_end_mask_0, x = transpose_5)[name = tensor("op_7758_cast")]; + tensor var_7762_begin_0 = const()[name = tensor("op_7762_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_7762_end_0 = const()[name = tensor("op_7762_end_0"), val = tensor([2, 9216, 1, 120])]; + tensor var_7762_end_mask_0 = const()[name = tensor("op_7762_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7762_cast = slice_by_index(begin = var_7762_begin_0, end = var_7762_end_0, end_mask = var_7762_end_mask_0, x = transpose_5)[name = tensor("op_7762_cast")]; + tensor var_7766_begin_0 = const()[name = tensor("op_7766_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_7766_end_0 = const()[name = tensor("op_7766_end_0"), val = tensor([2, 9216, 1, 160])]; + tensor var_7766_end_mask_0 = const()[name = tensor("op_7766_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7766_cast = slice_by_index(begin = var_7766_begin_0, end = var_7766_end_0, end_mask = var_7766_end_mask_0, x = transpose_5)[name = tensor("op_7766_cast")]; + tensor var_7770_begin_0 = const()[name = tensor("op_7770_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_7770_end_0 = const()[name = tensor("op_7770_end_0"), val = tensor([2, 9216, 1, 200])]; + tensor var_7770_end_mask_0 = const()[name = tensor("op_7770_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7770_cast = slice_by_index(begin = var_7770_begin_0, end = var_7770_end_0, end_mask = var_7770_end_mask_0, x = transpose_5)[name = tensor("op_7770_cast")]; + tensor var_7774_begin_0 = const()[name = tensor("op_7774_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_7774_end_0 = const()[name = tensor("op_7774_end_0"), val = tensor([2, 9216, 1, 240])]; + tensor var_7774_end_mask_0 = const()[name = tensor("op_7774_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7774_cast = slice_by_index(begin = var_7774_begin_0, end = var_7774_end_0, end_mask = var_7774_end_mask_0, x = transpose_5)[name = tensor("op_7774_cast")]; + tensor var_7778_begin_0 = const()[name = tensor("op_7778_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_7778_end_0 = const()[name = tensor("op_7778_end_0"), val = tensor([2, 9216, 1, 280])]; + tensor var_7778_end_mask_0 = const()[name = tensor("op_7778_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7778_cast = slice_by_index(begin = var_7778_begin_0, end = var_7778_end_0, end_mask = var_7778_end_mask_0, x = transpose_5)[name = tensor("op_7778_cast")]; + tensor var_7782_begin_0 = const()[name = tensor("op_7782_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_7782_end_0 = const()[name = tensor("op_7782_end_0"), val = tensor([2, 9216, 1, 320])]; + tensor var_7782_end_mask_0 = const()[name = tensor("op_7782_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7782_cast = slice_by_index(begin = var_7782_begin_0, end = var_7782_end_0, end_mask = var_7782_end_mask_0, x = transpose_5)[name = tensor("op_7782_cast")]; + tensor var_7784_begin_0 = const()[name = tensor("op_7784_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7784_end_0 = const()[name = tensor("op_7784_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_7784_end_mask_0 = const()[name = tensor("op_7784_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7784_cast = slice_by_index(begin = var_7784_begin_0, end = var_7784_end_0, end_mask = var_7784_end_mask_0, x = v_53_cast)[name = tensor("op_7784_cast")]; + tensor var_7788_begin_0 = const()[name = tensor("op_7788_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_7788_end_0 = const()[name = tensor("op_7788_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_7788_end_mask_0 = const()[name = tensor("op_7788_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7788_cast = slice_by_index(begin = var_7788_begin_0, end = var_7788_end_0, end_mask = var_7788_end_mask_0, x = v_53_cast)[name = tensor("op_7788_cast")]; + tensor var_7792_begin_0 = const()[name = tensor("op_7792_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7792_end_0 = const()[name = tensor("op_7792_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_7792_end_mask_0 = const()[name = tensor("op_7792_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7792_cast = slice_by_index(begin = var_7792_begin_0, end = var_7792_end_0, end_mask = var_7792_end_mask_0, x = v_53_cast)[name = tensor("op_7792_cast")]; + tensor var_7796_begin_0 = const()[name = tensor("op_7796_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_7796_end_0 = const()[name = tensor("op_7796_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_7796_end_mask_0 = const()[name = tensor("op_7796_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7796_cast = slice_by_index(begin = var_7796_begin_0, end = var_7796_end_0, end_mask = var_7796_end_mask_0, x = v_53_cast)[name = tensor("op_7796_cast")]; + tensor var_7800_begin_0 = const()[name = tensor("op_7800_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_7800_end_0 = const()[name = tensor("op_7800_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_7800_end_mask_0 = const()[name = tensor("op_7800_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7800_cast = slice_by_index(begin = var_7800_begin_0, end = var_7800_end_0, end_mask = var_7800_end_mask_0, x = v_53_cast)[name = tensor("op_7800_cast")]; + tensor var_7804_begin_0 = const()[name = tensor("op_7804_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_7804_end_0 = const()[name = tensor("op_7804_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_7804_end_mask_0 = const()[name = tensor("op_7804_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7804_cast = slice_by_index(begin = var_7804_begin_0, end = var_7804_end_0, end_mask = var_7804_end_mask_0, x = v_53_cast)[name = tensor("op_7804_cast")]; + tensor var_7808_begin_0 = const()[name = tensor("op_7808_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_7808_end_0 = const()[name = tensor("op_7808_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_7808_end_mask_0 = const()[name = tensor("op_7808_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7808_cast = slice_by_index(begin = var_7808_begin_0, end = var_7808_end_0, end_mask = var_7808_end_mask_0, x = v_53_cast)[name = tensor("op_7808_cast")]; + tensor var_7812_begin_0 = const()[name = tensor("op_7812_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_7812_end_0 = const()[name = tensor("op_7812_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_7812_end_mask_0 = const()[name = tensor("op_7812_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7812_cast = slice_by_index(begin = var_7812_begin_0, end = var_7812_end_0, end_mask = var_7812_end_mask_0, x = v_53_cast)[name = tensor("op_7812_cast")]; + tensor var_7816_equation_0 = const()[name = tensor("op_7816_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7816_cast = einsum(equation = var_7816_equation_0, values = (var_7754_cast, var_7719_cast))[name = tensor("op_7816_cast")]; + tensor var_7817_to_fp16 = const()[name = tensor("op_7817_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_417_cast = mul(x = var_7816_cast, y = var_7817_to_fp16)[name = tensor("aw_417_cast")]; + tensor var_7820_equation_0 = const()[name = tensor("op_7820_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7820_cast = einsum(equation = var_7820_equation_0, values = (var_7758_cast, var_7723_cast))[name = tensor("op_7820_cast")]; + tensor var_7821_to_fp16 = const()[name = tensor("op_7821_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_419_cast = mul(x = var_7820_cast, y = var_7821_to_fp16)[name = tensor("aw_419_cast")]; + tensor var_7824_equation_0 = const()[name = tensor("op_7824_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7824_cast = einsum(equation = var_7824_equation_0, values = (var_7762_cast, var_7727_cast))[name = tensor("op_7824_cast")]; + tensor var_7825_to_fp16 = const()[name = tensor("op_7825_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_421_cast = mul(x = var_7824_cast, y = var_7825_to_fp16)[name = tensor("aw_421_cast")]; + tensor var_7828_equation_0 = const()[name = tensor("op_7828_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7828_cast = einsum(equation = var_7828_equation_0, values = (var_7766_cast, var_7731_cast))[name = tensor("op_7828_cast")]; + tensor var_7829_to_fp16 = const()[name = tensor("op_7829_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_423_cast = mul(x = var_7828_cast, y = var_7829_to_fp16)[name = tensor("aw_423_cast")]; + tensor var_7832_equation_0 = const()[name = tensor("op_7832_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7832_cast = einsum(equation = var_7832_equation_0, values = (var_7770_cast, var_7735_cast))[name = tensor("op_7832_cast")]; + tensor var_7833_to_fp16 = const()[name = tensor("op_7833_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_425_cast = mul(x = var_7832_cast, y = var_7833_to_fp16)[name = tensor("aw_425_cast")]; + tensor var_7836_equation_0 = const()[name = tensor("op_7836_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7836_cast = einsum(equation = var_7836_equation_0, values = (var_7774_cast, var_7739_cast))[name = tensor("op_7836_cast")]; + tensor var_7837_to_fp16 = const()[name = tensor("op_7837_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_427_cast = mul(x = var_7836_cast, y = var_7837_to_fp16)[name = tensor("aw_427_cast")]; + tensor var_7840_equation_0 = const()[name = tensor("op_7840_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7840_cast = einsum(equation = var_7840_equation_0, values = (var_7778_cast, var_7743_cast))[name = tensor("op_7840_cast")]; + tensor var_7841_to_fp16 = const()[name = tensor("op_7841_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_429_cast = mul(x = var_7840_cast, y = var_7841_to_fp16)[name = tensor("aw_429_cast")]; + tensor var_7844_equation_0 = const()[name = tensor("op_7844_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_7844_cast = einsum(equation = var_7844_equation_0, values = (var_7782_cast, var_7747_cast))[name = tensor("op_7844_cast")]; + tensor var_7845_to_fp16 = const()[name = tensor("op_7845_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_431_cast = mul(x = var_7844_cast, y = var_7845_to_fp16)[name = tensor("aw_431_cast")]; + tensor var_7847_cast = softmax(axis = var_7587, x = aw_417_cast)[name = tensor("op_7847_cast")]; + tensor var_7848_cast = softmax(axis = var_7587, x = aw_419_cast)[name = tensor("op_7848_cast")]; + tensor var_7849_cast = softmax(axis = var_7587, x = aw_421_cast)[name = tensor("op_7849_cast")]; + tensor var_7850_cast = softmax(axis = var_7587, x = aw_423_cast)[name = tensor("op_7850_cast")]; + tensor var_7851_cast = softmax(axis = var_7587, x = aw_425_cast)[name = tensor("op_7851_cast")]; + tensor var_7852_cast = softmax(axis = var_7587, x = aw_427_cast)[name = tensor("op_7852_cast")]; + tensor var_7853_cast = softmax(axis = var_7587, x = aw_429_cast)[name = tensor("op_7853_cast")]; + tensor var_7854_cast = softmax(axis = var_7587, x = aw_431_cast)[name = tensor("op_7854_cast")]; + tensor var_7856_equation_0 = const()[name = tensor("op_7856_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7856_cast = einsum(equation = var_7856_equation_0, values = (var_7784_cast, var_7847_cast))[name = tensor("op_7856_cast")]; + tensor var_7858_equation_0 = const()[name = tensor("op_7858_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7858_cast = einsum(equation = var_7858_equation_0, values = (var_7788_cast, var_7848_cast))[name = tensor("op_7858_cast")]; + tensor var_7860_equation_0 = const()[name = tensor("op_7860_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7860_cast = einsum(equation = var_7860_equation_0, values = (var_7792_cast, var_7849_cast))[name = tensor("op_7860_cast")]; + tensor var_7862_equation_0 = const()[name = tensor("op_7862_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7862_cast = einsum(equation = var_7862_equation_0, values = (var_7796_cast, var_7850_cast))[name = tensor("op_7862_cast")]; + tensor var_7864_equation_0 = const()[name = tensor("op_7864_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7864_cast = einsum(equation = var_7864_equation_0, values = (var_7800_cast, var_7851_cast))[name = tensor("op_7864_cast")]; + tensor var_7866_equation_0 = const()[name = tensor("op_7866_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7866_cast = einsum(equation = var_7866_equation_0, values = (var_7804_cast, var_7852_cast))[name = tensor("op_7866_cast")]; + tensor var_7868_equation_0 = const()[name = tensor("op_7868_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7868_cast = einsum(equation = var_7868_equation_0, values = (var_7808_cast, var_7853_cast))[name = tensor("op_7868_cast")]; + tensor var_7870_equation_0 = const()[name = tensor("op_7870_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_7870_cast = einsum(equation = var_7870_equation_0, values = (var_7812_cast, var_7854_cast))[name = tensor("op_7870_cast")]; + tensor input_465_interleave_0 = const()[name = tensor("input_465_interleave_0"), val = tensor(false)]; + tensor input_465_cast = concat(axis = var_7587, interleave = input_465_interleave_0, values = (var_7856_cast, var_7858_cast, var_7860_cast, var_7862_cast, var_7864_cast, var_7866_cast, var_7868_cast, var_7870_cast))[name = tensor("input_465_cast")]; + tensor var_7876 = const()[name = tensor("op_7876"), val = tensor([1, 1])]; + tensor var_7878 = const()[name = tensor("op_7878"), val = tensor([1, 1])]; + tensor var_7880_pad_type_0 = const()[name = tensor("op_7880_pad_type_0"), val = tensor("custom")]; + tensor var_7880_pad_0 = const()[name = tensor("op_7880_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634627264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634704128))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634704320)))]; + tensor var_7880_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_7878, groups = var_7587, pad = var_7880_pad_0, pad_type = var_7880_pad_type_0, strides = var_7876, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_465_cast)[name = tensor("op_7880_cast")]; + tensor inputs_81_cast = add(x = var_7880_cast, y = inputs_79_cast)[name = tensor("inputs_81_cast")]; + tensor var_7884 = const()[name = tensor("op_7884"), val = tensor([1])]; + tensor channels_mean_81_cast = reduce_mean(axes = var_7884, keep_dims = var_7582, x = inputs_81_cast)[name = tensor("channels_mean_81_cast")]; + tensor zero_mean_81_cast = sub(x = inputs_81_cast, y = channels_mean_81_cast)[name = tensor("zero_mean_81_cast")]; + tensor zero_mean_sq_81_cast = mul(x = zero_mean_81_cast, y = zero_mean_81_cast)[name = tensor("zero_mean_sq_81_cast")]; + tensor var_7888 = const()[name = tensor("op_7888"), val = tensor([1])]; + tensor var_7889_cast = reduce_mean(axes = var_7888, keep_dims = var_7582, x = zero_mean_sq_81_cast)[name = tensor("op_7889_cast")]; + tensor var_7890_to_fp16 = const()[name = tensor("op_7890_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7891_cast = add(x = var_7889_cast, y = var_7890_to_fp16)[name = tensor("op_7891_cast")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_7891_cast)[name = tensor("denom_81_cast")]; + tensor out_81_cast = mul(x = zero_mean_81_cast, y = denom_81_cast)[name = tensor("out_81_cast")]; + tensor var_7895_to_fp16 = const()[name = tensor("op_7895_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634705024)))]; + tensor var_7896_cast = add(x = out_81_cast, y = var_7895_to_fp16)[name = tensor("op_7896_cast")]; + tensor var_7898_to_fp16 = const()[name = tensor("op_7898_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634705728)))]; + tensor hidden_states_289_cast = mul(x = var_7896_cast, y = var_7898_to_fp16)[name = tensor("hidden_states_289_cast")]; + tensor var_7905 = const()[name = tensor("op_7905"), val = tensor([1, 1])]; + tensor var_7907 = const()[name = tensor("op_7907"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634706432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634783296))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_55_cast = conv(dilations = var_7907, groups = var_7587, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_7905, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_289_cast)[name = tensor("q_55_cast")]; + tensor var_7911 = const()[name = tensor("op_7911"), val = tensor([1, 1])]; + tensor var_7913 = const()[name = tensor("op_7913"), val = tensor([1, 1])]; + tensor k_109_pad_type_0 = const()[name = tensor("k_109_pad_type_0"), val = tensor("custom")]; + tensor k_109_pad_0 = const()[name = tensor("k_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634783488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634967872))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_109_cast = conv(dilations = var_7913, groups = var_7587, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_7911, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_109_cast")]; + tensor var_7917 = const()[name = tensor("op_7917"), val = tensor([1, 1])]; + tensor var_7919 = const()[name = tensor("op_7919"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634968064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635152448))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_55_cast = conv(dilations = var_7919, groups = var_7587, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_7917, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_55_cast")]; + tensor var_7923_begin_0 = const()[name = tensor("op_7923_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7923_end_0 = const()[name = tensor("op_7923_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_7923_end_mask_0 = const()[name = tensor("op_7923_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7923_cast = slice_by_index(begin = var_7923_begin_0, end = var_7923_end_0, end_mask = var_7923_end_mask_0, x = q_55_cast)[name = tensor("op_7923_cast")]; + tensor var_7927_begin_0 = const()[name = tensor("op_7927_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_7927_end_0 = const()[name = tensor("op_7927_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_7927_end_mask_0 = const()[name = tensor("op_7927_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7927_cast = slice_by_index(begin = var_7927_begin_0, end = var_7927_end_0, end_mask = var_7927_end_mask_0, x = q_55_cast)[name = tensor("op_7927_cast")]; + tensor var_7931_begin_0 = const()[name = tensor("op_7931_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7931_end_0 = const()[name = tensor("op_7931_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_7931_end_mask_0 = const()[name = tensor("op_7931_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7931_cast = slice_by_index(begin = var_7931_begin_0, end = var_7931_end_0, end_mask = var_7931_end_mask_0, x = q_55_cast)[name = tensor("op_7931_cast")]; + tensor var_7935_begin_0 = const()[name = tensor("op_7935_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_7935_end_0 = const()[name = tensor("op_7935_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_7935_end_mask_0 = const()[name = tensor("op_7935_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7935_cast = slice_by_index(begin = var_7935_begin_0, end = var_7935_end_0, end_mask = var_7935_end_mask_0, x = q_55_cast)[name = tensor("op_7935_cast")]; + tensor var_7939_begin_0 = const()[name = tensor("op_7939_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_7939_end_0 = const()[name = tensor("op_7939_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_7939_end_mask_0 = const()[name = tensor("op_7939_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7939_cast = slice_by_index(begin = var_7939_begin_0, end = var_7939_end_0, end_mask = var_7939_end_mask_0, x = q_55_cast)[name = tensor("op_7939_cast")]; + tensor var_7943_begin_0 = const()[name = tensor("op_7943_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_7943_end_0 = const()[name = tensor("op_7943_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_7943_end_mask_0 = const()[name = tensor("op_7943_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7943_cast = slice_by_index(begin = var_7943_begin_0, end = var_7943_end_0, end_mask = var_7943_end_mask_0, x = q_55_cast)[name = tensor("op_7943_cast")]; + tensor var_7947_begin_0 = const()[name = tensor("op_7947_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_7947_end_0 = const()[name = tensor("op_7947_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_7947_end_mask_0 = const()[name = tensor("op_7947_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7947_cast = slice_by_index(begin = var_7947_begin_0, end = var_7947_end_0, end_mask = var_7947_end_mask_0, x = q_55_cast)[name = tensor("op_7947_cast")]; + tensor var_7951_begin_0 = const()[name = tensor("op_7951_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_7951_end_0 = const()[name = tensor("op_7951_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_7951_end_mask_0 = const()[name = tensor("op_7951_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7951_cast = slice_by_index(begin = var_7951_begin_0, end = var_7951_end_0, end_mask = var_7951_end_mask_0, x = q_55_cast)[name = tensor("op_7951_cast")]; + tensor k_111_perm_0 = const()[name = tensor("k_111_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_7958_begin_0 = const()[name = tensor("op_7958_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7958_end_0 = const()[name = tensor("op_7958_end_0"), val = tensor([2, 77, 1, 40])]; + tensor var_7958_end_mask_0 = const()[name = tensor("op_7958_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_4 = transpose(perm = k_111_perm_0, x = k_109_cast)[name = tensor("transpose_4")]; + tensor var_7958_cast = slice_by_index(begin = var_7958_begin_0, end = var_7958_end_0, end_mask = var_7958_end_mask_0, x = transpose_4)[name = tensor("op_7958_cast")]; + tensor var_7962_begin_0 = const()[name = tensor("op_7962_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_7962_end_0 = const()[name = tensor("op_7962_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_7962_end_mask_0 = const()[name = tensor("op_7962_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7962_cast = slice_by_index(begin = var_7962_begin_0, end = var_7962_end_0, end_mask = var_7962_end_mask_0, x = transpose_4)[name = tensor("op_7962_cast")]; + tensor var_7966_begin_0 = const()[name = tensor("op_7966_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_7966_end_0 = const()[name = tensor("op_7966_end_0"), val = tensor([2, 77, 1, 120])]; + tensor var_7966_end_mask_0 = const()[name = tensor("op_7966_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7966_cast = slice_by_index(begin = var_7966_begin_0, end = var_7966_end_0, end_mask = var_7966_end_mask_0, x = transpose_4)[name = tensor("op_7966_cast")]; + tensor var_7970_begin_0 = const()[name = tensor("op_7970_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_7970_end_0 = const()[name = tensor("op_7970_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_7970_end_mask_0 = const()[name = tensor("op_7970_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7970_cast = slice_by_index(begin = var_7970_begin_0, end = var_7970_end_0, end_mask = var_7970_end_mask_0, x = transpose_4)[name = tensor("op_7970_cast")]; + tensor var_7974_begin_0 = const()[name = tensor("op_7974_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_7974_end_0 = const()[name = tensor("op_7974_end_0"), val = tensor([2, 77, 1, 200])]; + tensor var_7974_end_mask_0 = const()[name = tensor("op_7974_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7974_cast = slice_by_index(begin = var_7974_begin_0, end = var_7974_end_0, end_mask = var_7974_end_mask_0, x = transpose_4)[name = tensor("op_7974_cast")]; + tensor var_7978_begin_0 = const()[name = tensor("op_7978_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_7978_end_0 = const()[name = tensor("op_7978_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_7978_end_mask_0 = const()[name = tensor("op_7978_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7978_cast = slice_by_index(begin = var_7978_begin_0, end = var_7978_end_0, end_mask = var_7978_end_mask_0, x = transpose_4)[name = tensor("op_7978_cast")]; + tensor var_7982_begin_0 = const()[name = tensor("op_7982_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_7982_end_0 = const()[name = tensor("op_7982_end_0"), val = tensor([2, 77, 1, 280])]; + tensor var_7982_end_mask_0 = const()[name = tensor("op_7982_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7982_cast = slice_by_index(begin = var_7982_begin_0, end = var_7982_end_0, end_mask = var_7982_end_mask_0, x = transpose_4)[name = tensor("op_7982_cast")]; + tensor var_7986_begin_0 = const()[name = tensor("op_7986_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_7986_end_0 = const()[name = tensor("op_7986_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_7986_end_mask_0 = const()[name = tensor("op_7986_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_7986_cast = slice_by_index(begin = var_7986_begin_0, end = var_7986_end_0, end_mask = var_7986_end_mask_0, x = transpose_4)[name = tensor("op_7986_cast")]; + tensor var_7988_begin_0 = const()[name = tensor("op_7988_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_7988_end_0 = const()[name = tensor("op_7988_end_0"), val = tensor([2, 40, 1, 77])]; + tensor var_7988_end_mask_0 = const()[name = tensor("op_7988_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7988_cast = slice_by_index(begin = var_7988_begin_0, end = var_7988_end_0, end_mask = var_7988_end_mask_0, x = v_55_cast)[name = tensor("op_7988_cast")]; + tensor var_7992_begin_0 = const()[name = tensor("op_7992_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_7992_end_0 = const()[name = tensor("op_7992_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_7992_end_mask_0 = const()[name = tensor("op_7992_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7992_cast = slice_by_index(begin = var_7992_begin_0, end = var_7992_end_0, end_mask = var_7992_end_mask_0, x = v_55_cast)[name = tensor("op_7992_cast")]; + tensor var_7996_begin_0 = const()[name = tensor("op_7996_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_7996_end_0 = const()[name = tensor("op_7996_end_0"), val = tensor([2, 120, 1, 77])]; + tensor var_7996_end_mask_0 = const()[name = tensor("op_7996_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_7996_cast = slice_by_index(begin = var_7996_begin_0, end = var_7996_end_0, end_mask = var_7996_end_mask_0, x = v_55_cast)[name = tensor("op_7996_cast")]; + tensor var_8000_begin_0 = const()[name = tensor("op_8000_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_8000_end_0 = const()[name = tensor("op_8000_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_8000_end_mask_0 = const()[name = tensor("op_8000_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8000_cast = slice_by_index(begin = var_8000_begin_0, end = var_8000_end_0, end_mask = var_8000_end_mask_0, x = v_55_cast)[name = tensor("op_8000_cast")]; + tensor var_8004_begin_0 = const()[name = tensor("op_8004_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_8004_end_0 = const()[name = tensor("op_8004_end_0"), val = tensor([2, 200, 1, 77])]; + tensor var_8004_end_mask_0 = const()[name = tensor("op_8004_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8004_cast = slice_by_index(begin = var_8004_begin_0, end = var_8004_end_0, end_mask = var_8004_end_mask_0, x = v_55_cast)[name = tensor("op_8004_cast")]; + tensor var_8008_begin_0 = const()[name = tensor("op_8008_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_8008_end_0 = const()[name = tensor("op_8008_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_8008_end_mask_0 = const()[name = tensor("op_8008_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8008_cast = slice_by_index(begin = var_8008_begin_0, end = var_8008_end_0, end_mask = var_8008_end_mask_0, x = v_55_cast)[name = tensor("op_8008_cast")]; + tensor var_8012_begin_0 = const()[name = tensor("op_8012_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_8012_end_0 = const()[name = tensor("op_8012_end_0"), val = tensor([2, 280, 1, 77])]; + tensor var_8012_end_mask_0 = const()[name = tensor("op_8012_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8012_cast = slice_by_index(begin = var_8012_begin_0, end = var_8012_end_0, end_mask = var_8012_end_mask_0, x = v_55_cast)[name = tensor("op_8012_cast")]; + tensor var_8016_begin_0 = const()[name = tensor("op_8016_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_8016_end_0 = const()[name = tensor("op_8016_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_8016_end_mask_0 = const()[name = tensor("op_8016_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8016_cast = slice_by_index(begin = var_8016_begin_0, end = var_8016_end_0, end_mask = var_8016_end_mask_0, x = v_55_cast)[name = tensor("op_8016_cast")]; + tensor var_8020_equation_0 = const()[name = tensor("op_8020_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8020_cast = einsum(equation = var_8020_equation_0, values = (var_7958_cast, var_7923_cast))[name = tensor("op_8020_cast")]; + tensor var_8021_to_fp16 = const()[name = tensor("op_8021_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_433_cast = mul(x = var_8020_cast, y = var_8021_to_fp16)[name = tensor("aw_433_cast")]; + tensor var_8024_equation_0 = const()[name = tensor("op_8024_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8024_cast = einsum(equation = var_8024_equation_0, values = (var_7962_cast, var_7927_cast))[name = tensor("op_8024_cast")]; + tensor var_8025_to_fp16 = const()[name = tensor("op_8025_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_435_cast = mul(x = var_8024_cast, y = var_8025_to_fp16)[name = tensor("aw_435_cast")]; + tensor var_8028_equation_0 = const()[name = tensor("op_8028_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8028_cast = einsum(equation = var_8028_equation_0, values = (var_7966_cast, var_7931_cast))[name = tensor("op_8028_cast")]; + tensor var_8029_to_fp16 = const()[name = tensor("op_8029_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_437_cast = mul(x = var_8028_cast, y = var_8029_to_fp16)[name = tensor("aw_437_cast")]; + tensor var_8032_equation_0 = const()[name = tensor("op_8032_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8032_cast = einsum(equation = var_8032_equation_0, values = (var_7970_cast, var_7935_cast))[name = tensor("op_8032_cast")]; + tensor var_8033_to_fp16 = const()[name = tensor("op_8033_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_439_cast = mul(x = var_8032_cast, y = var_8033_to_fp16)[name = tensor("aw_439_cast")]; + tensor var_8036_equation_0 = const()[name = tensor("op_8036_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8036_cast = einsum(equation = var_8036_equation_0, values = (var_7974_cast, var_7939_cast))[name = tensor("op_8036_cast")]; + tensor var_8037_to_fp16 = const()[name = tensor("op_8037_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_441_cast = mul(x = var_8036_cast, y = var_8037_to_fp16)[name = tensor("aw_441_cast")]; + tensor var_8040_equation_0 = const()[name = tensor("op_8040_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8040_cast = einsum(equation = var_8040_equation_0, values = (var_7978_cast, var_7943_cast))[name = tensor("op_8040_cast")]; + tensor var_8041_to_fp16 = const()[name = tensor("op_8041_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_443_cast = mul(x = var_8040_cast, y = var_8041_to_fp16)[name = tensor("aw_443_cast")]; + tensor var_8044_equation_0 = const()[name = tensor("op_8044_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8044_cast = einsum(equation = var_8044_equation_0, values = (var_7982_cast, var_7947_cast))[name = tensor("op_8044_cast")]; + tensor var_8045_to_fp16 = const()[name = tensor("op_8045_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_445_cast = mul(x = var_8044_cast, y = var_8045_to_fp16)[name = tensor("aw_445_cast")]; + tensor var_8048_equation_0 = const()[name = tensor("op_8048_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8048_cast = einsum(equation = var_8048_equation_0, values = (var_7986_cast, var_7951_cast))[name = tensor("op_8048_cast")]; + tensor var_8049_to_fp16 = const()[name = tensor("op_8049_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_447_cast = mul(x = var_8048_cast, y = var_8049_to_fp16)[name = tensor("aw_447_cast")]; + tensor var_8051_cast = softmax(axis = var_7587, x = aw_433_cast)[name = tensor("op_8051_cast")]; + tensor var_8052_cast = softmax(axis = var_7587, x = aw_435_cast)[name = tensor("op_8052_cast")]; + tensor var_8053_cast = softmax(axis = var_7587, x = aw_437_cast)[name = tensor("op_8053_cast")]; + tensor var_8054_cast = softmax(axis = var_7587, x = aw_439_cast)[name = tensor("op_8054_cast")]; + tensor var_8055_cast = softmax(axis = var_7587, x = aw_441_cast)[name = tensor("op_8055_cast")]; + tensor var_8056_cast = softmax(axis = var_7587, x = aw_443_cast)[name = tensor("op_8056_cast")]; + tensor var_8057_cast = softmax(axis = var_7587, x = aw_445_cast)[name = tensor("op_8057_cast")]; + tensor var_8058_cast = softmax(axis = var_7587, x = aw_447_cast)[name = tensor("op_8058_cast")]; + tensor var_8060_equation_0 = const()[name = tensor("op_8060_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8060_cast = einsum(equation = var_8060_equation_0, values = (var_7988_cast, var_8051_cast))[name = tensor("op_8060_cast")]; + tensor var_8062_equation_0 = const()[name = tensor("op_8062_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8062_cast = einsum(equation = var_8062_equation_0, values = (var_7992_cast, var_8052_cast))[name = tensor("op_8062_cast")]; + tensor var_8064_equation_0 = const()[name = tensor("op_8064_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8064_cast = einsum(equation = var_8064_equation_0, values = (var_7996_cast, var_8053_cast))[name = tensor("op_8064_cast")]; + tensor var_8066_equation_0 = const()[name = tensor("op_8066_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8066_cast = einsum(equation = var_8066_equation_0, values = (var_8000_cast, var_8054_cast))[name = tensor("op_8066_cast")]; + tensor var_8068_equation_0 = const()[name = tensor("op_8068_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8068_cast = einsum(equation = var_8068_equation_0, values = (var_8004_cast, var_8055_cast))[name = tensor("op_8068_cast")]; + tensor var_8070_equation_0 = const()[name = tensor("op_8070_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8070_cast = einsum(equation = var_8070_equation_0, values = (var_8008_cast, var_8056_cast))[name = tensor("op_8070_cast")]; + tensor var_8072_equation_0 = const()[name = tensor("op_8072_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8072_cast = einsum(equation = var_8072_equation_0, values = (var_8012_cast, var_8057_cast))[name = tensor("op_8072_cast")]; + tensor var_8074_equation_0 = const()[name = tensor("op_8074_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8074_cast = einsum(equation = var_8074_equation_0, values = (var_8016_cast, var_8058_cast))[name = tensor("op_8074_cast")]; + tensor input_467_interleave_0 = const()[name = tensor("input_467_interleave_0"), val = tensor(false)]; + tensor input_467_cast = concat(axis = var_7587, interleave = input_467_interleave_0, values = (var_8060_cast, var_8062_cast, var_8064_cast, var_8066_cast, var_8068_cast, var_8070_cast, var_8072_cast, var_8074_cast))[name = tensor("input_467_cast")]; + tensor var_8080 = const()[name = tensor("op_8080"), val = tensor([1, 1])]; + tensor var_8082 = const()[name = tensor("op_8082"), val = tensor([1, 1])]; + tensor var_8084_pad_type_0 = const()[name = tensor("op_8084_pad_type_0"), val = tensor("custom")]; + tensor var_8084_pad_0 = const()[name = tensor("op_8084_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635152640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635229504))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635229696)))]; + tensor var_8084_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8082, groups = var_7587, pad = var_8084_pad_0, pad_type = var_8084_pad_type_0, strides = var_8080, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_467_cast)[name = tensor("op_8084_cast")]; + tensor inputs_83_cast = add(x = var_8084_cast, y = inputs_81_cast)[name = tensor("inputs_83_cast")]; + tensor var_8088 = const()[name = tensor("op_8088"), val = tensor([1])]; + tensor channels_mean_83_cast = reduce_mean(axes = var_8088, keep_dims = var_7582, x = inputs_83_cast)[name = tensor("channels_mean_83_cast")]; + tensor zero_mean_83_cast = sub(x = inputs_83_cast, y = channels_mean_83_cast)[name = tensor("zero_mean_83_cast")]; + tensor zero_mean_sq_83_cast = mul(x = zero_mean_83_cast, y = zero_mean_83_cast)[name = tensor("zero_mean_sq_83_cast")]; + tensor var_8092 = const()[name = tensor("op_8092"), val = tensor([1])]; + tensor var_8093_cast = reduce_mean(axes = var_8092, keep_dims = var_7582, x = zero_mean_sq_83_cast)[name = tensor("op_8093_cast")]; + tensor var_8094_to_fp16 = const()[name = tensor("op_8094_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8095_cast = add(x = var_8093_cast, y = var_8094_to_fp16)[name = tensor("op_8095_cast")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_8095_cast)[name = tensor("denom_83_cast")]; + tensor out_83_cast = mul(x = zero_mean_83_cast, y = denom_83_cast)[name = tensor("out_83_cast")]; + tensor var_8099_to_fp16 = const()[name = tensor("op_8099_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635230400)))]; + tensor var_8100_cast = add(x = out_83_cast, y = var_8099_to_fp16)[name = tensor("op_8100_cast")]; + tensor var_8102_to_fp16 = const()[name = tensor("op_8102_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635231104)))]; + tensor input_469_cast = mul(x = var_8100_cast, y = var_8102_to_fp16)[name = tensor("input_469_cast")]; + tensor var_8110 = const()[name = tensor("op_8110"), val = tensor([1, 1])]; + tensor var_8112 = const()[name = tensor("op_8112"), val = tensor([1, 1])]; + tensor var_8114_pad_type_0 = const()[name = tensor("op_8114_pad_type_0"), val = tensor("custom")]; + tensor var_8114_pad_0 = const()[name = tensor("op_8114_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635231808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635846272))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635846464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635848448))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_8114_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8112, groups = var_7587, pad = var_8114_pad_0, pad_type = var_8114_pad_type_0, strides = var_8110, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_469_cast)[name = tensor("op_8114_cast")]; + tensor var_8115_split_sizes_0 = const()[name = tensor("op_8115_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_8115_axis_0 = const()[name = tensor("op_8115_axis_0"), val = tensor(1)]; + tensor var_8115_cast_0, tensor var_8115_cast_1 = split(axis = var_8115_axis_0, split_sizes = var_8115_split_sizes_0, x = var_8114_cast)[name = tensor("op_8115_cast")]; + tensor var_8117_mode_0 = const()[name = tensor("op_8117_mode_0"), val = tensor("EXACT")]; + tensor var_8117_cast = gelu(mode = var_8117_mode_0, x = var_8115_cast_1)[name = tensor("op_8117_cast")]; + tensor input_471_cast = mul(x = var_8115_cast_0, y = var_8117_cast)[name = tensor("input_471_cast")]; + tensor var_8121 = const()[name = tensor("op_8121"), val = tensor([1, 1])]; + tensor var_8123 = const()[name = tensor("op_8123"), val = tensor([1, 1])]; + tensor var_8125_pad_type_0 = const()[name = tensor("op_8125_pad_type_0"), val = tensor("custom")]; + tensor var_8125_pad_0 = const()[name = tensor("op_8125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635848640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636155904))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636156096)))]; + tensor var_8125_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_8123, groups = var_7587, pad = var_8125_pad_0, pad_type = var_8125_pad_type_0, strides = var_8121, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_471_cast)[name = tensor("op_8125_cast")]; + tensor hidden_states_293_cast = add(x = var_8125_cast, y = inputs_83_cast)[name = tensor("hidden_states_293_cast")]; + tensor var_8127 = const()[name = tensor("op_8127"), val = tensor([2, 320, 96, 96])]; + tensor input_473_cast = reshape(shape = var_8127, x = hidden_states_293_cast)[name = tensor("input_473_cast")]; + tensor var_8131 = const()[name = tensor("op_8131"), val = tensor([1, 1])]; + tensor var_8133 = const()[name = tensor("op_8133"), val = tensor([1, 1])]; + tensor hidden_states_295_pad_type_0 = const()[name = tensor("hidden_states_295_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_295_pad_0 = const()[name = tensor("hidden_states_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636156800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636233664))), name = tensor("up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636233856)))]; + tensor hidden_states_295_cast = conv(bias = up_blocks_3_attentions_0_proj_out_bias_to_fp16, dilations = var_8133, groups = var_7587, pad = hidden_states_295_pad_0, pad_type = hidden_states_295_pad_type_0, strides = var_8131, weight = up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized, x = input_473_cast)[name = tensor("hidden_states_295_cast")]; + tensor hidden_states_297_cast = add(x = hidden_states_295_cast, y = hidden_states_283_cast)[name = tensor("hidden_states_297_cast")]; + tensor input_475_interleave_0 = const()[name = tensor("input_475_interleave_0"), val = tensor(false)]; + tensor input_475_cast = concat(axis = var_7587, interleave = input_475_interleave_0, values = (hidden_states_297_cast, input_35_cast))[name = tensor("input_475_cast")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([2, 32, 20, 96, 96])]; + tensor reshape_216_cast = reshape(shape = reshape_216_shape_0, x = input_475_cast)[name = tensor("reshape_216_cast")]; + tensor reduce_mean_162_axes_0 = const()[name = tensor("reduce_mean_162_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_162_keep_dims_0 = const()[name = tensor("reduce_mean_162_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_162_cast")]; + tensor sub_108_cast = sub(x = reshape_216_cast, y = reduce_mean_162_cast)[name = tensor("sub_108_cast")]; + tensor square_54_cast = square(x = sub_108_cast)[name = tensor("square_54_cast")]; + tensor reduce_mean_164_axes_0 = const()[name = tensor("reduce_mean_164_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_164_keep_dims_0 = const()[name = tensor("reduce_mean_164_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_164_cast")]; + tensor add_108_y_0_to_fp16 = const()[name = tensor("add_108_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_108_cast = add(x = reduce_mean_164_cast, y = add_108_y_0_to_fp16)[name = tensor("add_108_cast")]; + tensor sqrt_54_cast = sqrt(x = add_108_cast)[name = tensor("sqrt_54_cast")]; + tensor real_div_54_cast = real_div(x = sub_108_cast, y = sqrt_54_cast)[name = tensor("real_div_54_cast")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([2, 640, 96, 96])]; + tensor reshape_217_cast = reshape(shape = reshape_217_shape_0, x = real_div_54_cast)[name = tensor("reshape_217_cast")]; + tensor add_109_gamma_0_to_fp16 = const()[name = tensor("add_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636234560)))]; + tensor add_109_beta_0_to_fp16 = const()[name = tensor("add_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636235904)))]; + tensor add_109_epsilon_0_to_fp16 = const()[name = tensor("add_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_109_cast")]; + tensor input_479_cast = silu(x = add_109_cast)[name = tensor("input_479_cast")]; + tensor var_8151 = const()[name = tensor("op_8151"), val = tensor([1, 1])]; + tensor var_8153 = const()[name = tensor("op_8153"), val = tensor([1, 1])]; + tensor hidden_states_299_pad_type_0 = const()[name = tensor("hidden_states_299_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_299_pad_0 = const()[name = tensor("hidden_states_299_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636237248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637619712))), name = tensor("up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637619904)))]; + tensor hidden_states_299_cast = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_8153, groups = var_7587, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_8151, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_479_cast)[name = tensor("hidden_states_299_cast")]; + tensor var_8159 = const()[name = tensor("op_8159"), val = tensor([1, 1])]; + tensor var_8161 = const()[name = tensor("op_8161"), val = tensor([1, 1])]; + tensor temb_41_pad_type_0 = const()[name = tensor("temb_41_pad_type_0"), val = tensor("custom")]; + tensor temb_41_pad_0 = const()[name = tensor("temb_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637620608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637927872))), name = tensor("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637928064)))]; + tensor temb_41_cast = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_8161, groups = var_7587, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_8159, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_41_cast")]; + tensor input_483_cast = add(x = hidden_states_299_cast, y = temb_41_cast)[name = tensor("input_483_cast")]; + tensor reshape_220_shape_0 = const()[name = tensor("reshape_220_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_220_cast = reshape(shape = reshape_220_shape_0, x = input_483_cast)[name = tensor("reshape_220_cast")]; + tensor reduce_mean_165_axes_0 = const()[name = tensor("reduce_mean_165_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_165_keep_dims_0 = const()[name = tensor("reduce_mean_165_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_165_cast")]; + tensor sub_110_cast = sub(x = reshape_220_cast, y = reduce_mean_165_cast)[name = tensor("sub_110_cast")]; + tensor square_55_cast = square(x = sub_110_cast)[name = tensor("square_55_cast")]; + tensor reduce_mean_167_axes_0 = const()[name = tensor("reduce_mean_167_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_167_keep_dims_0 = const()[name = tensor("reduce_mean_167_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_167_cast")]; + tensor add_110_y_0_to_fp16 = const()[name = tensor("add_110_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_110_cast = add(x = reduce_mean_167_cast, y = add_110_y_0_to_fp16)[name = tensor("add_110_cast")]; + tensor sqrt_55_cast = sqrt(x = add_110_cast)[name = tensor("sqrt_55_cast")]; + tensor real_div_55_cast = real_div(x = sub_110_cast, y = sqrt_55_cast)[name = tensor("real_div_55_cast")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_221_cast = reshape(shape = reshape_221_shape_0, x = real_div_55_cast)[name = tensor("reshape_221_cast")]; + tensor add_111_gamma_0_to_fp16 = const()[name = tensor("add_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637928768)))]; + tensor add_111_beta_0_to_fp16 = const()[name = tensor("add_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637929472)))]; + tensor add_111_epsilon_0_to_fp16 = const()[name = tensor("add_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_111_cast")]; + tensor input_487_cast = silu(x = add_111_cast)[name = tensor("input_487_cast")]; + tensor var_8171 = const()[name = tensor("op_8171"), val = tensor([1, 1])]; + tensor var_8173 = const()[name = tensor("op_8173"), val = tensor([1, 1])]; + tensor hidden_states_301_pad_type_0 = const()[name = tensor("hidden_states_301_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_301_pad_0 = const()[name = tensor("hidden_states_301_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637930176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638621440))), name = tensor("up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638621632)))]; + tensor hidden_states_301_cast = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_8173, groups = var_7587, pad = hidden_states_301_pad_0, pad_type = hidden_states_301_pad_type_0, strides = var_8171, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_487_cast)[name = tensor("hidden_states_301_cast")]; + tensor var_8178 = const()[name = tensor("op_8178"), val = tensor([1, 1])]; + tensor var_8180 = const()[name = tensor("op_8180"), val = tensor([1, 1])]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("custom")]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638622336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638776000))), name = tensor("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638776192)))]; + tensor x_25_cast = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_8180, groups = var_7587, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_8178, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_475_cast)[name = tensor("x_25_cast")]; + tensor hidden_states_303_cast = add(x = x_25_cast, y = hidden_states_301_cast)[name = tensor("hidden_states_303_cast")]; + tensor reshape_224_shape_0 = const()[name = tensor("reshape_224_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_224_cast = reshape(shape = reshape_224_shape_0, x = hidden_states_303_cast)[name = tensor("reshape_224_cast")]; + tensor reduce_mean_168_axes_0 = const()[name = tensor("reduce_mean_168_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_168_keep_dims_0 = const()[name = tensor("reduce_mean_168_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_168_cast")]; + tensor sub_112_cast = sub(x = reshape_224_cast, y = reduce_mean_168_cast)[name = tensor("sub_112_cast")]; + tensor square_56_cast = square(x = sub_112_cast)[name = tensor("square_56_cast")]; + tensor reduce_mean_170_axes_0 = const()[name = tensor("reduce_mean_170_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_170_keep_dims_0 = const()[name = tensor("reduce_mean_170_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_170_cast")]; + tensor add_112_y_0_to_fp16 = const()[name = tensor("add_112_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_112_cast = add(x = reduce_mean_170_cast, y = add_112_y_0_to_fp16)[name = tensor("add_112_cast")]; + tensor sqrt_56_cast = sqrt(x = add_112_cast)[name = tensor("sqrt_56_cast")]; + tensor real_div_56_cast = real_div(x = sub_112_cast, y = sqrt_56_cast)[name = tensor("real_div_56_cast")]; + tensor reshape_225_shape_0 = const()[name = tensor("reshape_225_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_225_cast = reshape(shape = reshape_225_shape_0, x = real_div_56_cast)[name = tensor("reshape_225_cast")]; + tensor add_113_gamma_0_to_fp16 = const()[name = tensor("add_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638776896)))]; + tensor add_113_beta_0_to_fp16 = const()[name = tensor("add_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638777600)))]; + tensor add_113_epsilon_0_to_fp16 = const()[name = tensor("add_113_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_113_cast")]; + tensor var_8200 = const()[name = tensor("op_8200"), val = tensor([1, 1])]; + tensor var_8202 = const()[name = tensor("op_8202"), val = tensor([1, 1])]; + tensor hidden_states_305_pad_type_0 = const()[name = tensor("hidden_states_305_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_305_pad_0 = const()[name = tensor("hidden_states_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638778304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638855168))), name = tensor("up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638855360)))]; + tensor hidden_states_305_cast = conv(bias = up_blocks_3_attentions_1_proj_in_bias_to_fp16, dilations = var_8202, groups = var_7587, pad = hidden_states_305_pad_0, pad_type = hidden_states_305_pad_type_0, strides = var_8200, weight = up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized, x = add_113_cast)[name = tensor("hidden_states_305_cast")]; + tensor var_8207 = const()[name = tensor("op_8207"), val = tensor([2, 320, 1, 9216])]; + tensor inputs_85_cast = reshape(shape = var_8207, x = hidden_states_305_cast)[name = tensor("inputs_85_cast")]; + tensor var_8217 = const()[name = tensor("op_8217"), val = tensor([1])]; + tensor channels_mean_85_cast = reduce_mean(axes = var_8217, keep_dims = var_7582, x = inputs_85_cast)[name = tensor("channels_mean_85_cast")]; + tensor zero_mean_85_cast = sub(x = inputs_85_cast, y = channels_mean_85_cast)[name = tensor("zero_mean_85_cast")]; + tensor zero_mean_sq_85_cast = mul(x = zero_mean_85_cast, y = zero_mean_85_cast)[name = tensor("zero_mean_sq_85_cast")]; + tensor var_8221 = const()[name = tensor("op_8221"), val = tensor([1])]; + tensor var_8222_cast = reduce_mean(axes = var_8221, keep_dims = var_7582, x = zero_mean_sq_85_cast)[name = tensor("op_8222_cast")]; + tensor var_8223_to_fp16 = const()[name = tensor("op_8223_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8224_cast = add(x = var_8222_cast, y = var_8223_to_fp16)[name = tensor("op_8224_cast")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_8224_cast)[name = tensor("denom_85_cast")]; + tensor out_85_cast = mul(x = zero_mean_85_cast, y = denom_85_cast)[name = tensor("out_85_cast")]; + tensor var_8228_to_fp16 = const()[name = tensor("op_8228_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638856064)))]; + tensor var_8229_cast = add(x = out_85_cast, y = var_8228_to_fp16)[name = tensor("op_8229_cast")]; + tensor var_8231_to_fp16 = const()[name = tensor("op_8231_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638856768)))]; + tensor hidden_states_307_cast = mul(x = var_8229_cast, y = var_8231_to_fp16)[name = tensor("hidden_states_307_cast")]; + tensor var_8238 = const()[name = tensor("op_8238"), val = tensor([1, 1])]; + tensor var_8240 = const()[name = tensor("op_8240"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638857472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638934336))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_57_cast = conv(dilations = var_8240, groups = var_7587, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_8238, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor("q_57_cast")]; + tensor var_8244 = const()[name = tensor("op_8244"), val = tensor([1, 1])]; + tensor var_8246 = const()[name = tensor("op_8246"), val = tensor([1, 1])]; + tensor k_113_pad_type_0 = const()[name = tensor("k_113_pad_type_0"), val = tensor("custom")]; + tensor k_113_pad_0 = const()[name = tensor("k_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638934528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639011392))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_113_cast = conv(dilations = var_8246, groups = var_7587, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_8244, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor("k_113_cast")]; + tensor var_8250 = const()[name = tensor("op_8250"), val = tensor([1, 1])]; + tensor var_8252 = const()[name = tensor("op_8252"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639011584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639088448))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_57_cast = conv(dilations = var_8252, groups = var_7587, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_8250, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor("v_57_cast")]; + tensor var_8256_begin_0 = const()[name = tensor("op_8256_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8256_end_0 = const()[name = tensor("op_8256_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_8256_end_mask_0 = const()[name = tensor("op_8256_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8256_cast = slice_by_index(begin = var_8256_begin_0, end = var_8256_end_0, end_mask = var_8256_end_mask_0, x = q_57_cast)[name = tensor("op_8256_cast")]; + tensor var_8260_begin_0 = const()[name = tensor("op_8260_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_8260_end_0 = const()[name = tensor("op_8260_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_8260_end_mask_0 = const()[name = tensor("op_8260_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8260_cast = slice_by_index(begin = var_8260_begin_0, end = var_8260_end_0, end_mask = var_8260_end_mask_0, x = q_57_cast)[name = tensor("op_8260_cast")]; + tensor var_8264_begin_0 = const()[name = tensor("op_8264_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_8264_end_0 = const()[name = tensor("op_8264_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_8264_end_mask_0 = const()[name = tensor("op_8264_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8264_cast = slice_by_index(begin = var_8264_begin_0, end = var_8264_end_0, end_mask = var_8264_end_mask_0, x = q_57_cast)[name = tensor("op_8264_cast")]; + tensor var_8268_begin_0 = const()[name = tensor("op_8268_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_8268_end_0 = const()[name = tensor("op_8268_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_8268_end_mask_0 = const()[name = tensor("op_8268_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8268_cast = slice_by_index(begin = var_8268_begin_0, end = var_8268_end_0, end_mask = var_8268_end_mask_0, x = q_57_cast)[name = tensor("op_8268_cast")]; + tensor var_8272_begin_0 = const()[name = tensor("op_8272_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_8272_end_0 = const()[name = tensor("op_8272_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_8272_end_mask_0 = const()[name = tensor("op_8272_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8272_cast = slice_by_index(begin = var_8272_begin_0, end = var_8272_end_0, end_mask = var_8272_end_mask_0, x = q_57_cast)[name = tensor("op_8272_cast")]; + tensor var_8276_begin_0 = const()[name = tensor("op_8276_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_8276_end_0 = const()[name = tensor("op_8276_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_8276_end_mask_0 = const()[name = tensor("op_8276_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8276_cast = slice_by_index(begin = var_8276_begin_0, end = var_8276_end_0, end_mask = var_8276_end_mask_0, x = q_57_cast)[name = tensor("op_8276_cast")]; + tensor var_8280_begin_0 = const()[name = tensor("op_8280_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_8280_end_0 = const()[name = tensor("op_8280_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_8280_end_mask_0 = const()[name = tensor("op_8280_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8280_cast = slice_by_index(begin = var_8280_begin_0, end = var_8280_end_0, end_mask = var_8280_end_mask_0, x = q_57_cast)[name = tensor("op_8280_cast")]; + tensor var_8284_begin_0 = const()[name = tensor("op_8284_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_8284_end_0 = const()[name = tensor("op_8284_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_8284_end_mask_0 = const()[name = tensor("op_8284_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8284_cast = slice_by_index(begin = var_8284_begin_0, end = var_8284_end_0, end_mask = var_8284_end_mask_0, x = q_57_cast)[name = tensor("op_8284_cast")]; + tensor k_115_perm_0 = const()[name = tensor("k_115_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_8291_begin_0 = const()[name = tensor("op_8291_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8291_end_0 = const()[name = tensor("op_8291_end_0"), val = tensor([2, 9216, 1, 40])]; + tensor var_8291_end_mask_0 = const()[name = tensor("op_8291_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_3 = transpose(perm = k_115_perm_0, x = k_113_cast)[name = tensor("transpose_3")]; + tensor var_8291_cast = slice_by_index(begin = var_8291_begin_0, end = var_8291_end_0, end_mask = var_8291_end_mask_0, x = transpose_3)[name = tensor("op_8291_cast")]; + tensor var_8295_begin_0 = const()[name = tensor("op_8295_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_8295_end_0 = const()[name = tensor("op_8295_end_0"), val = tensor([2, 9216, 1, 80])]; + tensor var_8295_end_mask_0 = const()[name = tensor("op_8295_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8295_cast = slice_by_index(begin = var_8295_begin_0, end = var_8295_end_0, end_mask = var_8295_end_mask_0, x = transpose_3)[name = tensor("op_8295_cast")]; + tensor var_8299_begin_0 = const()[name = tensor("op_8299_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_8299_end_0 = const()[name = tensor("op_8299_end_0"), val = tensor([2, 9216, 1, 120])]; + tensor var_8299_end_mask_0 = const()[name = tensor("op_8299_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8299_cast = slice_by_index(begin = var_8299_begin_0, end = var_8299_end_0, end_mask = var_8299_end_mask_0, x = transpose_3)[name = tensor("op_8299_cast")]; + tensor var_8303_begin_0 = const()[name = tensor("op_8303_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_8303_end_0 = const()[name = tensor("op_8303_end_0"), val = tensor([2, 9216, 1, 160])]; + tensor var_8303_end_mask_0 = const()[name = tensor("op_8303_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8303_cast = slice_by_index(begin = var_8303_begin_0, end = var_8303_end_0, end_mask = var_8303_end_mask_0, x = transpose_3)[name = tensor("op_8303_cast")]; + tensor var_8307_begin_0 = const()[name = tensor("op_8307_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_8307_end_0 = const()[name = tensor("op_8307_end_0"), val = tensor([2, 9216, 1, 200])]; + tensor var_8307_end_mask_0 = const()[name = tensor("op_8307_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8307_cast = slice_by_index(begin = var_8307_begin_0, end = var_8307_end_0, end_mask = var_8307_end_mask_0, x = transpose_3)[name = tensor("op_8307_cast")]; + tensor var_8311_begin_0 = const()[name = tensor("op_8311_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_8311_end_0 = const()[name = tensor("op_8311_end_0"), val = tensor([2, 9216, 1, 240])]; + tensor var_8311_end_mask_0 = const()[name = tensor("op_8311_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8311_cast = slice_by_index(begin = var_8311_begin_0, end = var_8311_end_0, end_mask = var_8311_end_mask_0, x = transpose_3)[name = tensor("op_8311_cast")]; + tensor var_8315_begin_0 = const()[name = tensor("op_8315_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_8315_end_0 = const()[name = tensor("op_8315_end_0"), val = tensor([2, 9216, 1, 280])]; + tensor var_8315_end_mask_0 = const()[name = tensor("op_8315_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8315_cast = slice_by_index(begin = var_8315_begin_0, end = var_8315_end_0, end_mask = var_8315_end_mask_0, x = transpose_3)[name = tensor("op_8315_cast")]; + tensor var_8319_begin_0 = const()[name = tensor("op_8319_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_8319_end_0 = const()[name = tensor("op_8319_end_0"), val = tensor([2, 9216, 1, 320])]; + tensor var_8319_end_mask_0 = const()[name = tensor("op_8319_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8319_cast = slice_by_index(begin = var_8319_begin_0, end = var_8319_end_0, end_mask = var_8319_end_mask_0, x = transpose_3)[name = tensor("op_8319_cast")]; + tensor var_8321_begin_0 = const()[name = tensor("op_8321_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8321_end_0 = const()[name = tensor("op_8321_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_8321_end_mask_0 = const()[name = tensor("op_8321_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8321_cast = slice_by_index(begin = var_8321_begin_0, end = var_8321_end_0, end_mask = var_8321_end_mask_0, x = v_57_cast)[name = tensor("op_8321_cast")]; + tensor var_8325_begin_0 = const()[name = tensor("op_8325_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_8325_end_0 = const()[name = tensor("op_8325_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_8325_end_mask_0 = const()[name = tensor("op_8325_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8325_cast = slice_by_index(begin = var_8325_begin_0, end = var_8325_end_0, end_mask = var_8325_end_mask_0, x = v_57_cast)[name = tensor("op_8325_cast")]; + tensor var_8329_begin_0 = const()[name = tensor("op_8329_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_8329_end_0 = const()[name = tensor("op_8329_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_8329_end_mask_0 = const()[name = tensor("op_8329_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8329_cast = slice_by_index(begin = var_8329_begin_0, end = var_8329_end_0, end_mask = var_8329_end_mask_0, x = v_57_cast)[name = tensor("op_8329_cast")]; + tensor var_8333_begin_0 = const()[name = tensor("op_8333_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_8333_end_0 = const()[name = tensor("op_8333_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_8333_end_mask_0 = const()[name = tensor("op_8333_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8333_cast = slice_by_index(begin = var_8333_begin_0, end = var_8333_end_0, end_mask = var_8333_end_mask_0, x = v_57_cast)[name = tensor("op_8333_cast")]; + tensor var_8337_begin_0 = const()[name = tensor("op_8337_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_8337_end_0 = const()[name = tensor("op_8337_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_8337_end_mask_0 = const()[name = tensor("op_8337_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8337_cast = slice_by_index(begin = var_8337_begin_0, end = var_8337_end_0, end_mask = var_8337_end_mask_0, x = v_57_cast)[name = tensor("op_8337_cast")]; + tensor var_8341_begin_0 = const()[name = tensor("op_8341_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_8341_end_0 = const()[name = tensor("op_8341_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_8341_end_mask_0 = const()[name = tensor("op_8341_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8341_cast = slice_by_index(begin = var_8341_begin_0, end = var_8341_end_0, end_mask = var_8341_end_mask_0, x = v_57_cast)[name = tensor("op_8341_cast")]; + tensor var_8345_begin_0 = const()[name = tensor("op_8345_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_8345_end_0 = const()[name = tensor("op_8345_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_8345_end_mask_0 = const()[name = tensor("op_8345_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8345_cast = slice_by_index(begin = var_8345_begin_0, end = var_8345_end_0, end_mask = var_8345_end_mask_0, x = v_57_cast)[name = tensor("op_8345_cast")]; + tensor var_8349_begin_0 = const()[name = tensor("op_8349_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_8349_end_0 = const()[name = tensor("op_8349_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_8349_end_mask_0 = const()[name = tensor("op_8349_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8349_cast = slice_by_index(begin = var_8349_begin_0, end = var_8349_end_0, end_mask = var_8349_end_mask_0, x = v_57_cast)[name = tensor("op_8349_cast")]; + tensor var_8353_equation_0 = const()[name = tensor("op_8353_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8353_cast = einsum(equation = var_8353_equation_0, values = (var_8291_cast, var_8256_cast))[name = tensor("op_8353_cast")]; + tensor var_8354_to_fp16 = const()[name = tensor("op_8354_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_449_cast = mul(x = var_8353_cast, y = var_8354_to_fp16)[name = tensor("aw_449_cast")]; + tensor var_8357_equation_0 = const()[name = tensor("op_8357_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8357_cast = einsum(equation = var_8357_equation_0, values = (var_8295_cast, var_8260_cast))[name = tensor("op_8357_cast")]; + tensor var_8358_to_fp16 = const()[name = tensor("op_8358_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_451_cast = mul(x = var_8357_cast, y = var_8358_to_fp16)[name = tensor("aw_451_cast")]; + tensor var_8361_equation_0 = const()[name = tensor("op_8361_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8361_cast = einsum(equation = var_8361_equation_0, values = (var_8299_cast, var_8264_cast))[name = tensor("op_8361_cast")]; + tensor var_8362_to_fp16 = const()[name = tensor("op_8362_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_453_cast = mul(x = var_8361_cast, y = var_8362_to_fp16)[name = tensor("aw_453_cast")]; + tensor var_8365_equation_0 = const()[name = tensor("op_8365_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8365_cast = einsum(equation = var_8365_equation_0, values = (var_8303_cast, var_8268_cast))[name = tensor("op_8365_cast")]; + tensor var_8366_to_fp16 = const()[name = tensor("op_8366_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_455_cast = mul(x = var_8365_cast, y = var_8366_to_fp16)[name = tensor("aw_455_cast")]; + tensor var_8369_equation_0 = const()[name = tensor("op_8369_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8369_cast = einsum(equation = var_8369_equation_0, values = (var_8307_cast, var_8272_cast))[name = tensor("op_8369_cast")]; + tensor var_8370_to_fp16 = const()[name = tensor("op_8370_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_457_cast = mul(x = var_8369_cast, y = var_8370_to_fp16)[name = tensor("aw_457_cast")]; + tensor var_8373_equation_0 = const()[name = tensor("op_8373_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8373_cast = einsum(equation = var_8373_equation_0, values = (var_8311_cast, var_8276_cast))[name = tensor("op_8373_cast")]; + tensor var_8374_to_fp16 = const()[name = tensor("op_8374_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_459_cast = mul(x = var_8373_cast, y = var_8374_to_fp16)[name = tensor("aw_459_cast")]; + tensor var_8377_equation_0 = const()[name = tensor("op_8377_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8377_cast = einsum(equation = var_8377_equation_0, values = (var_8315_cast, var_8280_cast))[name = tensor("op_8377_cast")]; + tensor var_8378_to_fp16 = const()[name = tensor("op_8378_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_461_cast = mul(x = var_8377_cast, y = var_8378_to_fp16)[name = tensor("aw_461_cast")]; + tensor var_8381_equation_0 = const()[name = tensor("op_8381_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8381_cast = einsum(equation = var_8381_equation_0, values = (var_8319_cast, var_8284_cast))[name = tensor("op_8381_cast")]; + tensor var_8382_to_fp16 = const()[name = tensor("op_8382_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_463_cast = mul(x = var_8381_cast, y = var_8382_to_fp16)[name = tensor("aw_463_cast")]; + tensor var_8384_cast = softmax(axis = var_7587, x = aw_449_cast)[name = tensor("op_8384_cast")]; + tensor var_8385_cast = softmax(axis = var_7587, x = aw_451_cast)[name = tensor("op_8385_cast")]; + tensor var_8386_cast = softmax(axis = var_7587, x = aw_453_cast)[name = tensor("op_8386_cast")]; + tensor var_8387_cast = softmax(axis = var_7587, x = aw_455_cast)[name = tensor("op_8387_cast")]; + tensor var_8388_cast = softmax(axis = var_7587, x = aw_457_cast)[name = tensor("op_8388_cast")]; + tensor var_8389_cast = softmax(axis = var_7587, x = aw_459_cast)[name = tensor("op_8389_cast")]; + tensor var_8390_cast = softmax(axis = var_7587, x = aw_461_cast)[name = tensor("op_8390_cast")]; + tensor var_8391_cast = softmax(axis = var_7587, x = aw_463_cast)[name = tensor("op_8391_cast")]; + tensor var_8393_equation_0 = const()[name = tensor("op_8393_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8393_cast = einsum(equation = var_8393_equation_0, values = (var_8321_cast, var_8384_cast))[name = tensor("op_8393_cast")]; + tensor var_8395_equation_0 = const()[name = tensor("op_8395_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8395_cast = einsum(equation = var_8395_equation_0, values = (var_8325_cast, var_8385_cast))[name = tensor("op_8395_cast")]; + tensor var_8397_equation_0 = const()[name = tensor("op_8397_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8397_cast = einsum(equation = var_8397_equation_0, values = (var_8329_cast, var_8386_cast))[name = tensor("op_8397_cast")]; + tensor var_8399_equation_0 = const()[name = tensor("op_8399_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8399_cast = einsum(equation = var_8399_equation_0, values = (var_8333_cast, var_8387_cast))[name = tensor("op_8399_cast")]; + tensor var_8401_equation_0 = const()[name = tensor("op_8401_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8401_cast = einsum(equation = var_8401_equation_0, values = (var_8337_cast, var_8388_cast))[name = tensor("op_8401_cast")]; + tensor var_8403_equation_0 = const()[name = tensor("op_8403_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8403_cast = einsum(equation = var_8403_equation_0, values = (var_8341_cast, var_8389_cast))[name = tensor("op_8403_cast")]; + tensor var_8405_equation_0 = const()[name = tensor("op_8405_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8405_cast = einsum(equation = var_8405_equation_0, values = (var_8345_cast, var_8390_cast))[name = tensor("op_8405_cast")]; + tensor var_8407_equation_0 = const()[name = tensor("op_8407_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8407_cast = einsum(equation = var_8407_equation_0, values = (var_8349_cast, var_8391_cast))[name = tensor("op_8407_cast")]; + tensor input_491_interleave_0 = const()[name = tensor("input_491_interleave_0"), val = tensor(false)]; + tensor input_491_cast = concat(axis = var_7587, interleave = input_491_interleave_0, values = (var_8393_cast, var_8395_cast, var_8397_cast, var_8399_cast, var_8401_cast, var_8403_cast, var_8405_cast, var_8407_cast))[name = tensor("input_491_cast")]; + tensor var_8413 = const()[name = tensor("op_8413"), val = tensor([1, 1])]; + tensor var_8415 = const()[name = tensor("op_8415"), val = tensor([1, 1])]; + tensor var_8417_pad_type_0 = const()[name = tensor("op_8417_pad_type_0"), val = tensor("custom")]; + tensor var_8417_pad_0 = const()[name = tensor("op_8417_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639088640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639165504))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639165696)))]; + tensor var_8417_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8415, groups = var_7587, pad = var_8417_pad_0, pad_type = var_8417_pad_type_0, strides = var_8413, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_491_cast)[name = tensor("op_8417_cast")]; + tensor inputs_87_cast = add(x = var_8417_cast, y = inputs_85_cast)[name = tensor("inputs_87_cast")]; + tensor var_8421 = const()[name = tensor("op_8421"), val = tensor([1])]; + tensor channels_mean_87_cast = reduce_mean(axes = var_8421, keep_dims = var_7582, x = inputs_87_cast)[name = tensor("channels_mean_87_cast")]; + tensor zero_mean_87_cast = sub(x = inputs_87_cast, y = channels_mean_87_cast)[name = tensor("zero_mean_87_cast")]; + tensor zero_mean_sq_87_cast = mul(x = zero_mean_87_cast, y = zero_mean_87_cast)[name = tensor("zero_mean_sq_87_cast")]; + tensor var_8425 = const()[name = tensor("op_8425"), val = tensor([1])]; + tensor var_8426_cast = reduce_mean(axes = var_8425, keep_dims = var_7582, x = zero_mean_sq_87_cast)[name = tensor("op_8426_cast")]; + tensor var_8427_to_fp16 = const()[name = tensor("op_8427_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8428_cast = add(x = var_8426_cast, y = var_8427_to_fp16)[name = tensor("op_8428_cast")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_8428_cast)[name = tensor("denom_87_cast")]; + tensor out_87_cast = mul(x = zero_mean_87_cast, y = denom_87_cast)[name = tensor("out_87_cast")]; + tensor var_8432_to_fp16 = const()[name = tensor("op_8432_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639166400)))]; + tensor var_8433_cast = add(x = out_87_cast, y = var_8432_to_fp16)[name = tensor("op_8433_cast")]; + tensor var_8435_to_fp16 = const()[name = tensor("op_8435_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639167104)))]; + tensor hidden_states_309_cast = mul(x = var_8433_cast, y = var_8435_to_fp16)[name = tensor("hidden_states_309_cast")]; + tensor var_8442 = const()[name = tensor("op_8442"), val = tensor([1, 1])]; + tensor var_8444 = const()[name = tensor("op_8444"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639167808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639244672))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_59_cast = conv(dilations = var_8444, groups = var_7587, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_8442, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_309_cast)[name = tensor("q_59_cast")]; + tensor var_8448 = const()[name = tensor("op_8448"), val = tensor([1, 1])]; + tensor var_8450 = const()[name = tensor("op_8450"), val = tensor([1, 1])]; + tensor k_117_pad_type_0 = const()[name = tensor("k_117_pad_type_0"), val = tensor("custom")]; + tensor k_117_pad_0 = const()[name = tensor("k_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639244864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639429248))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_117_cast = conv(dilations = var_8450, groups = var_7587, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_8448, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_117_cast")]; + tensor var_8454 = const()[name = tensor("op_8454"), val = tensor([1, 1])]; + tensor var_8456 = const()[name = tensor("op_8456"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639429440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639613824))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_59_cast = conv(dilations = var_8456, groups = var_7587, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_8454, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_59_cast")]; + tensor var_8460_begin_0 = const()[name = tensor("op_8460_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8460_end_0 = const()[name = tensor("op_8460_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_8460_end_mask_0 = const()[name = tensor("op_8460_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8460_cast = slice_by_index(begin = var_8460_begin_0, end = var_8460_end_0, end_mask = var_8460_end_mask_0, x = q_59_cast)[name = tensor("op_8460_cast")]; + tensor var_8464_begin_0 = const()[name = tensor("op_8464_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_8464_end_0 = const()[name = tensor("op_8464_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_8464_end_mask_0 = const()[name = tensor("op_8464_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8464_cast = slice_by_index(begin = var_8464_begin_0, end = var_8464_end_0, end_mask = var_8464_end_mask_0, x = q_59_cast)[name = tensor("op_8464_cast")]; + tensor var_8468_begin_0 = const()[name = tensor("op_8468_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_8468_end_0 = const()[name = tensor("op_8468_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_8468_end_mask_0 = const()[name = tensor("op_8468_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8468_cast = slice_by_index(begin = var_8468_begin_0, end = var_8468_end_0, end_mask = var_8468_end_mask_0, x = q_59_cast)[name = tensor("op_8468_cast")]; + tensor var_8472_begin_0 = const()[name = tensor("op_8472_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_8472_end_0 = const()[name = tensor("op_8472_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_8472_end_mask_0 = const()[name = tensor("op_8472_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8472_cast = slice_by_index(begin = var_8472_begin_0, end = var_8472_end_0, end_mask = var_8472_end_mask_0, x = q_59_cast)[name = tensor("op_8472_cast")]; + tensor var_8476_begin_0 = const()[name = tensor("op_8476_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_8476_end_0 = const()[name = tensor("op_8476_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_8476_end_mask_0 = const()[name = tensor("op_8476_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8476_cast = slice_by_index(begin = var_8476_begin_0, end = var_8476_end_0, end_mask = var_8476_end_mask_0, x = q_59_cast)[name = tensor("op_8476_cast")]; + tensor var_8480_begin_0 = const()[name = tensor("op_8480_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_8480_end_0 = const()[name = tensor("op_8480_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_8480_end_mask_0 = const()[name = tensor("op_8480_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8480_cast = slice_by_index(begin = var_8480_begin_0, end = var_8480_end_0, end_mask = var_8480_end_mask_0, x = q_59_cast)[name = tensor("op_8480_cast")]; + tensor var_8484_begin_0 = const()[name = tensor("op_8484_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_8484_end_0 = const()[name = tensor("op_8484_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_8484_end_mask_0 = const()[name = tensor("op_8484_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8484_cast = slice_by_index(begin = var_8484_begin_0, end = var_8484_end_0, end_mask = var_8484_end_mask_0, x = q_59_cast)[name = tensor("op_8484_cast")]; + tensor var_8488_begin_0 = const()[name = tensor("op_8488_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_8488_end_0 = const()[name = tensor("op_8488_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_8488_end_mask_0 = const()[name = tensor("op_8488_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8488_cast = slice_by_index(begin = var_8488_begin_0, end = var_8488_end_0, end_mask = var_8488_end_mask_0, x = q_59_cast)[name = tensor("op_8488_cast")]; + tensor k_119_perm_0 = const()[name = tensor("k_119_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_8495_begin_0 = const()[name = tensor("op_8495_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8495_end_0 = const()[name = tensor("op_8495_end_0"), val = tensor([2, 77, 1, 40])]; + tensor var_8495_end_mask_0 = const()[name = tensor("op_8495_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_2 = transpose(perm = k_119_perm_0, x = k_117_cast)[name = tensor("transpose_2")]; + tensor var_8495_cast = slice_by_index(begin = var_8495_begin_0, end = var_8495_end_0, end_mask = var_8495_end_mask_0, x = transpose_2)[name = tensor("op_8495_cast")]; + tensor var_8499_begin_0 = const()[name = tensor("op_8499_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_8499_end_0 = const()[name = tensor("op_8499_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_8499_end_mask_0 = const()[name = tensor("op_8499_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8499_cast = slice_by_index(begin = var_8499_begin_0, end = var_8499_end_0, end_mask = var_8499_end_mask_0, x = transpose_2)[name = tensor("op_8499_cast")]; + tensor var_8503_begin_0 = const()[name = tensor("op_8503_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_8503_end_0 = const()[name = tensor("op_8503_end_0"), val = tensor([2, 77, 1, 120])]; + tensor var_8503_end_mask_0 = const()[name = tensor("op_8503_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8503_cast = slice_by_index(begin = var_8503_begin_0, end = var_8503_end_0, end_mask = var_8503_end_mask_0, x = transpose_2)[name = tensor("op_8503_cast")]; + tensor var_8507_begin_0 = const()[name = tensor("op_8507_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_8507_end_0 = const()[name = tensor("op_8507_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_8507_end_mask_0 = const()[name = tensor("op_8507_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8507_cast = slice_by_index(begin = var_8507_begin_0, end = var_8507_end_0, end_mask = var_8507_end_mask_0, x = transpose_2)[name = tensor("op_8507_cast")]; + tensor var_8511_begin_0 = const()[name = tensor("op_8511_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_8511_end_0 = const()[name = tensor("op_8511_end_0"), val = tensor([2, 77, 1, 200])]; + tensor var_8511_end_mask_0 = const()[name = tensor("op_8511_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8511_cast = slice_by_index(begin = var_8511_begin_0, end = var_8511_end_0, end_mask = var_8511_end_mask_0, x = transpose_2)[name = tensor("op_8511_cast")]; + tensor var_8515_begin_0 = const()[name = tensor("op_8515_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_8515_end_0 = const()[name = tensor("op_8515_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_8515_end_mask_0 = const()[name = tensor("op_8515_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8515_cast = slice_by_index(begin = var_8515_begin_0, end = var_8515_end_0, end_mask = var_8515_end_mask_0, x = transpose_2)[name = tensor("op_8515_cast")]; + tensor var_8519_begin_0 = const()[name = tensor("op_8519_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_8519_end_0 = const()[name = tensor("op_8519_end_0"), val = tensor([2, 77, 1, 280])]; + tensor var_8519_end_mask_0 = const()[name = tensor("op_8519_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8519_cast = slice_by_index(begin = var_8519_begin_0, end = var_8519_end_0, end_mask = var_8519_end_mask_0, x = transpose_2)[name = tensor("op_8519_cast")]; + tensor var_8523_begin_0 = const()[name = tensor("op_8523_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_8523_end_0 = const()[name = tensor("op_8523_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_8523_end_mask_0 = const()[name = tensor("op_8523_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8523_cast = slice_by_index(begin = var_8523_begin_0, end = var_8523_end_0, end_mask = var_8523_end_mask_0, x = transpose_2)[name = tensor("op_8523_cast")]; + tensor var_8525_begin_0 = const()[name = tensor("op_8525_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8525_end_0 = const()[name = tensor("op_8525_end_0"), val = tensor([2, 40, 1, 77])]; + tensor var_8525_end_mask_0 = const()[name = tensor("op_8525_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8525_cast = slice_by_index(begin = var_8525_begin_0, end = var_8525_end_0, end_mask = var_8525_end_mask_0, x = v_59_cast)[name = tensor("op_8525_cast")]; + tensor var_8529_begin_0 = const()[name = tensor("op_8529_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_8529_end_0 = const()[name = tensor("op_8529_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_8529_end_mask_0 = const()[name = tensor("op_8529_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8529_cast = slice_by_index(begin = var_8529_begin_0, end = var_8529_end_0, end_mask = var_8529_end_mask_0, x = v_59_cast)[name = tensor("op_8529_cast")]; + tensor var_8533_begin_0 = const()[name = tensor("op_8533_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_8533_end_0 = const()[name = tensor("op_8533_end_0"), val = tensor([2, 120, 1, 77])]; + tensor var_8533_end_mask_0 = const()[name = tensor("op_8533_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8533_cast = slice_by_index(begin = var_8533_begin_0, end = var_8533_end_0, end_mask = var_8533_end_mask_0, x = v_59_cast)[name = tensor("op_8533_cast")]; + tensor var_8537_begin_0 = const()[name = tensor("op_8537_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_8537_end_0 = const()[name = tensor("op_8537_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_8537_end_mask_0 = const()[name = tensor("op_8537_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8537_cast = slice_by_index(begin = var_8537_begin_0, end = var_8537_end_0, end_mask = var_8537_end_mask_0, x = v_59_cast)[name = tensor("op_8537_cast")]; + tensor var_8541_begin_0 = const()[name = tensor("op_8541_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_8541_end_0 = const()[name = tensor("op_8541_end_0"), val = tensor([2, 200, 1, 77])]; + tensor var_8541_end_mask_0 = const()[name = tensor("op_8541_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8541_cast = slice_by_index(begin = var_8541_begin_0, end = var_8541_end_0, end_mask = var_8541_end_mask_0, x = v_59_cast)[name = tensor("op_8541_cast")]; + tensor var_8545_begin_0 = const()[name = tensor("op_8545_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_8545_end_0 = const()[name = tensor("op_8545_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_8545_end_mask_0 = const()[name = tensor("op_8545_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8545_cast = slice_by_index(begin = var_8545_begin_0, end = var_8545_end_0, end_mask = var_8545_end_mask_0, x = v_59_cast)[name = tensor("op_8545_cast")]; + tensor var_8549_begin_0 = const()[name = tensor("op_8549_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_8549_end_0 = const()[name = tensor("op_8549_end_0"), val = tensor([2, 280, 1, 77])]; + tensor var_8549_end_mask_0 = const()[name = tensor("op_8549_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8549_cast = slice_by_index(begin = var_8549_begin_0, end = var_8549_end_0, end_mask = var_8549_end_mask_0, x = v_59_cast)[name = tensor("op_8549_cast")]; + tensor var_8553_begin_0 = const()[name = tensor("op_8553_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_8553_end_0 = const()[name = tensor("op_8553_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_8553_end_mask_0 = const()[name = tensor("op_8553_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8553_cast = slice_by_index(begin = var_8553_begin_0, end = var_8553_end_0, end_mask = var_8553_end_mask_0, x = v_59_cast)[name = tensor("op_8553_cast")]; + tensor var_8557_equation_0 = const()[name = tensor("op_8557_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8557_cast = einsum(equation = var_8557_equation_0, values = (var_8495_cast, var_8460_cast))[name = tensor("op_8557_cast")]; + tensor var_8558_to_fp16 = const()[name = tensor("op_8558_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_465_cast = mul(x = var_8557_cast, y = var_8558_to_fp16)[name = tensor("aw_465_cast")]; + tensor var_8561_equation_0 = const()[name = tensor("op_8561_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8561_cast = einsum(equation = var_8561_equation_0, values = (var_8499_cast, var_8464_cast))[name = tensor("op_8561_cast")]; + tensor var_8562_to_fp16 = const()[name = tensor("op_8562_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_467_cast = mul(x = var_8561_cast, y = var_8562_to_fp16)[name = tensor("aw_467_cast")]; + tensor var_8565_equation_0 = const()[name = tensor("op_8565_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8565_cast = einsum(equation = var_8565_equation_0, values = (var_8503_cast, var_8468_cast))[name = tensor("op_8565_cast")]; + tensor var_8566_to_fp16 = const()[name = tensor("op_8566_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_469_cast = mul(x = var_8565_cast, y = var_8566_to_fp16)[name = tensor("aw_469_cast")]; + tensor var_8569_equation_0 = const()[name = tensor("op_8569_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8569_cast = einsum(equation = var_8569_equation_0, values = (var_8507_cast, var_8472_cast))[name = tensor("op_8569_cast")]; + tensor var_8570_to_fp16 = const()[name = tensor("op_8570_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_471_cast = mul(x = var_8569_cast, y = var_8570_to_fp16)[name = tensor("aw_471_cast")]; + tensor var_8573_equation_0 = const()[name = tensor("op_8573_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8573_cast = einsum(equation = var_8573_equation_0, values = (var_8511_cast, var_8476_cast))[name = tensor("op_8573_cast")]; + tensor var_8574_to_fp16 = const()[name = tensor("op_8574_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_473_cast = mul(x = var_8573_cast, y = var_8574_to_fp16)[name = tensor("aw_473_cast")]; + tensor var_8577_equation_0 = const()[name = tensor("op_8577_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8577_cast = einsum(equation = var_8577_equation_0, values = (var_8515_cast, var_8480_cast))[name = tensor("op_8577_cast")]; + tensor var_8578_to_fp16 = const()[name = tensor("op_8578_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_475_cast = mul(x = var_8577_cast, y = var_8578_to_fp16)[name = tensor("aw_475_cast")]; + tensor var_8581_equation_0 = const()[name = tensor("op_8581_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8581_cast = einsum(equation = var_8581_equation_0, values = (var_8519_cast, var_8484_cast))[name = tensor("op_8581_cast")]; + tensor var_8582_to_fp16 = const()[name = tensor("op_8582_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_477_cast = mul(x = var_8581_cast, y = var_8582_to_fp16)[name = tensor("aw_477_cast")]; + tensor var_8585_equation_0 = const()[name = tensor("op_8585_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8585_cast = einsum(equation = var_8585_equation_0, values = (var_8523_cast, var_8488_cast))[name = tensor("op_8585_cast")]; + tensor var_8586_to_fp16 = const()[name = tensor("op_8586_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_479_cast = mul(x = var_8585_cast, y = var_8586_to_fp16)[name = tensor("aw_479_cast")]; + tensor var_8588_cast = softmax(axis = var_7587, x = aw_465_cast)[name = tensor("op_8588_cast")]; + tensor var_8589_cast = softmax(axis = var_7587, x = aw_467_cast)[name = tensor("op_8589_cast")]; + tensor var_8590_cast = softmax(axis = var_7587, x = aw_469_cast)[name = tensor("op_8590_cast")]; + tensor var_8591_cast = softmax(axis = var_7587, x = aw_471_cast)[name = tensor("op_8591_cast")]; + tensor var_8592_cast = softmax(axis = var_7587, x = aw_473_cast)[name = tensor("op_8592_cast")]; + tensor var_8593_cast = softmax(axis = var_7587, x = aw_475_cast)[name = tensor("op_8593_cast")]; + tensor var_8594_cast = softmax(axis = var_7587, x = aw_477_cast)[name = tensor("op_8594_cast")]; + tensor var_8595_cast = softmax(axis = var_7587, x = aw_479_cast)[name = tensor("op_8595_cast")]; + tensor var_8597_equation_0 = const()[name = tensor("op_8597_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8597_cast = einsum(equation = var_8597_equation_0, values = (var_8525_cast, var_8588_cast))[name = tensor("op_8597_cast")]; + tensor var_8599_equation_0 = const()[name = tensor("op_8599_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8599_cast = einsum(equation = var_8599_equation_0, values = (var_8529_cast, var_8589_cast))[name = tensor("op_8599_cast")]; + tensor var_8601_equation_0 = const()[name = tensor("op_8601_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8601_cast = einsum(equation = var_8601_equation_0, values = (var_8533_cast, var_8590_cast))[name = tensor("op_8601_cast")]; + tensor var_8603_equation_0 = const()[name = tensor("op_8603_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8603_cast = einsum(equation = var_8603_equation_0, values = (var_8537_cast, var_8591_cast))[name = tensor("op_8603_cast")]; + tensor var_8605_equation_0 = const()[name = tensor("op_8605_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8605_cast = einsum(equation = var_8605_equation_0, values = (var_8541_cast, var_8592_cast))[name = tensor("op_8605_cast")]; + tensor var_8607_equation_0 = const()[name = tensor("op_8607_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8607_cast = einsum(equation = var_8607_equation_0, values = (var_8545_cast, var_8593_cast))[name = tensor("op_8607_cast")]; + tensor var_8609_equation_0 = const()[name = tensor("op_8609_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8609_cast = einsum(equation = var_8609_equation_0, values = (var_8549_cast, var_8594_cast))[name = tensor("op_8609_cast")]; + tensor var_8611_equation_0 = const()[name = tensor("op_8611_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8611_cast = einsum(equation = var_8611_equation_0, values = (var_8553_cast, var_8595_cast))[name = tensor("op_8611_cast")]; + tensor input_493_interleave_0 = const()[name = tensor("input_493_interleave_0"), val = tensor(false)]; + tensor input_493_cast = concat(axis = var_7587, interleave = input_493_interleave_0, values = (var_8597_cast, var_8599_cast, var_8601_cast, var_8603_cast, var_8605_cast, var_8607_cast, var_8609_cast, var_8611_cast))[name = tensor("input_493_cast")]; + tensor var_8617 = const()[name = tensor("op_8617"), val = tensor([1, 1])]; + tensor var_8619 = const()[name = tensor("op_8619"), val = tensor([1, 1])]; + tensor var_8621_pad_type_0 = const()[name = tensor("op_8621_pad_type_0"), val = tensor("custom")]; + tensor var_8621_pad_0 = const()[name = tensor("op_8621_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639614016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639690880))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639691072)))]; + tensor var_8621_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8619, groups = var_7587, pad = var_8621_pad_0, pad_type = var_8621_pad_type_0, strides = var_8617, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_493_cast)[name = tensor("op_8621_cast")]; + tensor inputs_89_cast = add(x = var_8621_cast, y = inputs_87_cast)[name = tensor("inputs_89_cast")]; + tensor var_8625 = const()[name = tensor("op_8625"), val = tensor([1])]; + tensor channels_mean_89_cast = reduce_mean(axes = var_8625, keep_dims = var_7582, x = inputs_89_cast)[name = tensor("channels_mean_89_cast")]; + tensor zero_mean_89_cast = sub(x = inputs_89_cast, y = channels_mean_89_cast)[name = tensor("zero_mean_89_cast")]; + tensor zero_mean_sq_89_cast = mul(x = zero_mean_89_cast, y = zero_mean_89_cast)[name = tensor("zero_mean_sq_89_cast")]; + tensor var_8629 = const()[name = tensor("op_8629"), val = tensor([1])]; + tensor var_8630_cast = reduce_mean(axes = var_8629, keep_dims = var_7582, x = zero_mean_sq_89_cast)[name = tensor("op_8630_cast")]; + tensor var_8631_to_fp16 = const()[name = tensor("op_8631_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8632_cast = add(x = var_8630_cast, y = var_8631_to_fp16)[name = tensor("op_8632_cast")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_8632_cast)[name = tensor("denom_89_cast")]; + tensor out_89_cast = mul(x = zero_mean_89_cast, y = denom_89_cast)[name = tensor("out_89_cast")]; + tensor var_8636_to_fp16 = const()[name = tensor("op_8636_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639691776)))]; + tensor var_8637_cast = add(x = out_89_cast, y = var_8636_to_fp16)[name = tensor("op_8637_cast")]; + tensor var_8639_to_fp16 = const()[name = tensor("op_8639_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639692480)))]; + tensor input_495_cast = mul(x = var_8637_cast, y = var_8639_to_fp16)[name = tensor("input_495_cast")]; + tensor var_8647 = const()[name = tensor("op_8647"), val = tensor([1, 1])]; + tensor var_8649 = const()[name = tensor("op_8649"), val = tensor([1, 1])]; + tensor var_8651_pad_type_0 = const()[name = tensor("op_8651_pad_type_0"), val = tensor("custom")]; + tensor var_8651_pad_0 = const()[name = tensor("op_8651_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639693184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640307648))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640307840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640309824))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_8651_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8649, groups = var_7587, pad = var_8651_pad_0, pad_type = var_8651_pad_type_0, strides = var_8647, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_495_cast)[name = tensor("op_8651_cast")]; + tensor var_8652_split_sizes_0 = const()[name = tensor("op_8652_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_8652_axis_0 = const()[name = tensor("op_8652_axis_0"), val = tensor(1)]; + tensor var_8652_cast_0, tensor var_8652_cast_1 = split(axis = var_8652_axis_0, split_sizes = var_8652_split_sizes_0, x = var_8651_cast)[name = tensor("op_8652_cast")]; + tensor var_8654_mode_0 = const()[name = tensor("op_8654_mode_0"), val = tensor("EXACT")]; + tensor var_8654_cast = gelu(mode = var_8654_mode_0, x = var_8652_cast_1)[name = tensor("op_8654_cast")]; + tensor input_497_cast = mul(x = var_8652_cast_0, y = var_8654_cast)[name = tensor("input_497_cast")]; + tensor var_8658 = const()[name = tensor("op_8658"), val = tensor([1, 1])]; + tensor var_8660 = const()[name = tensor("op_8660"), val = tensor([1, 1])]; + tensor var_8662_pad_type_0 = const()[name = tensor("op_8662_pad_type_0"), val = tensor("custom")]; + tensor var_8662_pad_0 = const()[name = tensor("op_8662_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640310016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640617280))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640617472)))]; + tensor var_8662_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_8660, groups = var_7587, pad = var_8662_pad_0, pad_type = var_8662_pad_type_0, strides = var_8658, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_497_cast)[name = tensor("op_8662_cast")]; + tensor hidden_states_313_cast = add(x = var_8662_cast, y = inputs_89_cast)[name = tensor("hidden_states_313_cast")]; + tensor var_8664 = const()[name = tensor("op_8664"), val = tensor([2, 320, 96, 96])]; + tensor input_499_cast = reshape(shape = var_8664, x = hidden_states_313_cast)[name = tensor("input_499_cast")]; + tensor var_8668 = const()[name = tensor("op_8668"), val = tensor([1, 1])]; + tensor var_8670 = const()[name = tensor("op_8670"), val = tensor([1, 1])]; + tensor hidden_states_315_pad_type_0 = const()[name = tensor("hidden_states_315_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_315_pad_0 = const()[name = tensor("hidden_states_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640618176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640695040))), name = tensor("up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640695232)))]; + tensor hidden_states_315_cast = conv(bias = up_blocks_3_attentions_1_proj_out_bias_to_fp16, dilations = var_8670, groups = var_7587, pad = hidden_states_315_pad_0, pad_type = hidden_states_315_pad_type_0, strides = var_8668, weight = up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized, x = input_499_cast)[name = tensor("hidden_states_315_cast")]; + tensor hidden_states_317_cast = add(x = hidden_states_315_cast, y = hidden_states_303_cast)[name = tensor("hidden_states_317_cast")]; + tensor input_501_interleave_0 = const()[name = tensor("input_501_interleave_0"), val = tensor(false)]; + tensor input_501_cast = concat(axis = var_7587, interleave = input_501_interleave_0, values = (hidden_states_317_cast, input_7_cast))[name = tensor("input_501_cast")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([2, 32, 20, 96, 96])]; + tensor reshape_228_cast = reshape(shape = reshape_228_shape_0, x = input_501_cast)[name = tensor("reshape_228_cast")]; + tensor reduce_mean_171_axes_0 = const()[name = tensor("reduce_mean_171_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_171_keep_dims_0 = const()[name = tensor("reduce_mean_171_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_171_cast")]; + tensor sub_114_cast = sub(x = reshape_228_cast, y = reduce_mean_171_cast)[name = tensor("sub_114_cast")]; + tensor square_57_cast = square(x = sub_114_cast)[name = tensor("square_57_cast")]; + tensor reduce_mean_173_axes_0 = const()[name = tensor("reduce_mean_173_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_173_keep_dims_0 = const()[name = tensor("reduce_mean_173_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_173_cast")]; + tensor add_114_y_0_to_fp16 = const()[name = tensor("add_114_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_114_cast = add(x = reduce_mean_173_cast, y = add_114_y_0_to_fp16)[name = tensor("add_114_cast")]; + tensor sqrt_57_cast = sqrt(x = add_114_cast)[name = tensor("sqrt_57_cast")]; + tensor real_div_57_cast = real_div(x = sub_114_cast, y = sqrt_57_cast)[name = tensor("real_div_57_cast")]; + tensor reshape_229_shape_0 = const()[name = tensor("reshape_229_shape_0"), val = tensor([2, 640, 96, 96])]; + tensor reshape_229_cast = reshape(shape = reshape_229_shape_0, x = real_div_57_cast)[name = tensor("reshape_229_cast")]; + tensor add_115_gamma_0_to_fp16 = const()[name = tensor("add_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640695936)))]; + tensor add_115_beta_0_to_fp16 = const()[name = tensor("add_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640697280)))]; + tensor add_115_epsilon_0_to_fp16 = const()[name = tensor("add_115_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_115_cast")]; + tensor input_505_cast = silu(x = add_115_cast)[name = tensor("input_505_cast")]; + tensor var_8688 = const()[name = tensor("op_8688"), val = tensor([1, 1])]; + tensor var_8690 = const()[name = tensor("op_8690"), val = tensor([1, 1])]; + tensor hidden_states_319_pad_type_0 = const()[name = tensor("hidden_states_319_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_319_pad_0 = const()[name = tensor("hidden_states_319_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640698624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642081088))), name = tensor("up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642081280)))]; + tensor hidden_states_319_cast = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_8690, groups = var_7587, pad = hidden_states_319_pad_0, pad_type = hidden_states_319_pad_type_0, strides = var_8688, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized, x = input_505_cast)[name = tensor("hidden_states_319_cast")]; + tensor var_8696 = const()[name = tensor("op_8696"), val = tensor([1, 1])]; + tensor var_8698 = const()[name = tensor("op_8698"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642081984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642389248))), name = tensor("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642389440)))]; + tensor temb_cast = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_8698, groups = var_7587, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_8696, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_cast")]; + tensor input_509_cast = add(x = hidden_states_319_cast, y = temb_cast)[name = tensor("input_509_cast")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_232_cast = reshape(shape = reshape_232_shape_0, x = input_509_cast)[name = tensor("reshape_232_cast")]; + tensor reduce_mean_174_axes_0 = const()[name = tensor("reduce_mean_174_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_174_keep_dims_0 = const()[name = tensor("reduce_mean_174_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_174_cast")]; + tensor sub_116_cast = sub(x = reshape_232_cast, y = reduce_mean_174_cast)[name = tensor("sub_116_cast")]; + tensor square_58_cast = square(x = sub_116_cast)[name = tensor("square_58_cast")]; + tensor reduce_mean_176_axes_0 = const()[name = tensor("reduce_mean_176_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_176_keep_dims_0 = const()[name = tensor("reduce_mean_176_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_176_cast")]; + tensor add_116_y_0_to_fp16 = const()[name = tensor("add_116_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_116_cast = add(x = reduce_mean_176_cast, y = add_116_y_0_to_fp16)[name = tensor("add_116_cast")]; + tensor sqrt_58_cast = sqrt(x = add_116_cast)[name = tensor("sqrt_58_cast")]; + tensor real_div_58_cast = real_div(x = sub_116_cast, y = sqrt_58_cast)[name = tensor("real_div_58_cast")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_233_cast = reshape(shape = reshape_233_shape_0, x = real_div_58_cast)[name = tensor("reshape_233_cast")]; + tensor add_117_gamma_0_to_fp16 = const()[name = tensor("add_117_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642390144)))]; + tensor add_117_beta_0_to_fp16 = const()[name = tensor("add_117_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642390848)))]; + tensor add_117_epsilon_0_to_fp16 = const()[name = tensor("add_117_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_117_cast")]; + tensor input_513_cast = silu(x = add_117_cast)[name = tensor("input_513_cast")]; + tensor var_8708 = const()[name = tensor("op_8708"), val = tensor([1, 1])]; + tensor var_8710 = const()[name = tensor("op_8710"), val = tensor([1, 1])]; + tensor hidden_states_321_pad_type_0 = const()[name = tensor("hidden_states_321_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_321_pad_0 = const()[name = tensor("hidden_states_321_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642391552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643082816))), name = tensor("up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643083008)))]; + tensor hidden_states_321_cast = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_8710, groups = var_7587, pad = hidden_states_321_pad_0, pad_type = hidden_states_321_pad_type_0, strides = var_8708, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized, x = input_513_cast)[name = tensor("hidden_states_321_cast")]; + tensor var_8715 = const()[name = tensor("op_8715"), val = tensor([1, 1])]; + tensor var_8717 = const()[name = tensor("op_8717"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643083712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643237376))), name = tensor("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643237568)))]; + tensor x_cast = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_8717, groups = var_7587, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_8715, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_501_cast)[name = tensor("x_cast")]; + tensor hidden_states_323_cast = add(x = x_cast, y = hidden_states_321_cast)[name = tensor("hidden_states_323_cast")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_236_cast = reshape(shape = reshape_236_shape_0, x = hidden_states_323_cast)[name = tensor("reshape_236_cast")]; + tensor reduce_mean_177_axes_0 = const()[name = tensor("reduce_mean_177_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_177_keep_dims_0 = const()[name = tensor("reduce_mean_177_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_177_cast")]; + tensor sub_118_cast = sub(x = reshape_236_cast, y = reduce_mean_177_cast)[name = tensor("sub_118_cast")]; + tensor square_59_cast = square(x = sub_118_cast)[name = tensor("square_59_cast")]; + tensor reduce_mean_179_axes_0 = const()[name = tensor("reduce_mean_179_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_179_keep_dims_0 = const()[name = tensor("reduce_mean_179_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_179_cast")]; + tensor add_118_y_0_to_fp16 = const()[name = tensor("add_118_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_118_cast = add(x = reduce_mean_179_cast, y = add_118_y_0_to_fp16)[name = tensor("add_118_cast")]; + tensor sqrt_59_cast = sqrt(x = add_118_cast)[name = tensor("sqrt_59_cast")]; + tensor real_div_59_cast = real_div(x = sub_118_cast, y = sqrt_59_cast)[name = tensor("real_div_59_cast")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_237_cast = reshape(shape = reshape_237_shape_0, x = real_div_59_cast)[name = tensor("reshape_237_cast")]; + tensor add_119_gamma_0_to_fp16 = const()[name = tensor("add_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643238272)))]; + tensor add_119_beta_0_to_fp16 = const()[name = tensor("add_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643238976)))]; + tensor add_119_epsilon_0_to_fp16 = const()[name = tensor("add_119_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_119_cast")]; + tensor var_8737 = const()[name = tensor("op_8737"), val = tensor([1, 1])]; + tensor var_8739 = const()[name = tensor("op_8739"), val = tensor([1, 1])]; + tensor hidden_states_325_pad_type_0 = const()[name = tensor("hidden_states_325_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_325_pad_0 = const()[name = tensor("hidden_states_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643239680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643316544))), name = tensor("up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643316736)))]; + tensor hidden_states_325_cast = conv(bias = up_blocks_3_attentions_2_proj_in_bias_to_fp16, dilations = var_8739, groups = var_7587, pad = hidden_states_325_pad_0, pad_type = hidden_states_325_pad_type_0, strides = var_8737, weight = up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized, x = add_119_cast)[name = tensor("hidden_states_325_cast")]; + tensor var_8744 = const()[name = tensor("op_8744"), val = tensor([2, 320, 1, 9216])]; + tensor inputs_91_cast = reshape(shape = var_8744, x = hidden_states_325_cast)[name = tensor("inputs_91_cast")]; + tensor var_8754 = const()[name = tensor("op_8754"), val = tensor([1])]; + tensor channels_mean_91_cast = reduce_mean(axes = var_8754, keep_dims = var_7582, x = inputs_91_cast)[name = tensor("channels_mean_91_cast")]; + tensor zero_mean_91_cast = sub(x = inputs_91_cast, y = channels_mean_91_cast)[name = tensor("zero_mean_91_cast")]; + tensor zero_mean_sq_91_cast = mul(x = zero_mean_91_cast, y = zero_mean_91_cast)[name = tensor("zero_mean_sq_91_cast")]; + tensor var_8758 = const()[name = tensor("op_8758"), val = tensor([1])]; + tensor var_8759_cast = reduce_mean(axes = var_8758, keep_dims = var_7582, x = zero_mean_sq_91_cast)[name = tensor("op_8759_cast")]; + tensor var_8760_to_fp16 = const()[name = tensor("op_8760_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8761_cast = add(x = var_8759_cast, y = var_8760_to_fp16)[name = tensor("op_8761_cast")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_8761_cast)[name = tensor("denom_91_cast")]; + tensor out_91_cast = mul(x = zero_mean_91_cast, y = denom_91_cast)[name = tensor("out_91_cast")]; + tensor var_8765_to_fp16 = const()[name = tensor("op_8765_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643317440)))]; + tensor var_8766_cast = add(x = out_91_cast, y = var_8765_to_fp16)[name = tensor("op_8766_cast")]; + tensor var_8768_to_fp16 = const()[name = tensor("op_8768_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643318144)))]; + tensor hidden_states_327_cast = mul(x = var_8766_cast, y = var_8768_to_fp16)[name = tensor("hidden_states_327_cast")]; + tensor var_8775 = const()[name = tensor("op_8775"), val = tensor([1, 1])]; + tensor var_8777 = const()[name = tensor("op_8777"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643318848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643395712))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_61_cast = conv(dilations = var_8777, groups = var_7587, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_8775, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor("q_61_cast")]; + tensor var_8781 = const()[name = tensor("op_8781"), val = tensor([1, 1])]; + tensor var_8783 = const()[name = tensor("op_8783"), val = tensor([1, 1])]; + tensor k_121_pad_type_0 = const()[name = tensor("k_121_pad_type_0"), val = tensor("custom")]; + tensor k_121_pad_0 = const()[name = tensor("k_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643395904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643472768))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_121_cast = conv(dilations = var_8783, groups = var_7587, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_8781, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor("k_121_cast")]; + tensor var_8787 = const()[name = tensor("op_8787"), val = tensor([1, 1])]; + tensor var_8789 = const()[name = tensor("op_8789"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643472960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643549824))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_61_cast = conv(dilations = var_8789, groups = var_7587, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_8787, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor("v_61_cast")]; + tensor var_8793_begin_0 = const()[name = tensor("op_8793_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8793_end_0 = const()[name = tensor("op_8793_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_8793_end_mask_0 = const()[name = tensor("op_8793_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8793_cast = slice_by_index(begin = var_8793_begin_0, end = var_8793_end_0, end_mask = var_8793_end_mask_0, x = q_61_cast)[name = tensor("op_8793_cast")]; + tensor var_8797_begin_0 = const()[name = tensor("op_8797_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_8797_end_0 = const()[name = tensor("op_8797_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_8797_end_mask_0 = const()[name = tensor("op_8797_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8797_cast = slice_by_index(begin = var_8797_begin_0, end = var_8797_end_0, end_mask = var_8797_end_mask_0, x = q_61_cast)[name = tensor("op_8797_cast")]; + tensor var_8801_begin_0 = const()[name = tensor("op_8801_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_8801_end_0 = const()[name = tensor("op_8801_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_8801_end_mask_0 = const()[name = tensor("op_8801_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8801_cast = slice_by_index(begin = var_8801_begin_0, end = var_8801_end_0, end_mask = var_8801_end_mask_0, x = q_61_cast)[name = tensor("op_8801_cast")]; + tensor var_8805_begin_0 = const()[name = tensor("op_8805_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_8805_end_0 = const()[name = tensor("op_8805_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_8805_end_mask_0 = const()[name = tensor("op_8805_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8805_cast = slice_by_index(begin = var_8805_begin_0, end = var_8805_end_0, end_mask = var_8805_end_mask_0, x = q_61_cast)[name = tensor("op_8805_cast")]; + tensor var_8809_begin_0 = const()[name = tensor("op_8809_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_8809_end_0 = const()[name = tensor("op_8809_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_8809_end_mask_0 = const()[name = tensor("op_8809_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8809_cast = slice_by_index(begin = var_8809_begin_0, end = var_8809_end_0, end_mask = var_8809_end_mask_0, x = q_61_cast)[name = tensor("op_8809_cast")]; + tensor var_8813_begin_0 = const()[name = tensor("op_8813_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_8813_end_0 = const()[name = tensor("op_8813_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_8813_end_mask_0 = const()[name = tensor("op_8813_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8813_cast = slice_by_index(begin = var_8813_begin_0, end = var_8813_end_0, end_mask = var_8813_end_mask_0, x = q_61_cast)[name = tensor("op_8813_cast")]; + tensor var_8817_begin_0 = const()[name = tensor("op_8817_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_8817_end_0 = const()[name = tensor("op_8817_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_8817_end_mask_0 = const()[name = tensor("op_8817_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8817_cast = slice_by_index(begin = var_8817_begin_0, end = var_8817_end_0, end_mask = var_8817_end_mask_0, x = q_61_cast)[name = tensor("op_8817_cast")]; + tensor var_8821_begin_0 = const()[name = tensor("op_8821_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_8821_end_0 = const()[name = tensor("op_8821_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_8821_end_mask_0 = const()[name = tensor("op_8821_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8821_cast = slice_by_index(begin = var_8821_begin_0, end = var_8821_end_0, end_mask = var_8821_end_mask_0, x = q_61_cast)[name = tensor("op_8821_cast")]; + tensor k_123_perm_0 = const()[name = tensor("k_123_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_8828_begin_0 = const()[name = tensor("op_8828_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8828_end_0 = const()[name = tensor("op_8828_end_0"), val = tensor([2, 9216, 1, 40])]; + tensor var_8828_end_mask_0 = const()[name = tensor("op_8828_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_1 = transpose(perm = k_123_perm_0, x = k_121_cast)[name = tensor("transpose_1")]; + tensor var_8828_cast = slice_by_index(begin = var_8828_begin_0, end = var_8828_end_0, end_mask = var_8828_end_mask_0, x = transpose_1)[name = tensor("op_8828_cast")]; + tensor var_8832_begin_0 = const()[name = tensor("op_8832_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_8832_end_0 = const()[name = tensor("op_8832_end_0"), val = tensor([2, 9216, 1, 80])]; + tensor var_8832_end_mask_0 = const()[name = tensor("op_8832_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8832_cast = slice_by_index(begin = var_8832_begin_0, end = var_8832_end_0, end_mask = var_8832_end_mask_0, x = transpose_1)[name = tensor("op_8832_cast")]; + tensor var_8836_begin_0 = const()[name = tensor("op_8836_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_8836_end_0 = const()[name = tensor("op_8836_end_0"), val = tensor([2, 9216, 1, 120])]; + tensor var_8836_end_mask_0 = const()[name = tensor("op_8836_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8836_cast = slice_by_index(begin = var_8836_begin_0, end = var_8836_end_0, end_mask = var_8836_end_mask_0, x = transpose_1)[name = tensor("op_8836_cast")]; + tensor var_8840_begin_0 = const()[name = tensor("op_8840_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_8840_end_0 = const()[name = tensor("op_8840_end_0"), val = tensor([2, 9216, 1, 160])]; + tensor var_8840_end_mask_0 = const()[name = tensor("op_8840_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8840_cast = slice_by_index(begin = var_8840_begin_0, end = var_8840_end_0, end_mask = var_8840_end_mask_0, x = transpose_1)[name = tensor("op_8840_cast")]; + tensor var_8844_begin_0 = const()[name = tensor("op_8844_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_8844_end_0 = const()[name = tensor("op_8844_end_0"), val = tensor([2, 9216, 1, 200])]; + tensor var_8844_end_mask_0 = const()[name = tensor("op_8844_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8844_cast = slice_by_index(begin = var_8844_begin_0, end = var_8844_end_0, end_mask = var_8844_end_mask_0, x = transpose_1)[name = tensor("op_8844_cast")]; + tensor var_8848_begin_0 = const()[name = tensor("op_8848_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_8848_end_0 = const()[name = tensor("op_8848_end_0"), val = tensor([2, 9216, 1, 240])]; + tensor var_8848_end_mask_0 = const()[name = tensor("op_8848_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8848_cast = slice_by_index(begin = var_8848_begin_0, end = var_8848_end_0, end_mask = var_8848_end_mask_0, x = transpose_1)[name = tensor("op_8848_cast")]; + tensor var_8852_begin_0 = const()[name = tensor("op_8852_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_8852_end_0 = const()[name = tensor("op_8852_end_0"), val = tensor([2, 9216, 1, 280])]; + tensor var_8852_end_mask_0 = const()[name = tensor("op_8852_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8852_cast = slice_by_index(begin = var_8852_begin_0, end = var_8852_end_0, end_mask = var_8852_end_mask_0, x = transpose_1)[name = tensor("op_8852_cast")]; + tensor var_8856_begin_0 = const()[name = tensor("op_8856_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_8856_end_0 = const()[name = tensor("op_8856_end_0"), val = tensor([2, 9216, 1, 320])]; + tensor var_8856_end_mask_0 = const()[name = tensor("op_8856_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_8856_cast = slice_by_index(begin = var_8856_begin_0, end = var_8856_end_0, end_mask = var_8856_end_mask_0, x = transpose_1)[name = tensor("op_8856_cast")]; + tensor var_8858_begin_0 = const()[name = tensor("op_8858_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8858_end_0 = const()[name = tensor("op_8858_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_8858_end_mask_0 = const()[name = tensor("op_8858_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8858_cast = slice_by_index(begin = var_8858_begin_0, end = var_8858_end_0, end_mask = var_8858_end_mask_0, x = v_61_cast)[name = tensor("op_8858_cast")]; + tensor var_8862_begin_0 = const()[name = tensor("op_8862_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_8862_end_0 = const()[name = tensor("op_8862_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_8862_end_mask_0 = const()[name = tensor("op_8862_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8862_cast = slice_by_index(begin = var_8862_begin_0, end = var_8862_end_0, end_mask = var_8862_end_mask_0, x = v_61_cast)[name = tensor("op_8862_cast")]; + tensor var_8866_begin_0 = const()[name = tensor("op_8866_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_8866_end_0 = const()[name = tensor("op_8866_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_8866_end_mask_0 = const()[name = tensor("op_8866_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8866_cast = slice_by_index(begin = var_8866_begin_0, end = var_8866_end_0, end_mask = var_8866_end_mask_0, x = v_61_cast)[name = tensor("op_8866_cast")]; + tensor var_8870_begin_0 = const()[name = tensor("op_8870_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_8870_end_0 = const()[name = tensor("op_8870_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_8870_end_mask_0 = const()[name = tensor("op_8870_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8870_cast = slice_by_index(begin = var_8870_begin_0, end = var_8870_end_0, end_mask = var_8870_end_mask_0, x = v_61_cast)[name = tensor("op_8870_cast")]; + tensor var_8874_begin_0 = const()[name = tensor("op_8874_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_8874_end_0 = const()[name = tensor("op_8874_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_8874_end_mask_0 = const()[name = tensor("op_8874_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8874_cast = slice_by_index(begin = var_8874_begin_0, end = var_8874_end_0, end_mask = var_8874_end_mask_0, x = v_61_cast)[name = tensor("op_8874_cast")]; + tensor var_8878_begin_0 = const()[name = tensor("op_8878_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_8878_end_0 = const()[name = tensor("op_8878_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_8878_end_mask_0 = const()[name = tensor("op_8878_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8878_cast = slice_by_index(begin = var_8878_begin_0, end = var_8878_end_0, end_mask = var_8878_end_mask_0, x = v_61_cast)[name = tensor("op_8878_cast")]; + tensor var_8882_begin_0 = const()[name = tensor("op_8882_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_8882_end_0 = const()[name = tensor("op_8882_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_8882_end_mask_0 = const()[name = tensor("op_8882_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8882_cast = slice_by_index(begin = var_8882_begin_0, end = var_8882_end_0, end_mask = var_8882_end_mask_0, x = v_61_cast)[name = tensor("op_8882_cast")]; + tensor var_8886_begin_0 = const()[name = tensor("op_8886_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_8886_end_0 = const()[name = tensor("op_8886_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_8886_end_mask_0 = const()[name = tensor("op_8886_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8886_cast = slice_by_index(begin = var_8886_begin_0, end = var_8886_end_0, end_mask = var_8886_end_mask_0, x = v_61_cast)[name = tensor("op_8886_cast")]; + tensor var_8890_equation_0 = const()[name = tensor("op_8890_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8890_cast = einsum(equation = var_8890_equation_0, values = (var_8828_cast, var_8793_cast))[name = tensor("op_8890_cast")]; + tensor var_8891_to_fp16 = const()[name = tensor("op_8891_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_481_cast = mul(x = var_8890_cast, y = var_8891_to_fp16)[name = tensor("aw_481_cast")]; + tensor var_8894_equation_0 = const()[name = tensor("op_8894_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8894_cast = einsum(equation = var_8894_equation_0, values = (var_8832_cast, var_8797_cast))[name = tensor("op_8894_cast")]; + tensor var_8895_to_fp16 = const()[name = tensor("op_8895_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_483_cast = mul(x = var_8894_cast, y = var_8895_to_fp16)[name = tensor("aw_483_cast")]; + tensor var_8898_equation_0 = const()[name = tensor("op_8898_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8898_cast = einsum(equation = var_8898_equation_0, values = (var_8836_cast, var_8801_cast))[name = tensor("op_8898_cast")]; + tensor var_8899_to_fp16 = const()[name = tensor("op_8899_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_485_cast = mul(x = var_8898_cast, y = var_8899_to_fp16)[name = tensor("aw_485_cast")]; + tensor var_8902_equation_0 = const()[name = tensor("op_8902_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8902_cast = einsum(equation = var_8902_equation_0, values = (var_8840_cast, var_8805_cast))[name = tensor("op_8902_cast")]; + tensor var_8903_to_fp16 = const()[name = tensor("op_8903_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_487_cast = mul(x = var_8902_cast, y = var_8903_to_fp16)[name = tensor("aw_487_cast")]; + tensor var_8906_equation_0 = const()[name = tensor("op_8906_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8906_cast = einsum(equation = var_8906_equation_0, values = (var_8844_cast, var_8809_cast))[name = tensor("op_8906_cast")]; + tensor var_8907_to_fp16 = const()[name = tensor("op_8907_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_489_cast = mul(x = var_8906_cast, y = var_8907_to_fp16)[name = tensor("aw_489_cast")]; + tensor var_8910_equation_0 = const()[name = tensor("op_8910_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8910_cast = einsum(equation = var_8910_equation_0, values = (var_8848_cast, var_8813_cast))[name = tensor("op_8910_cast")]; + tensor var_8911_to_fp16 = const()[name = tensor("op_8911_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_491_cast = mul(x = var_8910_cast, y = var_8911_to_fp16)[name = tensor("aw_491_cast")]; + tensor var_8914_equation_0 = const()[name = tensor("op_8914_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8914_cast = einsum(equation = var_8914_equation_0, values = (var_8852_cast, var_8817_cast))[name = tensor("op_8914_cast")]; + tensor var_8915_to_fp16 = const()[name = tensor("op_8915_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_493_cast = mul(x = var_8914_cast, y = var_8915_to_fp16)[name = tensor("aw_493_cast")]; + tensor var_8918_equation_0 = const()[name = tensor("op_8918_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_8918_cast = einsum(equation = var_8918_equation_0, values = (var_8856_cast, var_8821_cast))[name = tensor("op_8918_cast")]; + tensor var_8919_to_fp16 = const()[name = tensor("op_8919_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_495_cast = mul(x = var_8918_cast, y = var_8919_to_fp16)[name = tensor("aw_495_cast")]; + tensor var_8921_cast = softmax(axis = var_7587, x = aw_481_cast)[name = tensor("op_8921_cast")]; + tensor var_8922_cast = softmax(axis = var_7587, x = aw_483_cast)[name = tensor("op_8922_cast")]; + tensor var_8923_cast = softmax(axis = var_7587, x = aw_485_cast)[name = tensor("op_8923_cast")]; + tensor var_8924_cast = softmax(axis = var_7587, x = aw_487_cast)[name = tensor("op_8924_cast")]; + tensor var_8925_cast = softmax(axis = var_7587, x = aw_489_cast)[name = tensor("op_8925_cast")]; + tensor var_8926_cast = softmax(axis = var_7587, x = aw_491_cast)[name = tensor("op_8926_cast")]; + tensor var_8927_cast = softmax(axis = var_7587, x = aw_493_cast)[name = tensor("op_8927_cast")]; + tensor var_8928_cast = softmax(axis = var_7587, x = aw_495_cast)[name = tensor("op_8928_cast")]; + tensor var_8930_equation_0 = const()[name = tensor("op_8930_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8930_cast = einsum(equation = var_8930_equation_0, values = (var_8858_cast, var_8921_cast))[name = tensor("op_8930_cast")]; + tensor var_8932_equation_0 = const()[name = tensor("op_8932_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8932_cast = einsum(equation = var_8932_equation_0, values = (var_8862_cast, var_8922_cast))[name = tensor("op_8932_cast")]; + tensor var_8934_equation_0 = const()[name = tensor("op_8934_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8934_cast = einsum(equation = var_8934_equation_0, values = (var_8866_cast, var_8923_cast))[name = tensor("op_8934_cast")]; + tensor var_8936_equation_0 = const()[name = tensor("op_8936_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8936_cast = einsum(equation = var_8936_equation_0, values = (var_8870_cast, var_8924_cast))[name = tensor("op_8936_cast")]; + tensor var_8938_equation_0 = const()[name = tensor("op_8938_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8938_cast = einsum(equation = var_8938_equation_0, values = (var_8874_cast, var_8925_cast))[name = tensor("op_8938_cast")]; + tensor var_8940_equation_0 = const()[name = tensor("op_8940_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8940_cast = einsum(equation = var_8940_equation_0, values = (var_8878_cast, var_8926_cast))[name = tensor("op_8940_cast")]; + tensor var_8942_equation_0 = const()[name = tensor("op_8942_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8942_cast = einsum(equation = var_8942_equation_0, values = (var_8882_cast, var_8927_cast))[name = tensor("op_8942_cast")]; + tensor var_8944_equation_0 = const()[name = tensor("op_8944_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_8944_cast = einsum(equation = var_8944_equation_0, values = (var_8886_cast, var_8928_cast))[name = tensor("op_8944_cast")]; + tensor input_517_interleave_0 = const()[name = tensor("input_517_interleave_0"), val = tensor(false)]; + tensor input_517_cast = concat(axis = var_7587, interleave = input_517_interleave_0, values = (var_8930_cast, var_8932_cast, var_8934_cast, var_8936_cast, var_8938_cast, var_8940_cast, var_8942_cast, var_8944_cast))[name = tensor("input_517_cast")]; + tensor var_8950 = const()[name = tensor("op_8950"), val = tensor([1, 1])]; + tensor var_8952 = const()[name = tensor("op_8952"), val = tensor([1, 1])]; + tensor var_8954_pad_type_0 = const()[name = tensor("op_8954_pad_type_0"), val = tensor("custom")]; + tensor var_8954_pad_0 = const()[name = tensor("op_8954_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643550016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643626880))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643627072)))]; + tensor var_8954_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8952, groups = var_7587, pad = var_8954_pad_0, pad_type = var_8954_pad_type_0, strides = var_8950, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_517_cast)[name = tensor("op_8954_cast")]; + tensor inputs_93_cast = add(x = var_8954_cast, y = inputs_91_cast)[name = tensor("inputs_93_cast")]; + tensor var_8958 = const()[name = tensor("op_8958"), val = tensor([1])]; + tensor channels_mean_93_cast = reduce_mean(axes = var_8958, keep_dims = var_7582, x = inputs_93_cast)[name = tensor("channels_mean_93_cast")]; + tensor zero_mean_93_cast = sub(x = inputs_93_cast, y = channels_mean_93_cast)[name = tensor("zero_mean_93_cast")]; + tensor zero_mean_sq_93_cast = mul(x = zero_mean_93_cast, y = zero_mean_93_cast)[name = tensor("zero_mean_sq_93_cast")]; + tensor var_8962 = const()[name = tensor("op_8962"), val = tensor([1])]; + tensor var_8963_cast = reduce_mean(axes = var_8962, keep_dims = var_7582, x = zero_mean_sq_93_cast)[name = tensor("op_8963_cast")]; + tensor var_8964_to_fp16 = const()[name = tensor("op_8964_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8965_cast = add(x = var_8963_cast, y = var_8964_to_fp16)[name = tensor("op_8965_cast")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_8965_cast)[name = tensor("denom_93_cast")]; + tensor out_93_cast = mul(x = zero_mean_93_cast, y = denom_93_cast)[name = tensor("out_93_cast")]; + tensor var_8969_to_fp16 = const()[name = tensor("op_8969_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643627776)))]; + tensor var_8970_cast = add(x = out_93_cast, y = var_8969_to_fp16)[name = tensor("op_8970_cast")]; + tensor var_8972_to_fp16 = const()[name = tensor("op_8972_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643628480)))]; + tensor hidden_states_329_cast = mul(x = var_8970_cast, y = var_8972_to_fp16)[name = tensor("hidden_states_329_cast")]; + tensor var_8979 = const()[name = tensor("op_8979"), val = tensor([1, 1])]; + tensor var_8981 = const()[name = tensor("op_8981"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643629184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643706048))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_cast = conv(dilations = var_8981, groups = var_7587, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_8979, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_329_cast)[name = tensor("q_cast")]; + tensor var_8985 = const()[name = tensor("op_8985"), val = tensor([1, 1])]; + tensor var_8987 = const()[name = tensor("op_8987"), val = tensor([1, 1])]; + tensor k_125_pad_type_0 = const()[name = tensor("k_125_pad_type_0"), val = tensor("custom")]; + tensor k_125_pad_0 = const()[name = tensor("k_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643706240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643890624))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_125_cast = conv(dilations = var_8987, groups = var_7587, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_8985, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_125_cast")]; + tensor var_8991 = const()[name = tensor("op_8991"), val = tensor([1, 1])]; + tensor var_8993 = const()[name = tensor("op_8993"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643890816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644075200))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_cast = conv(dilations = var_8993, groups = var_7587, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_8991, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_cast")]; + tensor var_8997_begin_0 = const()[name = tensor("op_8997_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_8997_end_0 = const()[name = tensor("op_8997_end_0"), val = tensor([2, 40, 1, 9216])]; + tensor var_8997_end_mask_0 = const()[name = tensor("op_8997_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_8997_cast = slice_by_index(begin = var_8997_begin_0, end = var_8997_end_0, end_mask = var_8997_end_mask_0, x = q_cast)[name = tensor("op_8997_cast")]; + tensor var_9001_begin_0 = const()[name = tensor("op_9001_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_9001_end_0 = const()[name = tensor("op_9001_end_0"), val = tensor([2, 80, 1, 9216])]; + tensor var_9001_end_mask_0 = const()[name = tensor("op_9001_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9001_cast = slice_by_index(begin = var_9001_begin_0, end = var_9001_end_0, end_mask = var_9001_end_mask_0, x = q_cast)[name = tensor("op_9001_cast")]; + tensor var_9005_begin_0 = const()[name = tensor("op_9005_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_9005_end_0 = const()[name = tensor("op_9005_end_0"), val = tensor([2, 120, 1, 9216])]; + tensor var_9005_end_mask_0 = const()[name = tensor("op_9005_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9005_cast = slice_by_index(begin = var_9005_begin_0, end = var_9005_end_0, end_mask = var_9005_end_mask_0, x = q_cast)[name = tensor("op_9005_cast")]; + tensor var_9009_begin_0 = const()[name = tensor("op_9009_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_9009_end_0 = const()[name = tensor("op_9009_end_0"), val = tensor([2, 160, 1, 9216])]; + tensor var_9009_end_mask_0 = const()[name = tensor("op_9009_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9009_cast = slice_by_index(begin = var_9009_begin_0, end = var_9009_end_0, end_mask = var_9009_end_mask_0, x = q_cast)[name = tensor("op_9009_cast")]; + tensor var_9013_begin_0 = const()[name = tensor("op_9013_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_9013_end_0 = const()[name = tensor("op_9013_end_0"), val = tensor([2, 200, 1, 9216])]; + tensor var_9013_end_mask_0 = const()[name = tensor("op_9013_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9013_cast = slice_by_index(begin = var_9013_begin_0, end = var_9013_end_0, end_mask = var_9013_end_mask_0, x = q_cast)[name = tensor("op_9013_cast")]; + tensor var_9017_begin_0 = const()[name = tensor("op_9017_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_9017_end_0 = const()[name = tensor("op_9017_end_0"), val = tensor([2, 240, 1, 9216])]; + tensor var_9017_end_mask_0 = const()[name = tensor("op_9017_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9017_cast = slice_by_index(begin = var_9017_begin_0, end = var_9017_end_0, end_mask = var_9017_end_mask_0, x = q_cast)[name = tensor("op_9017_cast")]; + tensor var_9021_begin_0 = const()[name = tensor("op_9021_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_9021_end_0 = const()[name = tensor("op_9021_end_0"), val = tensor([2, 280, 1, 9216])]; + tensor var_9021_end_mask_0 = const()[name = tensor("op_9021_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9021_cast = slice_by_index(begin = var_9021_begin_0, end = var_9021_end_0, end_mask = var_9021_end_mask_0, x = q_cast)[name = tensor("op_9021_cast")]; + tensor var_9025_begin_0 = const()[name = tensor("op_9025_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_9025_end_0 = const()[name = tensor("op_9025_end_0"), val = tensor([2, 320, 1, 9216])]; + tensor var_9025_end_mask_0 = const()[name = tensor("op_9025_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9025_cast = slice_by_index(begin = var_9025_begin_0, end = var_9025_end_0, end_mask = var_9025_end_mask_0, x = q_cast)[name = tensor("op_9025_cast")]; + tensor k_perm_0 = const()[name = tensor("k_perm_0"), val = tensor([0, 3, 2, 1])]; + tensor var_9032_begin_0 = const()[name = tensor("op_9032_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_9032_end_0 = const()[name = tensor("op_9032_end_0"), val = tensor([2, 77, 1, 40])]; + tensor var_9032_end_mask_0 = const()[name = tensor("op_9032_end_mask_0"), val = tensor([true, true, true, false])]; + tensor transpose_0 = transpose(perm = k_perm_0, x = k_125_cast)[name = tensor("transpose_0")]; + tensor var_9032_cast = slice_by_index(begin = var_9032_begin_0, end = var_9032_end_0, end_mask = var_9032_end_mask_0, x = transpose_0)[name = tensor("op_9032_cast")]; + tensor var_9036_begin_0 = const()[name = tensor("op_9036_begin_0"), val = tensor([0, 0, 0, 40])]; + tensor var_9036_end_0 = const()[name = tensor("op_9036_end_0"), val = tensor([2, 77, 1, 80])]; + tensor var_9036_end_mask_0 = const()[name = tensor("op_9036_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_9036_cast = slice_by_index(begin = var_9036_begin_0, end = var_9036_end_0, end_mask = var_9036_end_mask_0, x = transpose_0)[name = tensor("op_9036_cast")]; + tensor var_9040_begin_0 = const()[name = tensor("op_9040_begin_0"), val = tensor([0, 0, 0, 80])]; + tensor var_9040_end_0 = const()[name = tensor("op_9040_end_0"), val = tensor([2, 77, 1, 120])]; + tensor var_9040_end_mask_0 = const()[name = tensor("op_9040_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_9040_cast = slice_by_index(begin = var_9040_begin_0, end = var_9040_end_0, end_mask = var_9040_end_mask_0, x = transpose_0)[name = tensor("op_9040_cast")]; + tensor var_9044_begin_0 = const()[name = tensor("op_9044_begin_0"), val = tensor([0, 0, 0, 120])]; + tensor var_9044_end_0 = const()[name = tensor("op_9044_end_0"), val = tensor([2, 77, 1, 160])]; + tensor var_9044_end_mask_0 = const()[name = tensor("op_9044_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_9044_cast = slice_by_index(begin = var_9044_begin_0, end = var_9044_end_0, end_mask = var_9044_end_mask_0, x = transpose_0)[name = tensor("op_9044_cast")]; + tensor var_9048_begin_0 = const()[name = tensor("op_9048_begin_0"), val = tensor([0, 0, 0, 160])]; + tensor var_9048_end_0 = const()[name = tensor("op_9048_end_0"), val = tensor([2, 77, 1, 200])]; + tensor var_9048_end_mask_0 = const()[name = tensor("op_9048_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_9048_cast = slice_by_index(begin = var_9048_begin_0, end = var_9048_end_0, end_mask = var_9048_end_mask_0, x = transpose_0)[name = tensor("op_9048_cast")]; + tensor var_9052_begin_0 = const()[name = tensor("op_9052_begin_0"), val = tensor([0, 0, 0, 200])]; + tensor var_9052_end_0 = const()[name = tensor("op_9052_end_0"), val = tensor([2, 77, 1, 240])]; + tensor var_9052_end_mask_0 = const()[name = tensor("op_9052_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_9052_cast = slice_by_index(begin = var_9052_begin_0, end = var_9052_end_0, end_mask = var_9052_end_mask_0, x = transpose_0)[name = tensor("op_9052_cast")]; + tensor var_9056_begin_0 = const()[name = tensor("op_9056_begin_0"), val = tensor([0, 0, 0, 240])]; + tensor var_9056_end_0 = const()[name = tensor("op_9056_end_0"), val = tensor([2, 77, 1, 280])]; + tensor var_9056_end_mask_0 = const()[name = tensor("op_9056_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_9056_cast = slice_by_index(begin = var_9056_begin_0, end = var_9056_end_0, end_mask = var_9056_end_mask_0, x = transpose_0)[name = tensor("op_9056_cast")]; + tensor var_9060_begin_0 = const()[name = tensor("op_9060_begin_0"), val = tensor([0, 0, 0, 280])]; + tensor var_9060_end_0 = const()[name = tensor("op_9060_end_0"), val = tensor([2, 77, 1, 320])]; + tensor var_9060_end_mask_0 = const()[name = tensor("op_9060_end_mask_0"), val = tensor([true, true, true, false])]; + tensor var_9060_cast = slice_by_index(begin = var_9060_begin_0, end = var_9060_end_0, end_mask = var_9060_end_mask_0, x = transpose_0)[name = tensor("op_9060_cast")]; + tensor var_9062_begin_0 = const()[name = tensor("op_9062_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_9062_end_0 = const()[name = tensor("op_9062_end_0"), val = tensor([2, 40, 1, 77])]; + tensor var_9062_end_mask_0 = const()[name = tensor("op_9062_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9062_cast = slice_by_index(begin = var_9062_begin_0, end = var_9062_end_0, end_mask = var_9062_end_mask_0, x = v_cast)[name = tensor("op_9062_cast")]; + tensor var_9066_begin_0 = const()[name = tensor("op_9066_begin_0"), val = tensor([0, 40, 0, 0])]; + tensor var_9066_end_0 = const()[name = tensor("op_9066_end_0"), val = tensor([2, 80, 1, 77])]; + tensor var_9066_end_mask_0 = const()[name = tensor("op_9066_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9066_cast = slice_by_index(begin = var_9066_begin_0, end = var_9066_end_0, end_mask = var_9066_end_mask_0, x = v_cast)[name = tensor("op_9066_cast")]; + tensor var_9070_begin_0 = const()[name = tensor("op_9070_begin_0"), val = tensor([0, 80, 0, 0])]; + tensor var_9070_end_0 = const()[name = tensor("op_9070_end_0"), val = tensor([2, 120, 1, 77])]; + tensor var_9070_end_mask_0 = const()[name = tensor("op_9070_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9070_cast = slice_by_index(begin = var_9070_begin_0, end = var_9070_end_0, end_mask = var_9070_end_mask_0, x = v_cast)[name = tensor("op_9070_cast")]; + tensor var_9074_begin_0 = const()[name = tensor("op_9074_begin_0"), val = tensor([0, 120, 0, 0])]; + tensor var_9074_end_0 = const()[name = tensor("op_9074_end_0"), val = tensor([2, 160, 1, 77])]; + tensor var_9074_end_mask_0 = const()[name = tensor("op_9074_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9074_cast = slice_by_index(begin = var_9074_begin_0, end = var_9074_end_0, end_mask = var_9074_end_mask_0, x = v_cast)[name = tensor("op_9074_cast")]; + tensor var_9078_begin_0 = const()[name = tensor("op_9078_begin_0"), val = tensor([0, 160, 0, 0])]; + tensor var_9078_end_0 = const()[name = tensor("op_9078_end_0"), val = tensor([2, 200, 1, 77])]; + tensor var_9078_end_mask_0 = const()[name = tensor("op_9078_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9078_cast = slice_by_index(begin = var_9078_begin_0, end = var_9078_end_0, end_mask = var_9078_end_mask_0, x = v_cast)[name = tensor("op_9078_cast")]; + tensor var_9082_begin_0 = const()[name = tensor("op_9082_begin_0"), val = tensor([0, 200, 0, 0])]; + tensor var_9082_end_0 = const()[name = tensor("op_9082_end_0"), val = tensor([2, 240, 1, 77])]; + tensor var_9082_end_mask_0 = const()[name = tensor("op_9082_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9082_cast = slice_by_index(begin = var_9082_begin_0, end = var_9082_end_0, end_mask = var_9082_end_mask_0, x = v_cast)[name = tensor("op_9082_cast")]; + tensor var_9086_begin_0 = const()[name = tensor("op_9086_begin_0"), val = tensor([0, 240, 0, 0])]; + tensor var_9086_end_0 = const()[name = tensor("op_9086_end_0"), val = tensor([2, 280, 1, 77])]; + tensor var_9086_end_mask_0 = const()[name = tensor("op_9086_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9086_cast = slice_by_index(begin = var_9086_begin_0, end = var_9086_end_0, end_mask = var_9086_end_mask_0, x = v_cast)[name = tensor("op_9086_cast")]; + tensor var_9090_begin_0 = const()[name = tensor("op_9090_begin_0"), val = tensor([0, 280, 0, 0])]; + tensor var_9090_end_0 = const()[name = tensor("op_9090_end_0"), val = tensor([2, 320, 1, 77])]; + tensor var_9090_end_mask_0 = const()[name = tensor("op_9090_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_9090_cast = slice_by_index(begin = var_9090_begin_0, end = var_9090_end_0, end_mask = var_9090_end_mask_0, x = v_cast)[name = tensor("op_9090_cast")]; + tensor var_9094_equation_0 = const()[name = tensor("op_9094_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9094_cast = einsum(equation = var_9094_equation_0, values = (var_9032_cast, var_8997_cast))[name = tensor("op_9094_cast")]; + tensor var_9095_to_fp16 = const()[name = tensor("op_9095_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_497_cast = mul(x = var_9094_cast, y = var_9095_to_fp16)[name = tensor("aw_497_cast")]; + tensor var_9098_equation_0 = const()[name = tensor("op_9098_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9098_cast = einsum(equation = var_9098_equation_0, values = (var_9036_cast, var_9001_cast))[name = tensor("op_9098_cast")]; + tensor var_9099_to_fp16 = const()[name = tensor("op_9099_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_499_cast = mul(x = var_9098_cast, y = var_9099_to_fp16)[name = tensor("aw_499_cast")]; + tensor var_9102_equation_0 = const()[name = tensor("op_9102_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9102_cast = einsum(equation = var_9102_equation_0, values = (var_9040_cast, var_9005_cast))[name = tensor("op_9102_cast")]; + tensor var_9103_to_fp16 = const()[name = tensor("op_9103_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_501_cast = mul(x = var_9102_cast, y = var_9103_to_fp16)[name = tensor("aw_501_cast")]; + tensor var_9106_equation_0 = const()[name = tensor("op_9106_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9106_cast = einsum(equation = var_9106_equation_0, values = (var_9044_cast, var_9009_cast))[name = tensor("op_9106_cast")]; + tensor var_9107_to_fp16 = const()[name = tensor("op_9107_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_503_cast = mul(x = var_9106_cast, y = var_9107_to_fp16)[name = tensor("aw_503_cast")]; + tensor var_9110_equation_0 = const()[name = tensor("op_9110_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9110_cast = einsum(equation = var_9110_equation_0, values = (var_9048_cast, var_9013_cast))[name = tensor("op_9110_cast")]; + tensor var_9111_to_fp16 = const()[name = tensor("op_9111_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_505_cast = mul(x = var_9110_cast, y = var_9111_to_fp16)[name = tensor("aw_505_cast")]; + tensor var_9114_equation_0 = const()[name = tensor("op_9114_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9114_cast = einsum(equation = var_9114_equation_0, values = (var_9052_cast, var_9017_cast))[name = tensor("op_9114_cast")]; + tensor var_9115_to_fp16 = const()[name = tensor("op_9115_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_507_cast = mul(x = var_9114_cast, y = var_9115_to_fp16)[name = tensor("aw_507_cast")]; + tensor var_9118_equation_0 = const()[name = tensor("op_9118_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9118_cast = einsum(equation = var_9118_equation_0, values = (var_9056_cast, var_9021_cast))[name = tensor("op_9118_cast")]; + tensor var_9119_to_fp16 = const()[name = tensor("op_9119_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_509_cast = mul(x = var_9118_cast, y = var_9119_to_fp16)[name = tensor("aw_509_cast")]; + tensor var_9122_equation_0 = const()[name = tensor("op_9122_equation_0"), val = tensor("bkhc,bchq->bkhq")]; + tensor var_9122_cast = einsum(equation = var_9122_equation_0, values = (var_9060_cast, var_9025_cast))[name = tensor("op_9122_cast")]; + tensor var_9123_to_fp16 = const()[name = tensor("op_9123_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor aw_cast = mul(x = var_9122_cast, y = var_9123_to_fp16)[name = tensor("aw_cast")]; + tensor var_9125_cast = softmax(axis = var_7587, x = aw_497_cast)[name = tensor("op_9125_cast")]; + tensor var_9126_cast = softmax(axis = var_7587, x = aw_499_cast)[name = tensor("op_9126_cast")]; + tensor var_9127_cast = softmax(axis = var_7587, x = aw_501_cast)[name = tensor("op_9127_cast")]; + tensor var_9128_cast = softmax(axis = var_7587, x = aw_503_cast)[name = tensor("op_9128_cast")]; + tensor var_9129_cast = softmax(axis = var_7587, x = aw_505_cast)[name = tensor("op_9129_cast")]; + tensor var_9130_cast = softmax(axis = var_7587, x = aw_507_cast)[name = tensor("op_9130_cast")]; + tensor var_9131_cast = softmax(axis = var_7587, x = aw_509_cast)[name = tensor("op_9131_cast")]; + tensor var_9132_cast = softmax(axis = var_7587, x = aw_cast)[name = tensor("op_9132_cast")]; + tensor var_9134_equation_0 = const()[name = tensor("op_9134_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9134_cast = einsum(equation = var_9134_equation_0, values = (var_9062_cast, var_9125_cast))[name = tensor("op_9134_cast")]; + tensor var_9136_equation_0 = const()[name = tensor("op_9136_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9136_cast = einsum(equation = var_9136_equation_0, values = (var_9066_cast, var_9126_cast))[name = tensor("op_9136_cast")]; + tensor var_9138_equation_0 = const()[name = tensor("op_9138_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9138_cast = einsum(equation = var_9138_equation_0, values = (var_9070_cast, var_9127_cast))[name = tensor("op_9138_cast")]; + tensor var_9140_equation_0 = const()[name = tensor("op_9140_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9140_cast = einsum(equation = var_9140_equation_0, values = (var_9074_cast, var_9128_cast))[name = tensor("op_9140_cast")]; + tensor var_9142_equation_0 = const()[name = tensor("op_9142_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9142_cast = einsum(equation = var_9142_equation_0, values = (var_9078_cast, var_9129_cast))[name = tensor("op_9142_cast")]; + tensor var_9144_equation_0 = const()[name = tensor("op_9144_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9144_cast = einsum(equation = var_9144_equation_0, values = (var_9082_cast, var_9130_cast))[name = tensor("op_9144_cast")]; + tensor var_9146_equation_0 = const()[name = tensor("op_9146_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9146_cast = einsum(equation = var_9146_equation_0, values = (var_9086_cast, var_9131_cast))[name = tensor("op_9146_cast")]; + tensor var_9148_equation_0 = const()[name = tensor("op_9148_equation_0"), val = tensor("bchk,bkhq->bchq")]; + tensor var_9148_cast = einsum(equation = var_9148_equation_0, values = (var_9090_cast, var_9132_cast))[name = tensor("op_9148_cast")]; + tensor input_519_interleave_0 = const()[name = tensor("input_519_interleave_0"), val = tensor(false)]; + tensor input_519_cast = concat(axis = var_7587, interleave = input_519_interleave_0, values = (var_9134_cast, var_9136_cast, var_9138_cast, var_9140_cast, var_9142_cast, var_9144_cast, var_9146_cast, var_9148_cast))[name = tensor("input_519_cast")]; + tensor var_9154 = const()[name = tensor("op_9154"), val = tensor([1, 1])]; + tensor var_9156 = const()[name = tensor("op_9156"), val = tensor([1, 1])]; + tensor var_9158_pad_type_0 = const()[name = tensor("op_9158_pad_type_0"), val = tensor("custom")]; + tensor var_9158_pad_0 = const()[name = tensor("op_9158_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644075392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644152256))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644152448)))]; + tensor var_9158_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_9156, groups = var_7587, pad = var_9158_pad_0, pad_type = var_9158_pad_type_0, strides = var_9154, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_519_cast)[name = tensor("op_9158_cast")]; + tensor inputs_cast = add(x = var_9158_cast, y = inputs_93_cast)[name = tensor("inputs_cast")]; + tensor var_9162 = const()[name = tensor("op_9162"), val = tensor([1])]; + tensor channels_mean_cast = reduce_mean(axes = var_9162, keep_dims = var_7582, x = inputs_cast)[name = tensor("channels_mean_cast")]; + tensor zero_mean_cast = sub(x = inputs_cast, y = channels_mean_cast)[name = tensor("zero_mean_cast")]; + tensor zero_mean_sq_cast = mul(x = zero_mean_cast, y = zero_mean_cast)[name = tensor("zero_mean_sq_cast")]; + tensor var_9166 = const()[name = tensor("op_9166"), val = tensor([1])]; + tensor var_9167_cast = reduce_mean(axes = var_9166, keep_dims = var_7582, x = zero_mean_sq_cast)[name = tensor("op_9167_cast")]; + tensor var_9168_to_fp16 = const()[name = tensor("op_9168_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9169_cast = add(x = var_9167_cast, y = var_9168_to_fp16)[name = tensor("op_9169_cast")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_9169_cast)[name = tensor("denom_cast")]; + tensor out_cast = mul(x = zero_mean_cast, y = denom_cast)[name = tensor("out_cast")]; + tensor var_9173_to_fp16 = const()[name = tensor("op_9173_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644153152)))]; + tensor var_9174_cast = add(x = out_cast, y = var_9173_to_fp16)[name = tensor("op_9174_cast")]; + tensor var_9176_to_fp16 = const()[name = tensor("op_9176_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644153856)))]; + tensor input_521_cast = mul(x = var_9174_cast, y = var_9176_to_fp16)[name = tensor("input_521_cast")]; + tensor var_9184 = const()[name = tensor("op_9184"), val = tensor([1, 1])]; + tensor var_9186 = const()[name = tensor("op_9186"), val = tensor([1, 1])]; + tensor var_9188_pad_type_0 = const()[name = tensor("op_9188_pad_type_0"), val = tensor("custom")]; + tensor var_9188_pad_0 = const()[name = tensor("op_9188_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644154560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644769024))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644769216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644771200))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_9188_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9186, groups = var_7587, pad = var_9188_pad_0, pad_type = var_9188_pad_type_0, strides = var_9184, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_521_cast)[name = tensor("op_9188_cast")]; + tensor var_9189_split_sizes_0 = const()[name = tensor("op_9189_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_9189_axis_0 = const()[name = tensor("op_9189_axis_0"), val = tensor(1)]; + tensor var_9189_cast_0, tensor var_9189_cast_1 = split(axis = var_9189_axis_0, split_sizes = var_9189_split_sizes_0, x = var_9188_cast)[name = tensor("op_9189_cast")]; + tensor var_9191_mode_0 = const()[name = tensor("op_9191_mode_0"), val = tensor("EXACT")]; + tensor var_9191_cast = gelu(mode = var_9191_mode_0, x = var_9189_cast_1)[name = tensor("op_9191_cast")]; + tensor input_523_cast = mul(x = var_9189_cast_0, y = var_9191_cast)[name = tensor("input_523_cast")]; + tensor var_9195 = const()[name = tensor("op_9195"), val = tensor([1, 1])]; + tensor var_9197 = const()[name = tensor("op_9197"), val = tensor([1, 1])]; + tensor var_9199_pad_type_0 = const()[name = tensor("op_9199_pad_type_0"), val = tensor("custom")]; + tensor var_9199_pad_0 = const()[name = tensor("op_9199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644771392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645078656))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645078848)))]; + tensor var_9199_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_9197, groups = var_7587, pad = var_9199_pad_0, pad_type = var_9199_pad_type_0, strides = var_9195, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_523_cast)[name = tensor("op_9199_cast")]; + tensor hidden_states_333_cast = add(x = var_9199_cast, y = inputs_cast)[name = tensor("hidden_states_333_cast")]; + tensor var_9201 = const()[name = tensor("op_9201"), val = tensor([2, 320, 96, 96])]; + tensor input_525_cast = reshape(shape = var_9201, x = hidden_states_333_cast)[name = tensor("input_525_cast")]; + tensor var_9205 = const()[name = tensor("op_9205"), val = tensor([1, 1])]; + tensor var_9207 = const()[name = tensor("op_9207"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645079552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645156416))), name = tensor("up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645156608)))]; + tensor hidden_states_cast = conv(bias = up_blocks_3_attentions_2_proj_out_bias_to_fp16, dilations = var_9207, groups = var_7587, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_9205, weight = up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized, x = input_525_cast)[name = tensor("hidden_states_cast")]; + tensor input_527_cast = add(x = hidden_states_cast, y = hidden_states_323_cast)[name = tensor("input_527_cast")]; + tensor reshape_240_shape_0 = const()[name = tensor("reshape_240_shape_0"), val = tensor([2, 32, 10, 96, 96])]; + tensor reshape_240_cast = reshape(shape = reshape_240_shape_0, x = input_527_cast)[name = tensor("reshape_240_cast")]; + tensor reduce_mean_180_axes_0 = const()[name = tensor("reduce_mean_180_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_180_keep_dims_0 = const()[name = tensor("reduce_mean_180_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_180_cast")]; + tensor sub_120_cast = sub(x = reshape_240_cast, y = reduce_mean_180_cast)[name = tensor("sub_120_cast")]; + tensor square_60_cast = square(x = sub_120_cast)[name = tensor("square_60_cast")]; + tensor reduce_mean_182_axes_0 = const()[name = tensor("reduce_mean_182_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_182_keep_dims_0 = const()[name = tensor("reduce_mean_182_keep_dims_0"), val = tensor(true)]; + tensor 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("reduce_mean_182_cast")]; + tensor add_120_y_0_to_fp16 = const()[name = tensor("add_120_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_120_cast = add(x = reduce_mean_182_cast, y = add_120_y_0_to_fp16)[name = tensor("add_120_cast")]; + tensor sqrt_60_cast = sqrt(x = add_120_cast)[name = tensor("sqrt_60_cast")]; + tensor real_div_60_cast = real_div(x = sub_120_cast, y = sqrt_60_cast)[name = tensor("real_div_60_cast")]; + tensor reshape_241_shape_0 = const()[name = tensor("reshape_241_shape_0"), val = tensor([2, 320, 96, 96])]; + tensor reshape_241_cast = reshape(shape = reshape_241_shape_0, x = real_div_60_cast)[name = tensor("reshape_241_cast")]; + tensor add_121_gamma_0_to_fp16 = const()[name = tensor("add_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645157312)))]; + tensor add_121_beta_0_to_fp16 = const()[name = tensor("add_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645158016)))]; + tensor add_121_epsilon_0_to_fp16 = const()[name = tensor("add_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor 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("add_121_cast")]; + tensor input_cast = silu(x = add_121_cast)[name = tensor("input_cast")]; + tensor var_9221 = const()[name = tensor("op_9221"), val = tensor(1)]; + tensor var_9224 = const()[name = tensor("op_9224"), val = tensor([1, 1])]; + tensor var_9226 = const()[name = tensor("op_9226"), val = tensor([1, 1])]; + tensor var_9228_pad_type_0 = const()[name = tensor("op_9228_pad_type_0"), val = tensor("custom")]; + tensor var_9228_pad_0 = const()[name = tensor("op_9228_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645158720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645167424))), name = tensor("conv_out_weight_to_fp16_palettized"), shape = tensor([4, 320, 3, 3])]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([-0x1.7ecp-10, -0x1.814p-10, -0x1.b9p-13, -0x1.774p-9])]; + tensor var_9228_cast = conv(bias = conv_out_bias_to_fp16, dilations = var_9226, groups = var_9221, pad = var_9228_pad_0, pad_type = var_9228_pad_type_0, strides = var_9224, weight = conv_out_weight_to_fp16_palettized, x = input_cast)[name = tensor("op_9228_cast")]; + tensor var_9228_cast_to_fp32_dtype_0 = const()[name = tensor("op_9228_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_9228_cast_to_fp32_dtype_0, x = var_9228_cast)[name = tensor("cast_0")]; + } -> (noise_pred); +} \ No newline at end of file