diff --git "a/unet/coreml_model.mlmodelc/model.mil" "b/unet/coreml_model.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/unet/coreml_model.mlmodelc/model.mil" @@ -0,0 +1,10234 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}})] +{ + func main(tensor additional_residual_0, tensor additional_residual_1, tensor additional_residual_2, tensor additional_residual_3, tensor additional_residual_4, tensor additional_residual_5, tensor additional_residual_6, tensor additional_residual_7, tensor additional_residual_8, tensor additional_residual_9, tensor encoder_hidden_states, tensor sample, tensor text_embeds, tensor time_ids, tensor timestep) { + tensor var_34 = const()[name = tensor("op_34"), val = tensor(-1)]; + tensor var_51_axes_0 = const()[name = tensor("op_51_axes_0"), val = tensor([1])]; + tensor var_51_cast_fp16 = expand_dims(axes = var_51_axes_0, x = timestep)[name = tensor("op_51_cast_fp16")]; + tensor var_53_to_fp16 = const()[name = tensor("op_53_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast_fp16 = mul(x = var_51_cast_fp16, y = var_53_to_fp16)[name = tensor("emb_3_cast_fp16")]; + tensor var_58_cast_fp16 = sin(x = emb_3_cast_fp16)[name = tensor("op_58_cast_fp16")]; + tensor var_59_cast_fp16 = cos(x = emb_3_cast_fp16)[name = tensor("op_59_cast_fp16")]; + tensor emb_7_interleave_0 = const()[name = tensor("emb_7_interleave_0"), val = tensor(false)]; + tensor emb_7_cast_fp16 = concat(axis = var_34, interleave = emb_7_interleave_0, values = (var_58_cast_fp16, var_59_cast_fp16))[name = tensor("emb_7_cast_fp16")]; + tensor var_63_begin_0 = const()[name = tensor("op_63_begin_0"), val = tensor([0, 160])]; + tensor var_63_end_0 = const()[name = tensor("op_63_end_0"), val = tensor([1, 320])]; + tensor var_63_end_mask_0 = const()[name = tensor("op_63_end_mask_0"), val = tensor([true, true])]; + tensor var_63_cast_fp16 = slice_by_index(begin = var_63_begin_0, end = var_63_end_0, end_mask = var_63_end_mask_0, x = emb_7_cast_fp16)[name = tensor("op_63_cast_fp16")]; + tensor var_65_begin_0 = const()[name = tensor("op_65_begin_0"), val = tensor([0, 0])]; + tensor var_65_end_0 = const()[name = tensor("op_65_end_0"), val = tensor([1, 160])]; + tensor var_65_end_mask_0 = const()[name = tensor("op_65_end_mask_0"), val = tensor([true, false])]; + tensor var_65_cast_fp16 = slice_by_index(begin = var_65_begin_0, end = var_65_end_0, end_mask = var_65_end_mask_0, x = emb_7_cast_fp16)[name = tensor("op_65_cast_fp16")]; + tensor sample_1_interleave_0 = const()[name = tensor("sample_1_interleave_0"), val = tensor(false)]; + tensor sample_1_cast_fp16 = concat(axis = var_34, interleave = sample_1_interleave_0, values = (var_63_cast_fp16, var_65_cast_fp16))[name = tensor("sample_1_cast_fp16")]; + tensor var_68 = const()[name = tensor("op_68"), val = tensor(1)]; + tensor var_75_axes_0 = const()[name = tensor("op_75_axes_0"), val = tensor([-1])]; + tensor var_75_cast_fp16 = expand_dims(axes = var_75_axes_0, x = sample_1_cast_fp16)[name = tensor("op_75_cast_fp16")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = var_75_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor var_81 = const()[name = tensor("op_81"), 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_fp16 = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_81, groups = var_68, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_79, weight = time_embedding_linear_1_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = silu(x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_87 = const()[name = tensor("op_87"), val = tensor([1, 1])]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([1, 1])]; + tensor emb_pad_type_0 = const()[name = tensor("emb_pad_type_0"), val = tensor("custom")]; + tensor emb_pad_0 = const()[name = tensor("emb_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 emb_cast_fp16 = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_89, groups = var_68, pad = emb_pad_0, pad_type = emb_pad_type_0, strides = var_87, weight = time_embedding_linear_2_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = tensor("emb_cast_fp16")]; + tensor concat_0 = const()[name = tensor("concat_0"), val = tensor([6])]; + tensor timesteps_cast_fp16 = reshape(shape = concat_0, x = time_ids)[name = tensor("timesteps_cast_fp16")]; + tensor var_95 = const()[name = tensor("op_95"), val = tensor(-1)]; + tensor var_112_axes_0 = const()[name = tensor("op_112_axes_0"), val = tensor([1])]; + tensor var_112_cast_fp16 = expand_dims(axes = var_112_axes_0, x = timesteps_cast_fp16)[name = tensor("op_112_cast_fp16")]; + tensor var_114_to_fp16 = const()[name = tensor("op_114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1542208)))]; + tensor emb_11_cast_fp16 = mul(x = var_112_cast_fp16, y = var_114_to_fp16)[name = tensor("emb_11_cast_fp16")]; + tensor var_119_cast_fp16 = sin(x = emb_11_cast_fp16)[name = tensor("op_119_cast_fp16")]; + tensor var_120_cast_fp16 = cos(x = emb_11_cast_fp16)[name = tensor("op_120_cast_fp16")]; + tensor emb_15_interleave_0 = const()[name = tensor("emb_15_interleave_0"), val = tensor(false)]; + tensor emb_15_cast_fp16 = concat(axis = var_95, interleave = emb_15_interleave_0, values = (var_119_cast_fp16, var_120_cast_fp16))[name = tensor("emb_15_cast_fp16")]; + tensor var_124_begin_0 = const()[name = tensor("op_124_begin_0"), val = tensor([0, 128])]; + tensor var_124_end_0 = const()[name = tensor("op_124_end_0"), val = tensor([6, 256])]; + tensor var_124_end_mask_0 = const()[name = tensor("op_124_end_mask_0"), val = tensor([true, true])]; + tensor var_124_cast_fp16 = slice_by_index(begin = var_124_begin_0, end = var_124_end_0, end_mask = var_124_end_mask_0, x = emb_15_cast_fp16)[name = tensor("op_124_cast_fp16")]; + tensor var_126_begin_0 = const()[name = tensor("op_126_begin_0"), val = tensor([0, 0])]; + tensor var_126_end_0 = const()[name = tensor("op_126_end_0"), val = tensor([6, 128])]; + tensor var_126_end_mask_0 = const()[name = tensor("op_126_end_mask_0"), val = tensor([true, false])]; + tensor var_126_cast_fp16 = slice_by_index(begin = var_126_begin_0, end = var_126_end_0, end_mask = var_126_end_mask_0, x = emb_15_cast_fp16)[name = tensor("op_126_cast_fp16")]; + tensor time_embeds_1_interleave_0 = const()[name = tensor("time_embeds_1_interleave_0"), val = tensor(false)]; + tensor time_embeds_1_cast_fp16 = concat(axis = var_95, interleave = time_embeds_1_interleave_0, values = (var_124_cast_fp16, var_126_cast_fp16))[name = tensor("time_embeds_1_cast_fp16")]; + tensor var_134 = const()[name = tensor("op_134"), val = tensor([1, -1])]; + tensor time_embeds_cast_fp16 = reshape(shape = var_134, x = time_embeds_1_cast_fp16)[name = tensor("time_embeds_cast_fp16")]; + tensor var_137 = const()[name = tensor("op_137"), val = tensor(-1)]; + tensor sample_3_interleave_0 = const()[name = tensor("sample_3_interleave_0"), val = tensor(false)]; + tensor sample_3_cast_fp16 = concat(axis = var_137, interleave = sample_3_interleave_0, values = (text_embeds, time_embeds_cast_fp16))[name = tensor("sample_3_cast_fp16")]; + tensor var_139 = const()[name = tensor("op_139"), val = tensor(1)]; + tensor var_146_axes_0 = const()[name = tensor("op_146_axes_0"), val = tensor([-1])]; + tensor var_146_cast_fp16 = expand_dims(axes = var_146_axes_0, x = sample_3_cast_fp16)[name = tensor("op_146_cast_fp16")]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor input_7_cast_fp16 = expand_dims(axes = input_7_axes_0, x = var_146_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 1])]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 1])]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1542528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4245952))), name = tensor("add_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor([1280, 2816, 1, 1])]; + tensor add_embedding_linear_1_bias_to_fp16 = const()[name = tensor("add_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4246144)))]; + tensor input_9_cast_fp16 = conv(bias = add_embedding_linear_1_bias_to_fp16, dilations = var_152, groups = var_139, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_150, weight = add_embedding_linear_1_weight_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = silu(x = input_9_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor([1, 1])]; + tensor var_160 = const()[name = tensor("op_160"), val = tensor([1, 1])]; + tensor aug_emb_pad_type_0 = const()[name = tensor("aug_emb_pad_type_0"), val = tensor("custom")]; + tensor aug_emb_pad_0 = const()[name = tensor("aug_emb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4248768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5477632))), name = tensor("add_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor add_embedding_linear_2_bias_to_fp16 = const()[name = tensor("add_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5477824)))]; + tensor aug_emb_cast_fp16 = conv(bias = add_embedding_linear_2_bias_to_fp16, dilations = var_160, groups = var_139, pad = aug_emb_pad_0, pad_type = aug_emb_pad_type_0, strides = var_158, weight = add_embedding_linear_2_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("aug_emb_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = emb_cast_fp16, y = aug_emb_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor var_168 = const()[name = tensor("op_168"), val = tensor(1)]; + tensor var_171 = const()[name = tensor("op_171"), val = tensor([1, 1])]; + tensor var_173 = const()[name = tensor("op_173"), val = tensor([1, 1])]; + tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("custom")]; + tensor input_15_pad_0 = const()[name = tensor("input_15_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(5480448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5489152))), 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(5489344)))]; + tensor input_15_cast_fp16 = conv(bias = conv_in_bias_to_fp16, dilations = var_173, groups = var_168, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_171, weight = conv_in_weight_to_fp16_palettized, x = sample)[name = tensor("input_15_cast_fp16")]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_15_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + 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(5490048)))]; + 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(5490752)))]; + 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(5491456)))]; + 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(5492160)))]; + 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_fp16 = 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_fp16)[name = tensor("add_1_cast_fp16")]; + tensor input_19_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor var_200 = const()[name = tensor("op_200"), val = tensor([1, 1])]; + tensor var_202 = const()[name = tensor("op_202"), 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(5492864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6184128))), 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(6184320)))]; + tensor hidden_states_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_202, groups = var_182, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_200, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_19_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor input_23_cast_fp16 = silu(x = input_21_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor var_208 = const()[name = tensor("op_208"), val = tensor([1, 1])]; + tensor var_210 = const()[name = tensor("op_210"), 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(6185024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6492288))), 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(6492480)))]; + tensor temb_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_210, groups = var_182, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_208, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_1_cast_fp16")]; + tensor input_25_cast_fp16 = add(x = hidden_states_1_cast_fp16, y = temb_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_25_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + 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(6493184)))]; + 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(6493888)))]; + 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_fp16 = 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_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_29_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor var_220 = const()[name = tensor("op_220"), val = tensor([1, 1])]; + tensor var_222 = const()[name = tensor("op_222"), 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(6494592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7185856))), 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(7186048)))]; + tensor hidden_states_3_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_222, groups = var_182, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_220, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_29_cast_fp16)[name = tensor("hidden_states_3_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_15_cast_fp16, y = hidden_states_3_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = input_31_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + 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(7186752)))]; + 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(7187456)))]; + 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_fp16 = 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_fp16)[name = tensor("add_5_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = add_5_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor var_237 = const()[name = tensor("op_237"), val = tensor([1, 1])]; + tensor var_239 = const()[name = tensor("op_239"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_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(7188160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7879424))), 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(7879616)))]; + tensor hidden_states_5_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_239, groups = var_182, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_237, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor var_245 = const()[name = tensor("op_245"), val = tensor([1, 1])]; + tensor var_247 = const()[name = tensor("op_247"), 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(7880320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8187584))), 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(8187776)))]; + tensor temb_3_cast_fp16 = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_247, groups = var_182, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_245, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_3_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = temb_3_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_39_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + 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(8188480)))]; + 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(8189184)))]; + 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_fp16 = 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_fp16)[name = tensor("add_7_cast_fp16")]; + tensor input_43_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor var_257 = const()[name = tensor("op_257"), val = tensor([1, 1])]; + tensor var_259 = const()[name = tensor("op_259"), 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([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(8189888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8881152))), 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(8881344)))]; + tensor hidden_states_7_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_259, groups = var_182, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_257, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = input_31_cast_fp16, y = hidden_states_7_cast_fp16)[name = tensor("input_45_cast_fp16")]; + tensor var_266 = const()[name = tensor("op_266"), val = tensor([2, 2])]; + tensor var_268 = const()[name = tensor("op_268"), val = tensor([1, 1])]; + tensor input_47_pad_type_0 = const()[name = tensor("input_47_pad_type_0"), val = tensor("custom")]; + tensor input_47_pad_0 = const()[name = tensor("input_47_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(8882048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9573312))), 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(9573504)))]; + tensor input_47_cast_fp16 = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_268, groups = var_182, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = var_266, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor var_276 = const()[name = tensor("op_276"), val = tensor(3)]; + tensor var_292 = const()[name = tensor("op_292"), val = tensor(1)]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 10, 64, 64])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_47_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 320, 64, 64])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + 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(9574208)))]; + 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(9574912)))]; + 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_fp16 = 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_fp16)[name = tensor("add_9_cast_fp16")]; + tensor input_51_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_51_cast_fp16")]; + tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, 1])]; + tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 1])]; + tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_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(9575616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10958080))), 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(10958272)))]; + tensor hidden_states_9_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_317, groups = var_292, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_315, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = tensor("hidden_states_9_cast_fp16")]; + tensor var_323 = const()[name = tensor("op_323"), val = tensor([1, 1])]; + tensor var_325 = const()[name = tensor("op_325"), 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(10959616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11574080))), 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(11574272)))]; + tensor temb_5_cast_fp16 = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_325, groups = var_292, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_323, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_5_cast_fp16")]; + tensor input_55_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = temb_5_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = input_55_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11575616)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11576960)))]; + 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(11578304)))]; + 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(11579648)))]; + 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_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor input_59_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor var_335 = const()[name = tensor("op_335"), val = tensor([1, 1])]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_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(11580992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14345856))), 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(14346048)))]; + tensor hidden_states_11_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_337, groups = var_292, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_335, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_342 = const()[name = tensor("op_342"), val = tensor([1, 1])]; + tensor var_344 = const()[name = tensor("op_344"), 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(14347392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14501056))), 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(14501248)))]; + tensor x_1_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_344, groups = var_292, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_342, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor("x_1_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = add(x = x_1_cast_fp16, y = hidden_states_11_cast_fp16)[name = tensor("hidden_states_13_cast_fp16")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = hidden_states_13_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + 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(14502592)))]; + 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(14503936)))]; + 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_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor var_366 = const()[name = tensor("op_366"), val = tensor([1, 1])]; + tensor var_368 = const()[name = tensor("op_368"), val = tensor([1, 1])]; + tensor hidden_states_15_pad_type_0 = const()[name = tensor("hidden_states_15_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_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(14505280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14812544))), 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(14812736)))]; + tensor hidden_states_15_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_368, groups = var_292, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_366, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_13_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; + tensor var_373 = const()[name = tensor("op_373"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_1_cast_fp16 = reshape(shape = var_373, x = hidden_states_15_cast_fp16)[name = tensor("inputs_1_cast_fp16")]; + tensor hidden_states_17_axes_0 = const()[name = tensor("hidden_states_17_axes_0"), val = tensor([1])]; + tensor hidden_states_17_gamma_0_to_fp16 = const()[name = tensor("hidden_states_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14814080)))]; + tensor hidden_states_17_beta_0_to_fp16 = const()[name = tensor("hidden_states_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14815424)))]; + tensor var_389_to_fp16 = const()[name = tensor("op_389_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_17_cast_fp16 = layer_norm(axes = hidden_states_17_axes_0, beta = hidden_states_17_beta_0_to_fp16, epsilon = var_389_to_fp16, gamma = hidden_states_17_gamma_0_to_fp16, x = inputs_1_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; + tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 1])]; + tensor var_406 = const()[name = tensor("op_406"), 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_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(14816768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15124032))), 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_1_cast_fp16 = conv(dilations = var_406, groups = var_292, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_404, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_17_cast_fp16)[name = tensor("q_1_cast_fp16")]; + tensor var_410 = const()[name = tensor("op_410"), val = tensor([1, 1])]; + tensor var_412 = const()[name = tensor("op_412"), 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_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(15124224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15431488))), 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_1_cast_fp16 = conv(dilations = var_412, groups = var_292, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_410, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_17_cast_fp16)[name = tensor("k_1_cast_fp16")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 1])]; + tensor var_418 = const()[name = tensor("op_418"), 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_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(15431680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15738944))), 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_1_cast_fp16 = conv(dilations = var_418, groups = var_292, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_416, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_17_cast_fp16)[name = tensor("v_1_cast_fp16")]; + tensor var_422 = const()[name = tensor("op_422"), val = tensor([1, 10, 64, -1])]; + tensor var_423_cast_fp16 = reshape(shape = var_422, x = q_1_cast_fp16)[name = tensor("op_423_cast_fp16")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 10, 64, -1])]; + tensor var_425_cast_fp16 = reshape(shape = var_424, x = k_1_cast_fp16)[name = tensor("op_425_cast_fp16")]; + tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, 10, 64, -1])]; + tensor var_427_cast_fp16 = reshape(shape = var_426, x = v_1_cast_fp16)[name = tensor("op_427_cast_fp16")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_423_cast_fp16, y = var_425_cast_fp16)[name = tensor("attn_weights_1_cast_fp16")]; + tensor var_283_to_fp16 = const()[name = tensor("op_283_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_3_cast_fp16")]; + tensor var_431_cast_fp16 = softmax(axis = var_276, x = attn_weights_3_cast_fp16)[name = tensor("op_431_cast_fp16")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_427_cast_fp16, y = var_431_cast_fp16)[name = tensor("attn_1_cast_fp16")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 640, 1, -1])]; + tensor input_63_cast_fp16 = reshape(shape = var_435, x = attn_1_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor var_440 = const()[name = tensor("op_440"), val = tensor([1, 1])]; + tensor var_442 = const()[name = tensor("op_442"), val = tensor([1, 1])]; + tensor var_444_pad_type_0 = const()[name = tensor("op_444_pad_type_0"), val = tensor("custom")]; + tensor var_444_pad_0 = const()[name = tensor("op_444_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(15739136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16046400))), 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(16046592)))]; + tensor var_444_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_442, groups = var_292, pad = var_444_pad_0, pad_type = var_444_pad_type_0, strides = var_440, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("op_444_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = var_444_cast_fp16, y = inputs_1_cast_fp16)[name = tensor("inputs_3_cast_fp16")]; + tensor hidden_states_19_axes_0 = const()[name = tensor("hidden_states_19_axes_0"), val = tensor([1])]; + tensor hidden_states_19_gamma_0_to_fp16 = const()[name = tensor("hidden_states_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16047936)))]; + tensor hidden_states_19_beta_0_to_fp16 = const()[name = tensor("hidden_states_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16049280)))]; + tensor var_454_to_fp16 = const()[name = tensor("op_454_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_19_cast_fp16 = layer_norm(axes = hidden_states_19_axes_0, beta = hidden_states_19_beta_0_to_fp16, epsilon = var_454_to_fp16, gamma = hidden_states_19_gamma_0_to_fp16, x = inputs_3_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor var_469 = const()[name = tensor("op_469"), val = tensor([1, 1])]; + tensor var_471 = const()[name = tensor("op_471"), 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_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(16050624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16357888))), 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_3_cast_fp16 = conv(dilations = var_471, groups = var_292, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_469, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_19_cast_fp16)[name = tensor("q_3_cast_fp16")]; + tensor var_475 = const()[name = tensor("op_475"), val = tensor([1, 1])]; + tensor var_477 = const()[name = tensor("op_477"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_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(16358080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17341184))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_3_cast_fp16 = conv(dilations = var_477, groups = var_292, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_475, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_3_cast_fp16")]; + tensor var_481 = const()[name = tensor("op_481"), val = tensor([1, 1])]; + tensor var_483 = const()[name = tensor("op_483"), 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_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(17341376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18324480))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_3_cast_fp16 = conv(dilations = var_483, groups = var_292, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_481, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_3_cast_fp16")]; + tensor var_487 = const()[name = tensor("op_487"), val = tensor([1, 10, 64, -1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = q_3_cast_fp16)[name = tensor("op_488_cast_fp16")]; + tensor var_489 = const()[name = tensor("op_489"), val = tensor([1, 10, 64, -1])]; + tensor var_490_cast_fp16 = reshape(shape = var_489, x = k_3_cast_fp16)[name = tensor("op_490_cast_fp16")]; + tensor var_491 = const()[name = tensor("op_491"), val = tensor([1, 10, 64, -1])]; + tensor var_492_cast_fp16 = reshape(shape = var_491, x = v_3_cast_fp16)[name = tensor("op_492_cast_fp16")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast_fp16 = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_488_cast_fp16, y = var_490_cast_fp16)[name = tensor("attn_weights_5_cast_fp16")]; + tensor attn_weights_7_cast_fp16 = mul(x = attn_weights_5_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_7_cast_fp16")]; + tensor var_496_cast_fp16 = softmax(axis = var_276, x = attn_weights_7_cast_fp16)[name = tensor("op_496_cast_fp16")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_492_cast_fp16, y = var_496_cast_fp16)[name = tensor("attn_3_cast_fp16")]; + tensor var_500 = const()[name = tensor("op_500"), val = tensor([1, 640, 1, -1])]; + tensor input_67_cast_fp16 = reshape(shape = var_500, x = attn_3_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor var_505 = const()[name = tensor("op_505"), val = tensor([1, 1])]; + tensor var_507 = const()[name = tensor("op_507"), val = tensor([1, 1])]; + tensor var_509_pad_type_0 = const()[name = tensor("op_509_pad_type_0"), val = tensor("custom")]; + tensor var_509_pad_0 = const()[name = tensor("op_509_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(18324672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18631936))), 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(18632128)))]; + tensor var_509_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_507, groups = var_292, pad = var_509_pad_0, pad_type = var_509_pad_type_0, strides = var_505, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = tensor("op_509_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = var_509_cast_fp16, y = inputs_3_cast_fp16)[name = tensor("inputs_5_cast_fp16")]; + tensor input_69_axes_0 = const()[name = tensor("input_69_axes_0"), val = tensor([1])]; + tensor input_69_gamma_0_to_fp16 = const()[name = tensor("input_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18633472)))]; + tensor input_69_beta_0_to_fp16 = const()[name = tensor("input_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18634816)))]; + tensor var_519_to_fp16 = const()[name = tensor("op_519_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_69_cast_fp16 = layer_norm(axes = input_69_axes_0, beta = input_69_beta_0_to_fp16, epsilon = var_519_to_fp16, gamma = input_69_gamma_0_to_fp16, x = inputs_5_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor var_535 = const()[name = tensor("op_535"), val = tensor([1, 1])]; + tensor var_537 = const()[name = tensor("op_537"), val = tensor([1, 1])]; + tensor var_539_pad_type_0 = const()[name = tensor("op_539_pad_type_0"), val = tensor("custom")]; + tensor var_539_pad_0 = const()[name = tensor("op_539_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(18636160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21093824))), 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(21094016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21097920))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_539_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_537, groups = var_292, pad = var_539_pad_0, pad_type = var_539_pad_type_0, strides = var_535, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = tensor("op_539_cast_fp16")]; + tensor var_540_split_sizes_0 = const()[name = tensor("op_540_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_540_axis_0 = const()[name = tensor("op_540_axis_0"), val = tensor(1)]; + tensor var_540_cast_fp16_0, tensor var_540_cast_fp16_1 = split(axis = var_540_axis_0, split_sizes = var_540_split_sizes_0, x = var_539_cast_fp16)[name = tensor("op_540_cast_fp16")]; + tensor var_542_mode_0 = const()[name = tensor("op_542_mode_0"), val = tensor("EXACT")]; + tensor var_542_cast_fp16 = gelu(mode = var_542_mode_0, x = var_540_cast_fp16_1)[name = tensor("op_542_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = var_540_cast_fp16_0, y = var_542_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor var_546 = const()[name = tensor("op_546"), val = tensor([1, 1])]; + tensor var_548 = const()[name = tensor("op_548"), val = tensor([1, 1])]; + tensor var_550_pad_type_0 = const()[name = tensor("op_550_pad_type_0"), val = tensor("custom")]; + tensor var_550_pad_0 = const()[name = tensor("op_550_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(21098112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22326976))), 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(22327168)))]; + tensor var_550_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_548, groups = var_292, pad = var_550_pad_0, pad_type = var_550_pad_type_0, strides = var_546, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = tensor("op_550_cast_fp16")]; + tensor inputs_7_cast_fp16 = add(x = var_550_cast_fp16, y = inputs_5_cast_fp16)[name = tensor("inputs_7_cast_fp16")]; + tensor hidden_states_23_axes_0 = const()[name = tensor("hidden_states_23_axes_0"), val = tensor([1])]; + tensor hidden_states_23_gamma_0_to_fp16 = const()[name = tensor("hidden_states_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22328512)))]; + tensor hidden_states_23_beta_0_to_fp16 = const()[name = tensor("hidden_states_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22329856)))]; + tensor var_566_to_fp16 = const()[name = tensor("op_566_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_23_cast_fp16 = layer_norm(axes = hidden_states_23_axes_0, beta = hidden_states_23_beta_0_to_fp16, epsilon = var_566_to_fp16, gamma = hidden_states_23_gamma_0_to_fp16, x = inputs_7_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; + tensor var_581 = const()[name = tensor("op_581"), val = tensor([1, 1])]; + tensor var_583 = const()[name = tensor("op_583"), 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_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22331200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22638464))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_5_cast_fp16 = conv(dilations = var_583, groups = var_292, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_581, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_23_cast_fp16)[name = tensor("q_5_cast_fp16")]; + tensor var_587 = const()[name = tensor("op_587"), val = tensor([1, 1])]; + tensor var_589 = const()[name = tensor("op_589"), 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_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22638656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22945920))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_5_cast_fp16 = conv(dilations = var_589, groups = var_292, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_587, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_23_cast_fp16)[name = tensor("k_5_cast_fp16")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1])]; + tensor var_595 = const()[name = tensor("op_595"), 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_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22946112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23253376))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_5_cast_fp16 = conv(dilations = var_595, groups = var_292, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_593, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 10, 64, -1])]; + tensor var_600_cast_fp16 = reshape(shape = var_599, x = q_5_cast_fp16)[name = tensor("op_600_cast_fp16")]; + tensor var_601 = const()[name = tensor("op_601"), val = tensor([1, 10, 64, -1])]; + tensor var_602_cast_fp16 = reshape(shape = var_601, x = k_5_cast_fp16)[name = tensor("op_602_cast_fp16")]; + tensor var_603 = const()[name = tensor("op_603"), val = tensor([1, 10, 64, -1])]; + tensor var_604_cast_fp16 = reshape(shape = var_603, x = v_5_cast_fp16)[name = tensor("op_604_cast_fp16")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_600_cast_fp16, y = var_602_cast_fp16)[name = tensor("attn_weights_9_cast_fp16")]; + tensor attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_11_cast_fp16")]; + tensor var_608_cast_fp16 = softmax(axis = var_276, x = attn_weights_11_cast_fp16)[name = tensor("op_608_cast_fp16")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_604_cast_fp16, y = var_608_cast_fp16)[name = tensor("attn_5_cast_fp16")]; + tensor var_612 = const()[name = tensor("op_612"), val = tensor([1, 640, 1, -1])]; + tensor input_73_cast_fp16 = reshape(shape = var_612, x = attn_5_cast_fp16)[name = tensor("input_73_cast_fp16")]; + tensor var_617 = const()[name = tensor("op_617"), val = tensor([1, 1])]; + tensor var_619 = const()[name = tensor("op_619"), val = tensor([1, 1])]; + tensor var_621_pad_type_0 = const()[name = tensor("op_621_pad_type_0"), val = tensor("custom")]; + tensor var_621_pad_0 = const()[name = tensor("op_621_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23253568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23560832))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23561024)))]; + tensor var_621_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_619, groups = var_292, pad = var_621_pad_0, pad_type = var_621_pad_type_0, strides = var_617, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_73_cast_fp16)[name = tensor("op_621_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = var_621_cast_fp16, y = inputs_7_cast_fp16)[name = tensor("inputs_9_cast_fp16")]; + tensor hidden_states_25_axes_0 = const()[name = tensor("hidden_states_25_axes_0"), val = tensor([1])]; + tensor hidden_states_25_gamma_0_to_fp16 = const()[name = tensor("hidden_states_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23562368)))]; + tensor hidden_states_25_beta_0_to_fp16 = const()[name = tensor("hidden_states_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23563712)))]; + tensor var_631_to_fp16 = const()[name = tensor("op_631_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_25_cast_fp16 = layer_norm(axes = hidden_states_25_axes_0, beta = hidden_states_25_beta_0_to_fp16, epsilon = var_631_to_fp16, gamma = hidden_states_25_gamma_0_to_fp16, x = inputs_9_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor var_646 = const()[name = tensor("op_646"), val = tensor([1, 1])]; + tensor var_648 = const()[name = tensor("op_648"), 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_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23565056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23872320))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_7_cast_fp16 = conv(dilations = var_648, groups = var_292, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_646, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_25_cast_fp16)[name = tensor("q_7_cast_fp16")]; + tensor var_652 = const()[name = tensor("op_652"), val = tensor([1, 1])]; + tensor var_654 = const()[name = tensor("op_654"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23872512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24855616))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_7_cast_fp16 = conv(dilations = var_654, groups = var_292, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_652, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_7_cast_fp16")]; + tensor var_658 = const()[name = tensor("op_658"), val = tensor([1, 1])]; + tensor var_660 = const()[name = tensor("op_660"), 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_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24855808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25838912))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_7_cast_fp16 = conv(dilations = var_660, groups = var_292, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_658, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_7_cast_fp16")]; + tensor var_664 = const()[name = tensor("op_664"), val = tensor([1, 10, 64, -1])]; + tensor var_665_cast_fp16 = reshape(shape = var_664, x = q_7_cast_fp16)[name = tensor("op_665_cast_fp16")]; + tensor var_666 = const()[name = tensor("op_666"), val = tensor([1, 10, 64, -1])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = k_7_cast_fp16)[name = tensor("op_667_cast_fp16")]; + tensor var_668 = const()[name = tensor("op_668"), val = tensor([1, 10, 64, -1])]; + tensor var_669_cast_fp16 = reshape(shape = var_668, x = v_7_cast_fp16)[name = tensor("op_669_cast_fp16")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast_fp16 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_665_cast_fp16, y = var_667_cast_fp16)[name = tensor("attn_weights_13_cast_fp16")]; + tensor attn_weights_15_cast_fp16 = mul(x = attn_weights_13_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_15_cast_fp16")]; + tensor var_673_cast_fp16 = softmax(axis = var_276, x = attn_weights_15_cast_fp16)[name = tensor("op_673_cast_fp16")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_669_cast_fp16, y = var_673_cast_fp16)[name = tensor("attn_7_cast_fp16")]; + tensor var_677 = const()[name = tensor("op_677"), val = tensor([1, 640, 1, -1])]; + tensor input_75_cast_fp16 = reshape(shape = var_677, x = attn_7_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor var_682 = const()[name = tensor("op_682"), val = tensor([1, 1])]; + tensor var_684 = const()[name = tensor("op_684"), val = tensor([1, 1])]; + tensor var_686_pad_type_0 = const()[name = tensor("op_686_pad_type_0"), val = tensor("custom")]; + tensor var_686_pad_0 = const()[name = tensor("op_686_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25839104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26146368))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26146560)))]; + tensor var_686_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_684, groups = var_292, pad = var_686_pad_0, pad_type = var_686_pad_type_0, strides = var_682, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor("op_686_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = var_686_cast_fp16, y = inputs_9_cast_fp16)[name = tensor("inputs_11_cast_fp16")]; + tensor input_77_axes_0 = const()[name = tensor("input_77_axes_0"), val = tensor([1])]; + tensor input_77_gamma_0_to_fp16 = const()[name = tensor("input_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26147904)))]; + tensor input_77_beta_0_to_fp16 = const()[name = tensor("input_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26149248)))]; + tensor var_696_to_fp16 = const()[name = tensor("op_696_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = input_77_beta_0_to_fp16, epsilon = var_696_to_fp16, gamma = input_77_gamma_0_to_fp16, x = inputs_11_cast_fp16)[name = tensor("input_77_cast_fp16")]; + tensor var_712 = const()[name = tensor("op_712"), val = tensor([1, 1])]; + tensor var_714 = const()[name = tensor("op_714"), val = tensor([1, 1])]; + tensor var_716_pad_type_0 = const()[name = tensor("op_716_pad_type_0"), val = tensor("custom")]; + tensor var_716_pad_0 = const()[name = tensor("op_716_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26150592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28608256))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28608448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28612352))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_716_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_714, groups = var_292, pad = var_716_pad_0, pad_type = var_716_pad_type_0, strides = var_712, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor("op_716_cast_fp16")]; + tensor var_717_split_sizes_0 = const()[name = tensor("op_717_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_717_axis_0 = const()[name = tensor("op_717_axis_0"), val = tensor(1)]; + tensor var_717_cast_fp16_0, tensor var_717_cast_fp16_1 = split(axis = var_717_axis_0, split_sizes = var_717_split_sizes_0, x = var_716_cast_fp16)[name = tensor("op_717_cast_fp16")]; + tensor var_719_mode_0 = const()[name = tensor("op_719_mode_0"), val = tensor("EXACT")]; + tensor var_719_cast_fp16 = gelu(mode = var_719_mode_0, x = var_717_cast_fp16_1)[name = tensor("op_719_cast_fp16")]; + tensor input_79_cast_fp16 = mul(x = var_717_cast_fp16_0, y = var_719_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor var_725 = const()[name = tensor("op_725"), val = tensor([1, 1])]; + tensor var_727_pad_type_0 = const()[name = tensor("op_727_pad_type_0"), val = tensor("custom")]; + tensor var_727_pad_0 = const()[name = tensor("op_727_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28612544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29841408))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29841600)))]; + tensor var_727_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_725, groups = var_292, pad = var_727_pad_0, pad_type = var_727_pad_type_0, strides = var_723, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_79_cast_fp16)[name = tensor("op_727_cast_fp16")]; + tensor hidden_states_29_cast_fp16 = add(x = var_727_cast_fp16, y = inputs_11_cast_fp16)[name = tensor("hidden_states_29_cast_fp16")]; + tensor var_729 = const()[name = tensor("op_729"), val = tensor([1, 640, 64, 64])]; + tensor input_81_cast_fp16 = reshape(shape = var_729, x = hidden_states_29_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor var_733 = const()[name = tensor("op_733"), val = tensor([1, 1])]; + tensor var_735 = const()[name = tensor("op_735"), val = tensor([1, 1])]; + tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_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(29842944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30150208))), 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(30150400)))]; + tensor hidden_states_31_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_735, groups = var_292, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_733, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_81_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = hidden_states_31_cast_fp16, y = hidden_states_13_cast_fp16)[name = tensor("input_83_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_83_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + 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(30151744)))]; + 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(30153088)))]; + 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_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor input_87_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("input_87_cast_fp16")]; + 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 hidden_states_33_pad_type_0 = const()[name = tensor("hidden_states_33_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_33_pad_0 = const()[name = tensor("hidden_states_33_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(30154432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32919296))), 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(32919488)))]; + tensor hidden_states_33_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_752, groups = var_292, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_750, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_758 = const()[name = tensor("op_758"), val = tensor([1, 1])]; + tensor var_760 = const()[name = tensor("op_760"), 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(32920832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33535296))), 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(33535488)))]; + tensor temb_7_cast_fp16 = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_760, groups = var_292, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_758, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_7_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = hidden_states_33_cast_fp16, y = temb_7_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_91_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + 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(33536832)))]; + 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(33538176)))]; + 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_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor input_95_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor("input_95_cast_fp16")]; + tensor var_770 = const()[name = tensor("op_770"), val = tensor([1, 1])]; + tensor var_772 = const()[name = tensor("op_772"), 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([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(33539520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36304384))), 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(36304576)))]; + tensor hidden_states_35_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_772, groups = var_292, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_770, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = tensor("hidden_states_35_cast_fp16")]; + tensor hidden_states_37_cast_fp16 = add(x = input_83_cast_fp16, y = hidden_states_35_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = hidden_states_37_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + 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(36305920)))]; + 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(36307264)))]; + 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_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor var_794 = const()[name = tensor("op_794"), val = tensor([1, 1])]; + tensor var_796 = const()[name = tensor("op_796"), 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([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(36308608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36615872))), 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(36616064)))]; + tensor hidden_states_39_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_796, groups = var_292, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_794, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_19_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_801 = const()[name = tensor("op_801"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_13_cast_fp16 = reshape(shape = var_801, x = hidden_states_39_cast_fp16)[name = tensor("inputs_13_cast_fp16")]; + tensor hidden_states_41_axes_0 = const()[name = tensor("hidden_states_41_axes_0"), val = tensor([1])]; + tensor hidden_states_41_gamma_0_to_fp16 = const()[name = tensor("hidden_states_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36617408)))]; + tensor hidden_states_41_beta_0_to_fp16 = const()[name = tensor("hidden_states_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36618752)))]; + tensor var_817_to_fp16 = const()[name = tensor("op_817_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_41_cast_fp16 = layer_norm(axes = hidden_states_41_axes_0, beta = hidden_states_41_beta_0_to_fp16, epsilon = var_817_to_fp16, gamma = hidden_states_41_gamma_0_to_fp16, x = inputs_13_cast_fp16)[name = tensor("hidden_states_41_cast_fp16")]; + tensor var_832 = const()[name = tensor("op_832"), val = tensor([1, 1])]; + tensor var_834 = const()[name = tensor("op_834"), 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_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(36620096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36927360))), 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_9_cast_fp16 = conv(dilations = var_834, groups = var_292, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_832, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_41_cast_fp16)[name = tensor("q_9_cast_fp16")]; + tensor var_838 = const()[name = tensor("op_838"), val = tensor([1, 1])]; + tensor var_840 = const()[name = tensor("op_840"), 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_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(36927552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37234816))), 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_9_cast_fp16 = conv(dilations = var_840, groups = var_292, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_838, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_41_cast_fp16)[name = tensor("k_9_cast_fp16")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor([1, 1])]; + tensor var_846 = const()[name = tensor("op_846"), 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_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(37235008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37542272))), 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_9_cast_fp16 = conv(dilations = var_846, groups = var_292, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_844, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; + tensor var_850 = const()[name = tensor("op_850"), val = tensor([1, 10, 64, -1])]; + tensor var_851_cast_fp16 = reshape(shape = var_850, x = q_9_cast_fp16)[name = tensor("op_851_cast_fp16")]; + tensor var_852 = const()[name = tensor("op_852"), val = tensor([1, 10, 64, -1])]; + tensor var_853_cast_fp16 = reshape(shape = var_852, x = k_9_cast_fp16)[name = tensor("op_853_cast_fp16")]; + tensor var_854 = const()[name = tensor("op_854"), val = tensor([1, 10, 64, -1])]; + tensor var_855_cast_fp16 = reshape(shape = var_854, x = v_9_cast_fp16)[name = tensor("op_855_cast_fp16")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_851_cast_fp16, y = var_853_cast_fp16)[name = tensor("attn_weights_17_cast_fp16")]; + tensor attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_19_cast_fp16")]; + tensor var_859_cast_fp16 = softmax(axis = var_276, x = attn_weights_19_cast_fp16)[name = tensor("op_859_cast_fp16")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_855_cast_fp16, y = var_859_cast_fp16)[name = tensor("attn_9_cast_fp16")]; + tensor var_863 = const()[name = tensor("op_863"), val = tensor([1, 640, 1, -1])]; + tensor input_99_cast_fp16 = reshape(shape = var_863, x = attn_9_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor([1, 1])]; + tensor var_870 = const()[name = tensor("op_870"), val = tensor([1, 1])]; + tensor var_872_pad_type_0 = const()[name = tensor("op_872_pad_type_0"), val = tensor("custom")]; + tensor var_872_pad_0 = const()[name = tensor("op_872_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(37542464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37849728))), 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(37849920)))]; + tensor var_872_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_870, groups = var_292, pad = var_872_pad_0, pad_type = var_872_pad_type_0, strides = var_868, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor("op_872_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = var_872_cast_fp16, y = inputs_13_cast_fp16)[name = tensor("inputs_15_cast_fp16")]; + tensor hidden_states_43_axes_0 = const()[name = tensor("hidden_states_43_axes_0"), val = tensor([1])]; + tensor hidden_states_43_gamma_0_to_fp16 = const()[name = tensor("hidden_states_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37851264)))]; + tensor hidden_states_43_beta_0_to_fp16 = const()[name = tensor("hidden_states_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37852608)))]; + tensor var_882_to_fp16 = const()[name = tensor("op_882_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_43_cast_fp16 = layer_norm(axes = hidden_states_43_axes_0, beta = hidden_states_43_beta_0_to_fp16, epsilon = var_882_to_fp16, gamma = hidden_states_43_gamma_0_to_fp16, x = inputs_15_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_897 = const()[name = tensor("op_897"), val = tensor([1, 1])]; + tensor var_899 = const()[name = tensor("op_899"), 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_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(37853952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38161216))), 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_11_cast_fp16 = conv(dilations = var_899, groups = var_292, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_897, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_43_cast_fp16)[name = tensor("q_11_cast_fp16")]; + tensor var_903 = const()[name = tensor("op_903"), val = tensor([1, 1])]; + tensor var_905 = const()[name = tensor("op_905"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_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(38161408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39144512))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_11_cast_fp16 = conv(dilations = var_905, groups = var_292, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_903, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_11_cast_fp16")]; + tensor var_909 = const()[name = tensor("op_909"), val = tensor([1, 1])]; + tensor var_911 = const()[name = tensor("op_911"), 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_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(39144704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40127808))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_11_cast_fp16 = conv(dilations = var_911, groups = var_292, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_909, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_11_cast_fp16")]; + tensor var_915 = const()[name = tensor("op_915"), val = tensor([1, 10, 64, -1])]; + tensor var_916_cast_fp16 = reshape(shape = var_915, x = q_11_cast_fp16)[name = tensor("op_916_cast_fp16")]; + tensor var_917 = const()[name = tensor("op_917"), val = tensor([1, 10, 64, -1])]; + tensor var_918_cast_fp16 = reshape(shape = var_917, x = k_11_cast_fp16)[name = tensor("op_918_cast_fp16")]; + tensor var_919 = const()[name = tensor("op_919"), val = tensor([1, 10, 64, -1])]; + tensor var_920_cast_fp16 = reshape(shape = var_919, x = v_11_cast_fp16)[name = tensor("op_920_cast_fp16")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast_fp16 = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_916_cast_fp16, y = var_918_cast_fp16)[name = tensor("attn_weights_21_cast_fp16")]; + tensor attn_weights_23_cast_fp16 = mul(x = attn_weights_21_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_23_cast_fp16")]; + tensor var_924_cast_fp16 = softmax(axis = var_276, x = attn_weights_23_cast_fp16)[name = tensor("op_924_cast_fp16")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_920_cast_fp16, y = var_924_cast_fp16)[name = tensor("attn_11_cast_fp16")]; + tensor var_928 = const()[name = tensor("op_928"), val = tensor([1, 640, 1, -1])]; + tensor input_101_cast_fp16 = reshape(shape = var_928, x = attn_11_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor var_933 = const()[name = tensor("op_933"), val = tensor([1, 1])]; + tensor var_935 = const()[name = tensor("op_935"), val = tensor([1, 1])]; + tensor var_937_pad_type_0 = const()[name = tensor("op_937_pad_type_0"), val = tensor("custom")]; + tensor var_937_pad_0 = const()[name = tensor("op_937_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(40128000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40435264))), 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(40435456)))]; + tensor var_937_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_935, groups = var_292, pad = var_937_pad_0, pad_type = var_937_pad_type_0, strides = var_933, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = tensor("op_937_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = var_937_cast_fp16, y = inputs_15_cast_fp16)[name = tensor("inputs_17_cast_fp16")]; + tensor input_103_axes_0 = const()[name = tensor("input_103_axes_0"), val = tensor([1])]; + tensor input_103_gamma_0_to_fp16 = const()[name = tensor("input_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40436800)))]; + tensor input_103_beta_0_to_fp16 = const()[name = tensor("input_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40438144)))]; + tensor var_947_to_fp16 = const()[name = tensor("op_947_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_103_cast_fp16 = layer_norm(axes = input_103_axes_0, beta = input_103_beta_0_to_fp16, epsilon = var_947_to_fp16, gamma = input_103_gamma_0_to_fp16, x = inputs_17_cast_fp16)[name = tensor("input_103_cast_fp16")]; + tensor var_963 = const()[name = tensor("op_963"), val = tensor([1, 1])]; + tensor var_965 = const()[name = tensor("op_965"), val = tensor([1, 1])]; + tensor var_967_pad_type_0 = const()[name = tensor("op_967_pad_type_0"), val = tensor("custom")]; + tensor var_967_pad_0 = const()[name = tensor("op_967_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(40439488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42897152))), 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(42897344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42901248))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_967_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_965, groups = var_292, pad = var_967_pad_0, pad_type = var_967_pad_type_0, strides = var_963, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_103_cast_fp16)[name = tensor("op_967_cast_fp16")]; + tensor var_968_split_sizes_0 = const()[name = tensor("op_968_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_968_axis_0 = const()[name = tensor("op_968_axis_0"), val = tensor(1)]; + tensor var_968_cast_fp16_0, tensor var_968_cast_fp16_1 = split(axis = var_968_axis_0, split_sizes = var_968_split_sizes_0, x = var_967_cast_fp16)[name = tensor("op_968_cast_fp16")]; + tensor var_970_mode_0 = const()[name = tensor("op_970_mode_0"), val = tensor("EXACT")]; + tensor var_970_cast_fp16 = gelu(mode = var_970_mode_0, x = var_968_cast_fp16_1)[name = tensor("op_970_cast_fp16")]; + tensor input_105_cast_fp16 = mul(x = var_968_cast_fp16_0, y = var_970_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor var_974 = const()[name = tensor("op_974"), val = tensor([1, 1])]; + tensor var_976 = const()[name = tensor("op_976"), val = tensor([1, 1])]; + tensor var_978_pad_type_0 = const()[name = tensor("op_978_pad_type_0"), val = tensor("custom")]; + tensor var_978_pad_0 = const()[name = tensor("op_978_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(42901440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44130304))), 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(44130496)))]; + tensor var_978_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_976, groups = var_292, pad = var_978_pad_0, pad_type = var_978_pad_type_0, strides = var_974, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = tensor("op_978_cast_fp16")]; + tensor inputs_19_cast_fp16 = add(x = var_978_cast_fp16, y = inputs_17_cast_fp16)[name = tensor("inputs_19_cast_fp16")]; + tensor hidden_states_47_axes_0 = const()[name = tensor("hidden_states_47_axes_0"), val = tensor([1])]; + tensor hidden_states_47_gamma_0_to_fp16 = const()[name = tensor("hidden_states_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44131840)))]; + tensor hidden_states_47_beta_0_to_fp16 = const()[name = tensor("hidden_states_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44133184)))]; + tensor var_994_to_fp16 = const()[name = tensor("op_994_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_47_cast_fp16 = layer_norm(axes = hidden_states_47_axes_0, beta = hidden_states_47_beta_0_to_fp16, epsilon = var_994_to_fp16, gamma = hidden_states_47_gamma_0_to_fp16, x = inputs_19_cast_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_1009 = const()[name = tensor("op_1009"), val = tensor([1, 1])]; + tensor var_1011 = const()[name = tensor("op_1011"), 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_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44134528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44441792))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_13_cast_fp16 = conv(dilations = var_1011, groups = var_292, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1009, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_47_cast_fp16)[name = tensor("q_13_cast_fp16")]; + tensor var_1015 = const()[name = tensor("op_1015"), val = tensor([1, 1])]; + tensor var_1017 = const()[name = tensor("op_1017"), 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_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44441984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44749248))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_13_cast_fp16 = conv(dilations = var_1017, groups = var_292, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1015, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_47_cast_fp16)[name = tensor("k_13_cast_fp16")]; + tensor var_1021 = const()[name = tensor("op_1021"), val = tensor([1, 1])]; + tensor var_1023 = const()[name = tensor("op_1023"), 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_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44749440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45056704))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_13_cast_fp16 = conv(dilations = var_1023, groups = var_292, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1021, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_47_cast_fp16)[name = tensor("v_13_cast_fp16")]; + tensor var_1027 = const()[name = tensor("op_1027"), val = tensor([1, 10, 64, -1])]; + tensor var_1028_cast_fp16 = reshape(shape = var_1027, x = q_13_cast_fp16)[name = tensor("op_1028_cast_fp16")]; + tensor var_1029 = const()[name = tensor("op_1029"), val = tensor([1, 10, 64, -1])]; + tensor var_1030_cast_fp16 = reshape(shape = var_1029, x = k_13_cast_fp16)[name = tensor("op_1030_cast_fp16")]; + tensor var_1031 = const()[name = tensor("op_1031"), val = tensor([1, 10, 64, -1])]; + tensor var_1032_cast_fp16 = reshape(shape = var_1031, x = v_13_cast_fp16)[name = tensor("op_1032_cast_fp16")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1028_cast_fp16, y = var_1030_cast_fp16)[name = tensor("attn_weights_25_cast_fp16")]; + tensor attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_27_cast_fp16")]; + tensor var_1036_cast_fp16 = softmax(axis = var_276, x = attn_weights_27_cast_fp16)[name = tensor("op_1036_cast_fp16")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1032_cast_fp16, y = var_1036_cast_fp16)[name = tensor("attn_13_cast_fp16")]; + tensor var_1040 = const()[name = tensor("op_1040"), val = tensor([1, 640, 1, -1])]; + tensor input_107_cast_fp16 = reshape(shape = var_1040, x = attn_13_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor var_1045 = const()[name = tensor("op_1045"), val = tensor([1, 1])]; + tensor var_1047 = const()[name = tensor("op_1047"), val = tensor([1, 1])]; + tensor var_1049_pad_type_0 = const()[name = tensor("op_1049_pad_type_0"), val = tensor("custom")]; + tensor var_1049_pad_0 = const()[name = tensor("op_1049_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45056896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45364160))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45364352)))]; + tensor var_1049_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1047, groups = var_292, pad = var_1049_pad_0, pad_type = var_1049_pad_type_0, strides = var_1045, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor("op_1049_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = var_1049_cast_fp16, y = inputs_19_cast_fp16)[name = tensor("inputs_21_cast_fp16")]; + tensor hidden_states_49_axes_0 = const()[name = tensor("hidden_states_49_axes_0"), val = tensor([1])]; + tensor hidden_states_49_gamma_0_to_fp16 = const()[name = tensor("hidden_states_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45365696)))]; + tensor hidden_states_49_beta_0_to_fp16 = const()[name = tensor("hidden_states_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45367040)))]; + tensor var_1059_to_fp16 = const()[name = tensor("op_1059_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_49_cast_fp16 = layer_norm(axes = hidden_states_49_axes_0, beta = hidden_states_49_beta_0_to_fp16, epsilon = var_1059_to_fp16, gamma = hidden_states_49_gamma_0_to_fp16, x = inputs_21_cast_fp16)[name = tensor("hidden_states_49_cast_fp16")]; + tensor var_1074 = const()[name = tensor("op_1074"), val = tensor([1, 1])]; + tensor var_1076 = const()[name = tensor("op_1076"), 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_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45368384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45675648))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_15_cast_fp16 = conv(dilations = var_1076, groups = var_292, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1074, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_49_cast_fp16)[name = tensor("q_15_cast_fp16")]; + tensor var_1080 = const()[name = tensor("op_1080"), val = tensor([1, 1])]; + tensor var_1082 = const()[name = tensor("op_1082"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45675840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46658944))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_15_cast_fp16 = conv(dilations = var_1082, groups = var_292, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1080, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_15_cast_fp16")]; + tensor var_1086 = const()[name = tensor("op_1086"), val = tensor([1, 1])]; + tensor var_1088 = const()[name = tensor("op_1088"), 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_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46659136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47642240))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_15_cast_fp16 = conv(dilations = var_1088, groups = var_292, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1086, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_15_cast_fp16")]; + tensor var_1092 = const()[name = tensor("op_1092"), val = tensor([1, 10, 64, -1])]; + tensor var_1093_cast_fp16 = reshape(shape = var_1092, x = q_15_cast_fp16)[name = tensor("op_1093_cast_fp16")]; + tensor var_1094 = const()[name = tensor("op_1094"), val = tensor([1, 10, 64, -1])]; + tensor var_1095_cast_fp16 = reshape(shape = var_1094, x = k_15_cast_fp16)[name = tensor("op_1095_cast_fp16")]; + tensor var_1096 = const()[name = tensor("op_1096"), val = tensor([1, 10, 64, -1])]; + tensor var_1097_cast_fp16 = reshape(shape = var_1096, x = v_15_cast_fp16)[name = tensor("op_1097_cast_fp16")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast_fp16 = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1093_cast_fp16, y = var_1095_cast_fp16)[name = tensor("attn_weights_29_cast_fp16")]; + tensor attn_weights_31_cast_fp16 = mul(x = attn_weights_29_cast_fp16, y = var_283_to_fp16)[name = tensor("attn_weights_31_cast_fp16")]; + tensor var_1101_cast_fp16 = softmax(axis = var_276, x = attn_weights_31_cast_fp16)[name = tensor("op_1101_cast_fp16")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1097_cast_fp16, y = var_1101_cast_fp16)[name = tensor("attn_15_cast_fp16")]; + tensor var_1105 = const()[name = tensor("op_1105"), val = tensor([1, 640, 1, -1])]; + tensor input_109_cast_fp16 = reshape(shape = var_1105, x = attn_15_cast_fp16)[name = tensor("input_109_cast_fp16")]; + tensor var_1110 = const()[name = tensor("op_1110"), val = tensor([1, 1])]; + tensor var_1112 = const()[name = tensor("op_1112"), val = tensor([1, 1])]; + tensor var_1114_pad_type_0 = const()[name = tensor("op_1114_pad_type_0"), val = tensor("custom")]; + tensor var_1114_pad_0 = const()[name = tensor("op_1114_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47642432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47949696))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47949888)))]; + tensor var_1114_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1112, groups = var_292, pad = var_1114_pad_0, pad_type = var_1114_pad_type_0, strides = var_1110, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_109_cast_fp16)[name = tensor("op_1114_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = var_1114_cast_fp16, y = inputs_21_cast_fp16)[name = tensor("inputs_23_cast_fp16")]; + tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([1])]; + tensor input_111_gamma_0_to_fp16 = const()[name = tensor("input_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47951232)))]; + tensor input_111_beta_0_to_fp16 = const()[name = tensor("input_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47952576)))]; + tensor var_1124_to_fp16 = const()[name = tensor("op_1124_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = input_111_beta_0_to_fp16, epsilon = var_1124_to_fp16, gamma = input_111_gamma_0_to_fp16, x = inputs_23_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor var_1140 = const()[name = tensor("op_1140"), val = tensor([1, 1])]; + tensor var_1142 = const()[name = tensor("op_1142"), val = tensor([1, 1])]; + tensor var_1144_pad_type_0 = const()[name = tensor("op_1144_pad_type_0"), val = tensor("custom")]; + tensor var_1144_pad_0 = const()[name = tensor("op_1144_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47953920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50411584))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50411776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50415680))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_1144_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1142, groups = var_292, pad = var_1144_pad_0, pad_type = var_1144_pad_type_0, strides = var_1140, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor("op_1144_cast_fp16")]; + tensor var_1145_split_sizes_0 = const()[name = tensor("op_1145_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1145_axis_0 = const()[name = tensor("op_1145_axis_0"), val = tensor(1)]; + tensor var_1145_cast_fp16_0, tensor var_1145_cast_fp16_1 = split(axis = var_1145_axis_0, split_sizes = var_1145_split_sizes_0, x = var_1144_cast_fp16)[name = tensor("op_1145_cast_fp16")]; + tensor var_1147_mode_0 = const()[name = tensor("op_1147_mode_0"), val = tensor("EXACT")]; + tensor var_1147_cast_fp16 = gelu(mode = var_1147_mode_0, x = var_1145_cast_fp16_1)[name = tensor("op_1147_cast_fp16")]; + tensor input_113_cast_fp16 = mul(x = var_1145_cast_fp16_0, y = var_1147_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor var_1151 = const()[name = tensor("op_1151"), val = tensor([1, 1])]; + tensor var_1153 = const()[name = tensor("op_1153"), val = tensor([1, 1])]; + tensor var_1155_pad_type_0 = const()[name = tensor("op_1155_pad_type_0"), val = tensor("custom")]; + tensor var_1155_pad_0 = const()[name = tensor("op_1155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50415872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51644736))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51644928)))]; + tensor var_1155_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1153, groups = var_292, pad = var_1155_pad_0, pad_type = var_1155_pad_type_0, strides = var_1151, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = tensor("op_1155_cast_fp16")]; + tensor hidden_states_53_cast_fp16 = add(x = var_1155_cast_fp16, y = inputs_23_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor var_1157 = const()[name = tensor("op_1157"), val = tensor([1, 640, 64, 64])]; + tensor input_115_cast_fp16 = reshape(shape = var_1157, x = hidden_states_53_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor var_1161 = const()[name = tensor("op_1161"), val = tensor([1, 1])]; + tensor var_1163 = const()[name = tensor("op_1163"), 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([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(51646272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51953536))), 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(51953728)))]; + tensor hidden_states_55_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1163, groups = var_292, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_1161, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_115_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor var_1170 = const()[name = tensor("op_1170"), val = tensor([2, 2])]; + tensor var_1172 = const()[name = tensor("op_1172"), val = tensor([1, 1])]; + tensor input_119_pad_type_0 = const()[name = tensor("input_119_pad_type_0"), val = tensor("custom")]; + tensor input_119_pad_0 = const()[name = tensor("input_119_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(51955072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54719936))), 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(54720128)))]; + tensor input_119_cast_fp16 = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1172, groups = var_292, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = var_1170, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized, x = input_117_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor var_1180 = const()[name = tensor("op_1180"), val = tensor(3)]; + tensor var_1196 = const()[name = tensor("op_1196"), val = tensor(1)]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 20, 32, 32])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_119_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 640, 32, 32])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + 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(54721472)))]; + 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(54722816)))]; + 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_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor input_123_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_123_cast_fp16")]; + tensor var_1217 = const()[name = tensor("op_1217"), val = tensor([1, 1])]; + tensor var_1219 = const()[name = tensor("op_1219"), 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_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54724160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60253824))), 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(60254016)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1219, groups = var_1196, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_1217, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = tensor("hidden_states_57_cast_fp16")]; + tensor var_1225 = const()[name = tensor("op_1225"), val = tensor([1, 1])]; + tensor var_1227 = const()[name = tensor("op_1227"), 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(60256640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61485504))), 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(61485696)))]; + tensor temb_9_cast_fp16 = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1227, groups = var_1196, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1225, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_9_cast_fp16")]; + tensor input_127_cast_fp16 = add(x = hidden_states_57_cast_fp16, y = temb_9_cast_fp16)[name = tensor("input_127_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_127_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_mean_0_to_fp16 = const()[name = tensor("add_23_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61488320)))]; + tensor add_23_variance_0_to_fp16 = const()[name = tensor("add_23_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61490944)))]; + 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(61493568)))]; + 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(61496192)))]; + 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_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor input_131_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor("input_131_cast_fp16")]; + tensor var_1237 = const()[name = tensor("op_1237"), val = tensor([1, 1])]; + tensor var_1239 = const()[name = tensor("op_1239"), val = tensor([1, 1])]; + tensor hidden_states_59_pad_type_0 = const()[name = tensor("hidden_states_59_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_59_pad_0 = const()[name = tensor("hidden_states_59_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(61498816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72558080))), 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(72558272)))]; + tensor hidden_states_59_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1239, groups = var_1196, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_1237, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + 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 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(72560896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73175360))), 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(73175552)))]; + tensor x_3_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1246, groups = var_1196, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1244, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = tensor("x_3_cast_fp16")]; + tensor hidden_states_61_cast_fp16 = add(x = x_3_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = hidden_states_61_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + 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(73178176)))]; + 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(73180800)))]; + 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_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1, 1])]; + tensor var_1286 = const()[name = tensor("op_1286"), val = tensor([1, 1])]; + tensor hidden_states_63_pad_type_0 = const()[name = tensor("hidden_states_63_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_63_pad_0 = const()[name = tensor("hidden_states_63_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(73183424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74412288))), 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(74412480)))]; + tensor hidden_states_63_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1286, groups = var_1196, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = var_1284, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_25_cast_fp16)[name = tensor("hidden_states_63_cast_fp16")]; + tensor var_1291 = const()[name = tensor("op_1291"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_25_cast_fp16 = reshape(shape = var_1291, x = hidden_states_63_cast_fp16)[name = tensor("inputs_25_cast_fp16")]; + tensor hidden_states_65_axes_0 = const()[name = tensor("hidden_states_65_axes_0"), val = tensor([1])]; + tensor hidden_states_65_gamma_0_to_fp16 = const()[name = tensor("hidden_states_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74415104)))]; + tensor hidden_states_65_beta_0_to_fp16 = const()[name = tensor("hidden_states_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74417728)))]; + tensor var_1307_to_fp16 = const()[name = tensor("op_1307_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_65_cast_fp16 = layer_norm(axes = hidden_states_65_axes_0, beta = hidden_states_65_beta_0_to_fp16, epsilon = var_1307_to_fp16, gamma = hidden_states_65_gamma_0_to_fp16, x = inputs_25_cast_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_1322 = const()[name = tensor("op_1322"), val = tensor([1, 1])]; + tensor var_1324 = const()[name = tensor("op_1324"), 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(74420352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75649216))), 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_fp16 = conv(dilations = var_1324, groups = var_1196, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1322, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_65_cast_fp16)[name = tensor("q_17_cast_fp16")]; + tensor var_1328 = const()[name = tensor("op_1328"), val = tensor([1, 1])]; + tensor var_1330 = const()[name = tensor("op_1330"), 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_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(75649408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76878272))), 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_17_cast_fp16 = conv(dilations = var_1330, groups = var_1196, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1328, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_65_cast_fp16)[name = tensor("k_17_cast_fp16")]; + tensor var_1334 = const()[name = tensor("op_1334"), val = tensor([1, 1])]; + tensor var_1336 = const()[name = tensor("op_1336"), 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(76878464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78107328))), 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_fp16 = conv(dilations = var_1336, groups = var_1196, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1334, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_65_cast_fp16)[name = tensor("v_17_cast_fp16")]; + tensor var_1340 = const()[name = tensor("op_1340"), val = tensor([1, 20, 64, -1])]; + tensor var_1341_cast_fp16 = reshape(shape = var_1340, x = q_17_cast_fp16)[name = tensor("op_1341_cast_fp16")]; + tensor var_1342 = const()[name = tensor("op_1342"), val = tensor([1, 20, 64, -1])]; + tensor var_1343_cast_fp16 = reshape(shape = var_1342, x = k_17_cast_fp16)[name = tensor("op_1343_cast_fp16")]; + tensor var_1344 = const()[name = tensor("op_1344"), val = tensor([1, 20, 64, -1])]; + tensor var_1345_cast_fp16 = reshape(shape = var_1344, x = v_17_cast_fp16)[name = tensor("op_1345_cast_fp16")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1341_cast_fp16, y = var_1343_cast_fp16)[name = tensor("attn_weights_33_cast_fp16")]; + tensor var_1187_to_fp16 = const()[name = tensor("op_1187_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_35_cast_fp16")]; + tensor var_1349_cast_fp16 = softmax(axis = var_1180, x = attn_weights_35_cast_fp16)[name = tensor("op_1349_cast_fp16")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1345_cast_fp16, y = var_1349_cast_fp16)[name = tensor("attn_17_cast_fp16")]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([1, 1280, 1, -1])]; + tensor input_135_cast_fp16 = reshape(shape = var_1353, x = attn_17_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor var_1358 = const()[name = tensor("op_1358"), val = tensor([1, 1])]; + tensor var_1360 = const()[name = tensor("op_1360"), val = tensor([1, 1])]; + tensor var_1362_pad_type_0 = const()[name = tensor("op_1362_pad_type_0"), val = tensor("custom")]; + tensor var_1362_pad_0 = const()[name = tensor("op_1362_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(78107520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79336384))), 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(79336576)))]; + tensor var_1362_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1360, groups = var_1196, pad = var_1362_pad_0, pad_type = var_1362_pad_type_0, strides = var_1358, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = tensor("op_1362_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = var_1362_cast_fp16, y = inputs_25_cast_fp16)[name = tensor("inputs_27_cast_fp16")]; + tensor hidden_states_67_axes_0 = const()[name = tensor("hidden_states_67_axes_0"), val = tensor([1])]; + tensor hidden_states_67_gamma_0_to_fp16 = const()[name = tensor("hidden_states_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79339200)))]; + tensor hidden_states_67_beta_0_to_fp16 = const()[name = tensor("hidden_states_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79341824)))]; + tensor var_1372_to_fp16 = const()[name = tensor("op_1372_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_67_cast_fp16 = layer_norm(axes = hidden_states_67_axes_0, beta = hidden_states_67_beta_0_to_fp16, epsilon = var_1372_to_fp16, gamma = hidden_states_67_gamma_0_to_fp16, x = inputs_27_cast_fp16)[name = tensor("hidden_states_67_cast_fp16")]; + tensor var_1387 = const()[name = tensor("op_1387"), val = tensor([1, 1])]; + tensor var_1389 = const()[name = tensor("op_1389"), 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(79344448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80573312))), 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_fp16 = conv(dilations = var_1389, groups = var_1196, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1387, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_67_cast_fp16)[name = tensor("q_19_cast_fp16")]; + tensor var_1393 = const()[name = tensor("op_1393"), val = tensor([1, 1])]; + tensor var_1395 = const()[name = tensor("op_1395"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_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(80573504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82539648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_19_cast_fp16 = conv(dilations = var_1395, groups = var_1196, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1393, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_19_cast_fp16")]; + tensor var_1399 = const()[name = tensor("op_1399"), val = tensor([1, 1])]; + tensor var_1401 = const()[name = tensor("op_1401"), 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(82539840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84505984))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_19_cast_fp16 = conv(dilations = var_1401, groups = var_1196, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1399, 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_fp16")]; + tensor var_1405 = const()[name = tensor("op_1405"), val = tensor([1, 20, 64, -1])]; + tensor var_1406_cast_fp16 = reshape(shape = var_1405, x = q_19_cast_fp16)[name = tensor("op_1406_cast_fp16")]; + tensor var_1407 = const()[name = tensor("op_1407"), val = tensor([1, 20, 64, -1])]; + tensor var_1408_cast_fp16 = reshape(shape = var_1407, x = k_19_cast_fp16)[name = tensor("op_1408_cast_fp16")]; + tensor var_1409 = const()[name = tensor("op_1409"), val = tensor([1, 20, 64, -1])]; + tensor var_1410_cast_fp16 = reshape(shape = var_1409, x = v_19_cast_fp16)[name = tensor("op_1410_cast_fp16")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast_fp16 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1406_cast_fp16, y = var_1408_cast_fp16)[name = tensor("attn_weights_37_cast_fp16")]; + tensor attn_weights_39_cast_fp16 = mul(x = attn_weights_37_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_39_cast_fp16")]; + tensor var_1414_cast_fp16 = softmax(axis = var_1180, x = attn_weights_39_cast_fp16)[name = tensor("op_1414_cast_fp16")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1410_cast_fp16, y = var_1414_cast_fp16)[name = tensor("attn_19_cast_fp16")]; + tensor var_1418 = const()[name = tensor("op_1418"), val = tensor([1, 1280, 1, -1])]; + tensor input_137_cast_fp16 = reshape(shape = var_1418, x = attn_19_cast_fp16)[name = tensor("input_137_cast_fp16")]; + tensor var_1423 = const()[name = tensor("op_1423"), val = tensor([1, 1])]; + tensor var_1425 = const()[name = tensor("op_1425"), val = tensor([1, 1])]; + tensor var_1427_pad_type_0 = const()[name = tensor("op_1427_pad_type_0"), val = tensor("custom")]; + tensor var_1427_pad_0 = const()[name = tensor("op_1427_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(84506176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85735040))), 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(85735232)))]; + tensor var_1427_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1425, groups = var_1196, pad = var_1427_pad_0, pad_type = var_1427_pad_type_0, strides = var_1423, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor("op_1427_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = var_1427_cast_fp16, y = inputs_27_cast_fp16)[name = tensor("inputs_29_cast_fp16")]; + tensor input_139_axes_0 = const()[name = tensor("input_139_axes_0"), val = tensor([1])]; + tensor input_139_gamma_0_to_fp16 = const()[name = tensor("input_139_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85737856)))]; + tensor input_139_beta_0_to_fp16 = const()[name = tensor("input_139_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85740480)))]; + tensor var_1437_to_fp16 = const()[name = tensor("op_1437_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_139_cast_fp16 = layer_norm(axes = input_139_axes_0, beta = input_139_beta_0_to_fp16, epsilon = var_1437_to_fp16, gamma = input_139_gamma_0_to_fp16, x = inputs_29_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([1, 1])]; + tensor var_1455 = const()[name = tensor("op_1455"), val = tensor([1, 1])]; + tensor var_1457_pad_type_0 = const()[name = tensor("op_1457_pad_type_0"), val = tensor("custom")]; + tensor var_1457_pad_0 = const()[name = tensor("op_1457_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(85743104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95573568))), 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(95573760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95581504))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1457_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1455, groups = var_1196, pad = var_1457_pad_0, pad_type = var_1457_pad_type_0, strides = var_1453, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = tensor("op_1457_cast_fp16")]; + tensor var_1458_split_sizes_0 = const()[name = tensor("op_1458_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1458_axis_0 = const()[name = tensor("op_1458_axis_0"), val = tensor(1)]; + tensor var_1458_cast_fp16_0, tensor var_1458_cast_fp16_1 = split(axis = var_1458_axis_0, split_sizes = var_1458_split_sizes_0, x = var_1457_cast_fp16)[name = tensor("op_1458_cast_fp16")]; + tensor var_1460_mode_0 = const()[name = tensor("op_1460_mode_0"), val = tensor("EXACT")]; + tensor var_1460_cast_fp16 = gelu(mode = var_1460_mode_0, x = var_1458_cast_fp16_1)[name = tensor("op_1460_cast_fp16")]; + tensor input_141_cast_fp16 = mul(x = var_1458_cast_fp16_0, y = var_1460_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor var_1464 = const()[name = tensor("op_1464"), val = tensor([1, 1])]; + tensor var_1466 = const()[name = tensor("op_1466"), val = tensor([1, 1])]; + tensor var_1468_pad_type_0 = const()[name = tensor("op_1468_pad_type_0"), val = tensor("custom")]; + tensor var_1468_pad_0 = const()[name = tensor("op_1468_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(95581696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100496960))), 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(100497152)))]; + tensor var_1468_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1466, groups = var_1196, pad = var_1468_pad_0, pad_type = var_1468_pad_type_0, strides = var_1464, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_141_cast_fp16)[name = tensor("op_1468_cast_fp16")]; + tensor inputs_31_cast_fp16 = add(x = var_1468_cast_fp16, y = inputs_29_cast_fp16)[name = tensor("inputs_31_cast_fp16")]; + tensor hidden_states_71_axes_0 = const()[name = tensor("hidden_states_71_axes_0"), val = tensor([1])]; + tensor hidden_states_71_gamma_0_to_fp16 = const()[name = tensor("hidden_states_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100499776)))]; + tensor hidden_states_71_beta_0_to_fp16 = const()[name = tensor("hidden_states_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100502400)))]; + tensor var_1484_to_fp16 = const()[name = tensor("op_1484_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_71_cast_fp16 = layer_norm(axes = hidden_states_71_axes_0, beta = hidden_states_71_beta_0_to_fp16, epsilon = var_1484_to_fp16, gamma = hidden_states_71_gamma_0_to_fp16, x = inputs_31_cast_fp16)[name = tensor("hidden_states_71_cast_fp16")]; + tensor var_1499 = const()[name = tensor("op_1499"), val = tensor([1, 1])]; + tensor var_1501 = const()[name = tensor("op_1501"), 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_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100505024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101733888))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_21_cast_fp16 = conv(dilations = var_1501, groups = var_1196, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1499, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_71_cast_fp16)[name = tensor("q_21_cast_fp16")]; + tensor var_1505 = const()[name = tensor("op_1505"), val = tensor([1, 1])]; + tensor var_1507 = const()[name = tensor("op_1507"), 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_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101734080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102962944))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_21_cast_fp16 = conv(dilations = var_1507, groups = var_1196, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1505, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_71_cast_fp16)[name = tensor("k_21_cast_fp16")]; + tensor var_1511 = const()[name = tensor("op_1511"), val = tensor([1, 1])]; + tensor var_1513 = const()[name = tensor("op_1513"), 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_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102963136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104192000))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_21_cast_fp16 = conv(dilations = var_1513, groups = var_1196, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1511, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_71_cast_fp16)[name = tensor("v_21_cast_fp16")]; + tensor var_1517 = const()[name = tensor("op_1517"), val = tensor([1, 20, 64, -1])]; + tensor var_1518_cast_fp16 = reshape(shape = var_1517, x = q_21_cast_fp16)[name = tensor("op_1518_cast_fp16")]; + tensor var_1519 = const()[name = tensor("op_1519"), val = tensor([1, 20, 64, -1])]; + tensor var_1520_cast_fp16 = reshape(shape = var_1519, x = k_21_cast_fp16)[name = tensor("op_1520_cast_fp16")]; + tensor var_1521 = const()[name = tensor("op_1521"), val = tensor([1, 20, 64, -1])]; + tensor var_1522_cast_fp16 = reshape(shape = var_1521, x = v_21_cast_fp16)[name = tensor("op_1522_cast_fp16")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1518_cast_fp16, y = var_1520_cast_fp16)[name = tensor("attn_weights_41_cast_fp16")]; + tensor attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_43_cast_fp16")]; + tensor var_1526_cast_fp16 = softmax(axis = var_1180, x = attn_weights_43_cast_fp16)[name = tensor("op_1526_cast_fp16")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1522_cast_fp16, y = var_1526_cast_fp16)[name = tensor("attn_21_cast_fp16")]; + tensor var_1530 = const()[name = tensor("op_1530"), val = tensor([1, 1280, 1, -1])]; + tensor input_143_cast_fp16 = reshape(shape = var_1530, x = attn_21_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor var_1535 = const()[name = tensor("op_1535"), val = tensor([1, 1])]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1, 1])]; + tensor var_1539_pad_type_0 = const()[name = tensor("op_1539_pad_type_0"), val = tensor("custom")]; + tensor var_1539_pad_0 = const()[name = tensor("op_1539_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104192192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105421056))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105421248)))]; + tensor var_1539_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1537, groups = var_1196, pad = var_1539_pad_0, pad_type = var_1539_pad_type_0, strides = var_1535, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = tensor("op_1539_cast_fp16")]; + tensor inputs_33_cast_fp16 = add(x = var_1539_cast_fp16, y = inputs_31_cast_fp16)[name = tensor("inputs_33_cast_fp16")]; + tensor hidden_states_73_axes_0 = const()[name = tensor("hidden_states_73_axes_0"), val = tensor([1])]; + tensor hidden_states_73_gamma_0_to_fp16 = const()[name = tensor("hidden_states_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105423872)))]; + tensor hidden_states_73_beta_0_to_fp16 = const()[name = tensor("hidden_states_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105426496)))]; + tensor var_1549_to_fp16 = const()[name = tensor("op_1549_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_73_cast_fp16 = layer_norm(axes = hidden_states_73_axes_0, beta = hidden_states_73_beta_0_to_fp16, epsilon = var_1549_to_fp16, gamma = hidden_states_73_gamma_0_to_fp16, x = inputs_33_cast_fp16)[name = tensor("hidden_states_73_cast_fp16")]; + tensor var_1564 = const()[name = tensor("op_1564"), val = tensor([1, 1])]; + tensor var_1566 = const()[name = tensor("op_1566"), 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_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105429120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106657984))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_23_cast_fp16 = conv(dilations = var_1566, groups = var_1196, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1564, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_73_cast_fp16)[name = tensor("q_23_cast_fp16")]; + tensor var_1570 = const()[name = tensor("op_1570"), val = tensor([1, 1])]; + tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106658176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108624320))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_23_cast_fp16 = conv(dilations = var_1572, groups = var_1196, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1570, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_23_cast_fp16")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([1, 1])]; + tensor var_1578 = const()[name = tensor("op_1578"), 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_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108624512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110590656))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_23_cast_fp16 = conv(dilations = var_1578, groups = var_1196, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1576, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_23_cast_fp16")]; + tensor var_1582 = const()[name = tensor("op_1582"), val = tensor([1, 20, 64, -1])]; + tensor var_1583_cast_fp16 = reshape(shape = var_1582, x = q_23_cast_fp16)[name = tensor("op_1583_cast_fp16")]; + tensor var_1584 = const()[name = tensor("op_1584"), val = tensor([1, 20, 64, -1])]; + tensor var_1585_cast_fp16 = reshape(shape = var_1584, x = k_23_cast_fp16)[name = tensor("op_1585_cast_fp16")]; + tensor var_1586 = const()[name = tensor("op_1586"), val = tensor([1, 20, 64, -1])]; + tensor var_1587_cast_fp16 = reshape(shape = var_1586, x = v_23_cast_fp16)[name = tensor("op_1587_cast_fp16")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast_fp16 = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1583_cast_fp16, y = var_1585_cast_fp16)[name = tensor("attn_weights_45_cast_fp16")]; + tensor attn_weights_47_cast_fp16 = mul(x = attn_weights_45_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_47_cast_fp16")]; + tensor var_1591_cast_fp16 = softmax(axis = var_1180, x = attn_weights_47_cast_fp16)[name = tensor("op_1591_cast_fp16")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1587_cast_fp16, y = var_1591_cast_fp16)[name = tensor("attn_23_cast_fp16")]; + tensor var_1595 = const()[name = tensor("op_1595"), val = tensor([1, 1280, 1, -1])]; + tensor input_145_cast_fp16 = reshape(shape = var_1595, x = attn_23_cast_fp16)[name = tensor("input_145_cast_fp16")]; + tensor var_1600 = const()[name = tensor("op_1600"), val = tensor([1, 1])]; + tensor var_1602 = const()[name = tensor("op_1602"), val = tensor([1, 1])]; + tensor var_1604_pad_type_0 = const()[name = tensor("op_1604_pad_type_0"), val = tensor("custom")]; + tensor var_1604_pad_0 = const()[name = tensor("op_1604_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110590848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111819712))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111819904)))]; + tensor var_1604_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1602, groups = var_1196, pad = var_1604_pad_0, pad_type = var_1604_pad_type_0, strides = var_1600, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_145_cast_fp16)[name = tensor("op_1604_cast_fp16")]; + tensor inputs_35_cast_fp16 = add(x = var_1604_cast_fp16, y = inputs_33_cast_fp16)[name = tensor("inputs_35_cast_fp16")]; + tensor input_147_axes_0 = const()[name = tensor("input_147_axes_0"), val = tensor([1])]; + tensor input_147_gamma_0_to_fp16 = const()[name = tensor("input_147_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111822528)))]; + tensor input_147_beta_0_to_fp16 = const()[name = tensor("input_147_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111825152)))]; + tensor var_1614_to_fp16 = const()[name = tensor("op_1614_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_147_cast_fp16 = layer_norm(axes = input_147_axes_0, beta = input_147_beta_0_to_fp16, epsilon = var_1614_to_fp16, gamma = input_147_gamma_0_to_fp16, x = inputs_35_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor var_1630 = const()[name = tensor("op_1630"), val = tensor([1, 1])]; + tensor var_1632 = const()[name = tensor("op_1632"), val = tensor([1, 1])]; + tensor var_1634_pad_type_0 = const()[name = tensor("op_1634_pad_type_0"), val = tensor("custom")]; + tensor var_1634_pad_0 = const()[name = tensor("op_1634_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111827776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121658240))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121658432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121666176))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1634_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1632, groups = var_1196, pad = var_1634_pad_0, pad_type = var_1634_pad_type_0, strides = var_1630, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = tensor("op_1634_cast_fp16")]; + tensor var_1635_split_sizes_0 = const()[name = tensor("op_1635_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1635_axis_0 = const()[name = tensor("op_1635_axis_0"), val = tensor(1)]; + tensor var_1635_cast_fp16_0, tensor var_1635_cast_fp16_1 = split(axis = var_1635_axis_0, split_sizes = var_1635_split_sizes_0, x = var_1634_cast_fp16)[name = tensor("op_1635_cast_fp16")]; + tensor var_1637_mode_0 = const()[name = tensor("op_1637_mode_0"), val = tensor("EXACT")]; + tensor var_1637_cast_fp16 = gelu(mode = var_1637_mode_0, x = var_1635_cast_fp16_1)[name = tensor("op_1637_cast_fp16")]; + tensor input_149_cast_fp16 = mul(x = var_1635_cast_fp16_0, y = var_1637_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor var_1641 = const()[name = tensor("op_1641"), val = tensor([1, 1])]; + tensor var_1643 = const()[name = tensor("op_1643"), val = tensor([1, 1])]; + tensor var_1645_pad_type_0 = const()[name = tensor("op_1645_pad_type_0"), val = tensor("custom")]; + tensor var_1645_pad_0 = const()[name = tensor("op_1645_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121666368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126581632))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126581824)))]; + tensor var_1645_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1643, groups = var_1196, pad = var_1645_pad_0, pad_type = var_1645_pad_type_0, strides = var_1641, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_149_cast_fp16)[name = tensor("op_1645_cast_fp16")]; + tensor inputs_37_cast_fp16 = add(x = var_1645_cast_fp16, y = inputs_35_cast_fp16)[name = tensor("inputs_37_cast_fp16")]; + tensor hidden_states_77_axes_0 = const()[name = tensor("hidden_states_77_axes_0"), val = tensor([1])]; + tensor hidden_states_77_gamma_0_to_fp16 = const()[name = tensor("hidden_states_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126584448)))]; + tensor hidden_states_77_beta_0_to_fp16 = const()[name = tensor("hidden_states_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126587072)))]; + tensor var_1661_to_fp16 = const()[name = tensor("op_1661_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_77_cast_fp16 = layer_norm(axes = hidden_states_77_axes_0, beta = hidden_states_77_beta_0_to_fp16, epsilon = var_1661_to_fp16, gamma = hidden_states_77_gamma_0_to_fp16, x = inputs_37_cast_fp16)[name = tensor("hidden_states_77_cast_fp16")]; + tensor var_1676 = const()[name = tensor("op_1676"), val = tensor([1, 1])]; + tensor var_1678 = const()[name = tensor("op_1678"), 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 down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126589696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127818560))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_25_cast_fp16 = conv(dilations = var_1678, groups = var_1196, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1676, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_77_cast_fp16)[name = tensor("q_25_cast_fp16")]; + tensor var_1682 = const()[name = tensor("op_1682"), val = tensor([1, 1])]; + tensor var_1684 = const()[name = tensor("op_1684"), 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_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127818752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129047616))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_25_cast_fp16 = conv(dilations = var_1684, groups = var_1196, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1682, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_77_cast_fp16)[name = tensor("k_25_cast_fp16")]; + tensor var_1688 = const()[name = tensor("op_1688"), val = tensor([1, 1])]; + tensor var_1690 = const()[name = tensor("op_1690"), 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 down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129047808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130276672))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_25_cast_fp16 = conv(dilations = var_1690, groups = var_1196, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1688, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_77_cast_fp16)[name = tensor("v_25_cast_fp16")]; + tensor var_1694 = const()[name = tensor("op_1694"), val = tensor([1, 20, 64, -1])]; + tensor var_1695_cast_fp16 = reshape(shape = var_1694, x = q_25_cast_fp16)[name = tensor("op_1695_cast_fp16")]; + tensor var_1696 = const()[name = tensor("op_1696"), val = tensor([1, 20, 64, -1])]; + tensor var_1697_cast_fp16 = reshape(shape = var_1696, x = k_25_cast_fp16)[name = tensor("op_1697_cast_fp16")]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([1, 20, 64, -1])]; + tensor var_1699_cast_fp16 = reshape(shape = var_1698, x = v_25_cast_fp16)[name = tensor("op_1699_cast_fp16")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1695_cast_fp16, y = var_1697_cast_fp16)[name = tensor("attn_weights_49_cast_fp16")]; + tensor attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_51_cast_fp16")]; + tensor var_1703_cast_fp16 = softmax(axis = var_1180, x = attn_weights_51_cast_fp16)[name = tensor("op_1703_cast_fp16")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1699_cast_fp16, y = var_1703_cast_fp16)[name = tensor("attn_25_cast_fp16")]; + tensor var_1707 = const()[name = tensor("op_1707"), val = tensor([1, 1280, 1, -1])]; + tensor input_151_cast_fp16 = reshape(shape = var_1707, x = attn_25_cast_fp16)[name = tensor("input_151_cast_fp16")]; + tensor var_1712 = const()[name = tensor("op_1712"), val = tensor([1, 1])]; + tensor var_1714 = const()[name = tensor("op_1714"), val = tensor([1, 1])]; + tensor var_1716_pad_type_0 = const()[name = tensor("op_1716_pad_type_0"), val = tensor("custom")]; + tensor var_1716_pad_0 = const()[name = tensor("op_1716_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130276864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131505728))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131505920)))]; + tensor var_1716_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_1714, groups = var_1196, pad = var_1716_pad_0, pad_type = var_1716_pad_type_0, strides = var_1712, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_151_cast_fp16)[name = tensor("op_1716_cast_fp16")]; + tensor inputs_39_cast_fp16 = add(x = var_1716_cast_fp16, y = inputs_37_cast_fp16)[name = tensor("inputs_39_cast_fp16")]; + tensor hidden_states_79_axes_0 = const()[name = tensor("hidden_states_79_axes_0"), val = tensor([1])]; + tensor hidden_states_79_gamma_0_to_fp16 = const()[name = tensor("hidden_states_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131508544)))]; + tensor hidden_states_79_beta_0_to_fp16 = const()[name = tensor("hidden_states_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131511168)))]; + tensor var_1726_to_fp16 = const()[name = tensor("op_1726_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_79_cast_fp16 = layer_norm(axes = hidden_states_79_axes_0, beta = hidden_states_79_beta_0_to_fp16, epsilon = var_1726_to_fp16, gamma = hidden_states_79_gamma_0_to_fp16, x = inputs_39_cast_fp16)[name = tensor("hidden_states_79_cast_fp16")]; + tensor var_1741 = const()[name = tensor("op_1741"), val = tensor([1, 1])]; + tensor var_1743 = const()[name = tensor("op_1743"), 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 down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131513792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132742656))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_27_cast_fp16 = conv(dilations = var_1743, groups = var_1196, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1741, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_79_cast_fp16)[name = tensor("q_27_cast_fp16")]; + tensor var_1747 = const()[name = tensor("op_1747"), val = tensor([1, 1])]; + tensor var_1749 = const()[name = tensor("op_1749"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132742848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134708992))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_27_cast_fp16 = conv(dilations = var_1749, groups = var_1196, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1747, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_27_cast_fp16")]; + 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 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 down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134709184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136675328))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_27_cast_fp16 = conv(dilations = var_1755, groups = var_1196, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_1753, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_27_cast_fp16")]; + tensor var_1759 = const()[name = tensor("op_1759"), val = tensor([1, 20, 64, -1])]; + tensor var_1760_cast_fp16 = reshape(shape = var_1759, x = q_27_cast_fp16)[name = tensor("op_1760_cast_fp16")]; + tensor var_1761 = const()[name = tensor("op_1761"), val = tensor([1, 20, 64, -1])]; + tensor var_1762_cast_fp16 = reshape(shape = var_1761, x = k_27_cast_fp16)[name = tensor("op_1762_cast_fp16")]; + tensor var_1763 = const()[name = tensor("op_1763"), val = tensor([1, 20, 64, -1])]; + tensor var_1764_cast_fp16 = reshape(shape = var_1763, x = v_27_cast_fp16)[name = tensor("op_1764_cast_fp16")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast_fp16 = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_1760_cast_fp16, y = var_1762_cast_fp16)[name = tensor("attn_weights_53_cast_fp16")]; + tensor attn_weights_55_cast_fp16 = mul(x = attn_weights_53_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_55_cast_fp16")]; + tensor var_1768_cast_fp16 = softmax(axis = var_1180, x = attn_weights_55_cast_fp16)[name = tensor("op_1768_cast_fp16")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1764_cast_fp16, y = var_1768_cast_fp16)[name = tensor("attn_27_cast_fp16")]; + tensor var_1772 = const()[name = tensor("op_1772"), val = tensor([1, 1280, 1, -1])]; + tensor input_153_cast_fp16 = reshape(shape = var_1772, x = attn_27_cast_fp16)[name = tensor("input_153_cast_fp16")]; + tensor var_1777 = const()[name = tensor("op_1777"), val = tensor([1, 1])]; + tensor var_1779 = const()[name = tensor("op_1779"), val = tensor([1, 1])]; + tensor var_1781_pad_type_0 = const()[name = tensor("op_1781_pad_type_0"), val = tensor("custom")]; + tensor var_1781_pad_0 = const()[name = tensor("op_1781_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136675520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137904384))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137904576)))]; + tensor var_1781_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_1779, groups = var_1196, pad = var_1781_pad_0, pad_type = var_1781_pad_type_0, strides = var_1777, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = tensor("op_1781_cast_fp16")]; + tensor inputs_41_cast_fp16 = add(x = var_1781_cast_fp16, y = inputs_39_cast_fp16)[name = tensor("inputs_41_cast_fp16")]; + tensor input_155_axes_0 = const()[name = tensor("input_155_axes_0"), val = tensor([1])]; + tensor input_155_gamma_0_to_fp16 = const()[name = tensor("input_155_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137907200)))]; + tensor input_155_beta_0_to_fp16 = const()[name = tensor("input_155_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137909824)))]; + tensor var_1791_to_fp16 = const()[name = tensor("op_1791_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_155_cast_fp16 = layer_norm(axes = input_155_axes_0, beta = input_155_beta_0_to_fp16, epsilon = var_1791_to_fp16, gamma = input_155_gamma_0_to_fp16, x = inputs_41_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor var_1807 = const()[name = tensor("op_1807"), val = tensor([1, 1])]; + tensor var_1809 = const()[name = tensor("op_1809"), val = tensor([1, 1])]; + tensor var_1811_pad_type_0 = const()[name = tensor("op_1811_pad_type_0"), val = tensor("custom")]; + tensor var_1811_pad_0 = const()[name = tensor("op_1811_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137912448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147742912))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147743104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147750848))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1811_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1809, groups = var_1196, pad = var_1811_pad_0, pad_type = var_1811_pad_type_0, strides = var_1807, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_155_cast_fp16)[name = tensor("op_1811_cast_fp16")]; + tensor var_1812_split_sizes_0 = const()[name = tensor("op_1812_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1812_axis_0 = const()[name = tensor("op_1812_axis_0"), val = tensor(1)]; + tensor var_1812_cast_fp16_0, tensor var_1812_cast_fp16_1 = split(axis = var_1812_axis_0, split_sizes = var_1812_split_sizes_0, x = var_1811_cast_fp16)[name = tensor("op_1812_cast_fp16")]; + tensor var_1814_mode_0 = const()[name = tensor("op_1814_mode_0"), val = tensor("EXACT")]; + tensor var_1814_cast_fp16 = gelu(mode = var_1814_mode_0, x = var_1812_cast_fp16_1)[name = tensor("op_1814_cast_fp16")]; + tensor input_157_cast_fp16 = mul(x = var_1812_cast_fp16_0, y = var_1814_cast_fp16)[name = tensor("input_157_cast_fp16")]; + tensor var_1818 = const()[name = tensor("op_1818"), val = tensor([1, 1])]; + tensor var_1820 = const()[name = tensor("op_1820"), val = tensor([1, 1])]; + tensor var_1822_pad_type_0 = const()[name = tensor("op_1822_pad_type_0"), val = tensor("custom")]; + tensor var_1822_pad_0 = const()[name = tensor("op_1822_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147751040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152666304))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152666496)))]; + tensor var_1822_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_1820, groups = var_1196, pad = var_1822_pad_0, pad_type = var_1822_pad_type_0, strides = var_1818, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = tensor("op_1822_cast_fp16")]; + tensor inputs_43_cast_fp16 = add(x = var_1822_cast_fp16, y = inputs_41_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; + tensor hidden_states_83_axes_0 = const()[name = tensor("hidden_states_83_axes_0"), val = tensor([1])]; + tensor hidden_states_83_gamma_0_to_fp16 = const()[name = tensor("hidden_states_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152669120)))]; + tensor hidden_states_83_beta_0_to_fp16 = const()[name = tensor("hidden_states_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152671744)))]; + tensor var_1838_to_fp16 = const()[name = tensor("op_1838_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_83_cast_fp16 = layer_norm(axes = hidden_states_83_axes_0, beta = hidden_states_83_beta_0_to_fp16, epsilon = var_1838_to_fp16, gamma = hidden_states_83_gamma_0_to_fp16, x = inputs_43_cast_fp16)[name = tensor("hidden_states_83_cast_fp16")]; + tensor var_1853 = const()[name = tensor("op_1853"), val = tensor([1, 1])]; + tensor var_1855 = const()[name = tensor("op_1855"), 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 down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152674368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153903232))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_29_cast_fp16 = conv(dilations = var_1855, groups = var_1196, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_1853, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_83_cast_fp16)[name = tensor("q_29_cast_fp16")]; + tensor var_1859 = const()[name = tensor("op_1859"), val = tensor([1, 1])]; + tensor var_1861 = const()[name = tensor("op_1861"), 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_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153903424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155132288))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_29_cast_fp16 = conv(dilations = var_1861, groups = var_1196, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_1859, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_83_cast_fp16)[name = tensor("k_29_cast_fp16")]; + tensor var_1865 = const()[name = tensor("op_1865"), val = tensor([1, 1])]; + tensor var_1867 = const()[name = tensor("op_1867"), 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 down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155132480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156361344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_29_cast_fp16 = conv(dilations = var_1867, groups = var_1196, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_1865, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_83_cast_fp16)[name = tensor("v_29_cast_fp16")]; + tensor var_1871 = const()[name = tensor("op_1871"), val = tensor([1, 20, 64, -1])]; + tensor var_1872_cast_fp16 = reshape(shape = var_1871, x = q_29_cast_fp16)[name = tensor("op_1872_cast_fp16")]; + tensor var_1873 = const()[name = tensor("op_1873"), val = tensor([1, 20, 64, -1])]; + tensor var_1874_cast_fp16 = reshape(shape = var_1873, x = k_29_cast_fp16)[name = tensor("op_1874_cast_fp16")]; + tensor var_1875 = const()[name = tensor("op_1875"), val = tensor([1, 20, 64, -1])]; + tensor var_1876_cast_fp16 = reshape(shape = var_1875, x = v_29_cast_fp16)[name = tensor("op_1876_cast_fp16")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_1872_cast_fp16, y = var_1874_cast_fp16)[name = tensor("attn_weights_57_cast_fp16")]; + tensor attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_59_cast_fp16")]; + tensor var_1880_cast_fp16 = softmax(axis = var_1180, x = attn_weights_59_cast_fp16)[name = tensor("op_1880_cast_fp16")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1876_cast_fp16, y = var_1880_cast_fp16)[name = tensor("attn_29_cast_fp16")]; + tensor var_1884 = const()[name = tensor("op_1884"), val = tensor([1, 1280, 1, -1])]; + tensor input_159_cast_fp16 = reshape(shape = var_1884, x = attn_29_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor var_1889 = const()[name = tensor("op_1889"), val = tensor([1, 1])]; + tensor var_1891 = const()[name = tensor("op_1891"), val = tensor([1, 1])]; + tensor var_1893_pad_type_0 = const()[name = tensor("op_1893_pad_type_0"), val = tensor("custom")]; + tensor var_1893_pad_0 = const()[name = tensor("op_1893_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156361536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157590400))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157590592)))]; + tensor var_1893_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_1891, groups = var_1196, pad = var_1893_pad_0, pad_type = var_1893_pad_type_0, strides = var_1889, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_159_cast_fp16)[name = tensor("op_1893_cast_fp16")]; + tensor inputs_45_cast_fp16 = add(x = var_1893_cast_fp16, y = inputs_43_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; + tensor hidden_states_85_axes_0 = const()[name = tensor("hidden_states_85_axes_0"), val = tensor([1])]; + tensor hidden_states_85_gamma_0_to_fp16 = const()[name = tensor("hidden_states_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157593216)))]; + tensor hidden_states_85_beta_0_to_fp16 = const()[name = tensor("hidden_states_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157595840)))]; + tensor var_1903_to_fp16 = const()[name = tensor("op_1903_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_85_cast_fp16 = layer_norm(axes = hidden_states_85_axes_0, beta = hidden_states_85_beta_0_to_fp16, epsilon = var_1903_to_fp16, gamma = hidden_states_85_gamma_0_to_fp16, x = inputs_45_cast_fp16)[name = tensor("hidden_states_85_cast_fp16")]; + tensor var_1918 = const()[name = tensor("op_1918"), val = tensor([1, 1])]; + tensor var_1920 = const()[name = tensor("op_1920"), 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 down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157598464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158827328))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_31_cast_fp16 = conv(dilations = var_1920, groups = var_1196, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_1918, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_85_cast_fp16)[name = tensor("q_31_cast_fp16")]; + tensor var_1924 = const()[name = tensor("op_1924"), val = tensor([1, 1])]; + tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158827520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160793664))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_31_cast_fp16 = conv(dilations = var_1926, groups = var_1196, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_1924, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_31_cast_fp16")]; + tensor var_1930 = const()[name = tensor("op_1930"), val = tensor([1, 1])]; + tensor var_1932 = const()[name = tensor("op_1932"), 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 down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160793856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162760000))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_31_cast_fp16 = conv(dilations = var_1932, groups = var_1196, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_1930, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_31_cast_fp16")]; + tensor var_1936 = const()[name = tensor("op_1936"), val = tensor([1, 20, 64, -1])]; + tensor var_1937_cast_fp16 = reshape(shape = var_1936, x = q_31_cast_fp16)[name = tensor("op_1937_cast_fp16")]; + tensor var_1938 = const()[name = tensor("op_1938"), val = tensor([1, 20, 64, -1])]; + tensor var_1939_cast_fp16 = reshape(shape = var_1938, x = k_31_cast_fp16)[name = tensor("op_1939_cast_fp16")]; + tensor var_1940 = const()[name = tensor("op_1940"), val = tensor([1, 20, 64, -1])]; + tensor var_1941_cast_fp16 = reshape(shape = var_1940, x = v_31_cast_fp16)[name = tensor("op_1941_cast_fp16")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast_fp16 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_1937_cast_fp16, y = var_1939_cast_fp16)[name = tensor("attn_weights_61_cast_fp16")]; + tensor attn_weights_63_cast_fp16 = mul(x = attn_weights_61_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_63_cast_fp16")]; + tensor var_1945_cast_fp16 = softmax(axis = var_1180, x = attn_weights_63_cast_fp16)[name = tensor("op_1945_cast_fp16")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1941_cast_fp16, y = var_1945_cast_fp16)[name = tensor("attn_31_cast_fp16")]; + tensor var_1949 = const()[name = tensor("op_1949"), val = tensor([1, 1280, 1, -1])]; + tensor input_161_cast_fp16 = reshape(shape = var_1949, x = attn_31_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor var_1954 = const()[name = tensor("op_1954"), val = tensor([1, 1])]; + tensor var_1956 = const()[name = tensor("op_1956"), val = tensor([1, 1])]; + tensor var_1958_pad_type_0 = const()[name = tensor("op_1958_pad_type_0"), val = tensor("custom")]; + tensor var_1958_pad_0 = const()[name = tensor("op_1958_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162760192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163989056))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163989248)))]; + tensor var_1958_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_1956, groups = var_1196, pad = var_1958_pad_0, pad_type = var_1958_pad_type_0, strides = var_1954, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = tensor("op_1958_cast_fp16")]; + tensor inputs_47_cast_fp16 = add(x = var_1958_cast_fp16, y = inputs_45_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; + tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([1])]; + tensor input_163_gamma_0_to_fp16 = const()[name = tensor("input_163_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163991872)))]; + tensor input_163_beta_0_to_fp16 = const()[name = tensor("input_163_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163994496)))]; + tensor var_1968_to_fp16 = const()[name = tensor("op_1968_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = input_163_beta_0_to_fp16, epsilon = var_1968_to_fp16, gamma = input_163_gamma_0_to_fp16, x = inputs_47_cast_fp16)[name = tensor("input_163_cast_fp16")]; + tensor var_1984 = const()[name = tensor("op_1984"), val = tensor([1, 1])]; + tensor var_1986 = const()[name = tensor("op_1986"), val = tensor([1, 1])]; + tensor var_1988_pad_type_0 = const()[name = tensor("op_1988_pad_type_0"), val = tensor("custom")]; + tensor var_1988_pad_0 = const()[name = tensor("op_1988_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163997120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173827584))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173827776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173835520))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1988_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1986, groups = var_1196, pad = var_1988_pad_0, pad_type = var_1988_pad_type_0, strides = var_1984, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = tensor("op_1988_cast_fp16")]; + tensor var_1989_split_sizes_0 = const()[name = tensor("op_1989_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1989_axis_0 = const()[name = tensor("op_1989_axis_0"), val = tensor(1)]; + tensor var_1989_cast_fp16_0, tensor var_1989_cast_fp16_1 = split(axis = var_1989_axis_0, split_sizes = var_1989_split_sizes_0, x = var_1988_cast_fp16)[name = tensor("op_1989_cast_fp16")]; + tensor var_1991_mode_0 = const()[name = tensor("op_1991_mode_0"), val = tensor("EXACT")]; + tensor var_1991_cast_fp16 = gelu(mode = var_1991_mode_0, x = var_1989_cast_fp16_1)[name = tensor("op_1991_cast_fp16")]; + tensor input_165_cast_fp16 = mul(x = var_1989_cast_fp16_0, y = var_1991_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor var_1995 = const()[name = tensor("op_1995"), val = tensor([1, 1])]; + tensor var_1997 = const()[name = tensor("op_1997"), val = tensor([1, 1])]; + tensor var_1999_pad_type_0 = const()[name = tensor("op_1999_pad_type_0"), val = tensor("custom")]; + tensor var_1999_pad_0 = const()[name = tensor("op_1999_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173835712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178750976))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178751168)))]; + tensor var_1999_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_1997, groups = var_1196, pad = var_1999_pad_0, pad_type = var_1999_pad_type_0, strides = var_1995, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = tensor("op_1999_cast_fp16")]; + tensor inputs_49_cast_fp16 = add(x = var_1999_cast_fp16, y = inputs_47_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; + tensor hidden_states_89_axes_0 = const()[name = tensor("hidden_states_89_axes_0"), val = tensor([1])]; + tensor hidden_states_89_gamma_0_to_fp16 = const()[name = tensor("hidden_states_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178753792)))]; + tensor hidden_states_89_beta_0_to_fp16 = const()[name = tensor("hidden_states_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178756416)))]; + tensor var_2015_to_fp16 = const()[name = tensor("op_2015_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_89_cast_fp16 = layer_norm(axes = hidden_states_89_axes_0, beta = hidden_states_89_beta_0_to_fp16, epsilon = var_2015_to_fp16, gamma = hidden_states_89_gamma_0_to_fp16, x = inputs_49_cast_fp16)[name = tensor("hidden_states_89_cast_fp16")]; + tensor var_2030 = const()[name = tensor("op_2030"), val = tensor([1, 1])]; + tensor var_2032 = const()[name = tensor("op_2032"), 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 down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178759040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179987904))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_33_cast_fp16 = conv(dilations = var_2032, groups = var_1196, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2030, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = tensor("q_33_cast_fp16")]; + tensor var_2036 = const()[name = tensor("op_2036"), val = tensor([1, 1])]; + tensor var_2038 = const()[name = tensor("op_2038"), 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_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179988096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181216960))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_33_cast_fp16 = conv(dilations = var_2038, groups = var_1196, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2036, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = tensor("k_33_cast_fp16")]; + tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([1, 1])]; + tensor var_2044 = const()[name = tensor("op_2044"), 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 down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181217152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182446016))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_33_cast_fp16 = conv(dilations = var_2044, groups = var_1196, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2042, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = tensor("v_33_cast_fp16")]; + tensor var_2048 = const()[name = tensor("op_2048"), val = tensor([1, 20, 64, -1])]; + tensor var_2049_cast_fp16 = reshape(shape = var_2048, x = q_33_cast_fp16)[name = tensor("op_2049_cast_fp16")]; + tensor var_2050 = const()[name = tensor("op_2050"), val = tensor([1, 20, 64, -1])]; + tensor var_2051_cast_fp16 = reshape(shape = var_2050, x = k_33_cast_fp16)[name = tensor("op_2051_cast_fp16")]; + tensor var_2052 = const()[name = tensor("op_2052"), val = tensor([1, 20, 64, -1])]; + tensor var_2053_cast_fp16 = reshape(shape = var_2052, x = v_33_cast_fp16)[name = tensor("op_2053_cast_fp16")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2049_cast_fp16, y = var_2051_cast_fp16)[name = tensor("attn_weights_65_cast_fp16")]; + tensor attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_67_cast_fp16")]; + tensor var_2057_cast_fp16 = softmax(axis = var_1180, x = attn_weights_67_cast_fp16)[name = tensor("op_2057_cast_fp16")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2053_cast_fp16, y = var_2057_cast_fp16)[name = tensor("attn_33_cast_fp16")]; + tensor var_2061 = const()[name = tensor("op_2061"), val = tensor([1, 1280, 1, -1])]; + tensor input_167_cast_fp16 = reshape(shape = var_2061, x = attn_33_cast_fp16)[name = tensor("input_167_cast_fp16")]; + 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 var_2070_pad_type_0 = const()[name = tensor("op_2070_pad_type_0"), val = tensor("custom")]; + tensor var_2070_pad_0 = const()[name = tensor("op_2070_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182446208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183675072))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183675264)))]; + tensor var_2070_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_2068, groups = var_1196, pad = var_2070_pad_0, pad_type = var_2070_pad_type_0, strides = var_2066, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("op_2070_cast_fp16")]; + tensor inputs_51_cast_fp16 = add(x = var_2070_cast_fp16, y = inputs_49_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; + tensor hidden_states_91_axes_0 = const()[name = tensor("hidden_states_91_axes_0"), val = tensor([1])]; + tensor hidden_states_91_gamma_0_to_fp16 = const()[name = tensor("hidden_states_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183677888)))]; + tensor hidden_states_91_beta_0_to_fp16 = const()[name = tensor("hidden_states_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183680512)))]; + tensor var_2080_to_fp16 = const()[name = tensor("op_2080_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_91_cast_fp16 = layer_norm(axes = hidden_states_91_axes_0, beta = hidden_states_91_beta_0_to_fp16, epsilon = var_2080_to_fp16, gamma = hidden_states_91_gamma_0_to_fp16, x = inputs_51_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1])]; + tensor var_2097 = const()[name = tensor("op_2097"), 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 down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183683136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184912000))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_35_cast_fp16 = conv(dilations = var_2097, groups = var_1196, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2095, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_91_cast_fp16)[name = tensor("q_35_cast_fp16")]; + tensor var_2101 = const()[name = tensor("op_2101"), val = tensor([1, 1])]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184912192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186878336))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_35_cast_fp16 = conv(dilations = var_2103, groups = var_1196, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2101, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_35_cast_fp16")]; + tensor var_2107 = const()[name = tensor("op_2107"), val = tensor([1, 1])]; + tensor var_2109 = const()[name = tensor("op_2109"), 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 down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186878528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188844672))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_35_cast_fp16 = conv(dilations = var_2109, groups = var_1196, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2107, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_35_cast_fp16")]; + tensor var_2113 = const()[name = tensor("op_2113"), val = tensor([1, 20, 64, -1])]; + tensor var_2114_cast_fp16 = reshape(shape = var_2113, x = q_35_cast_fp16)[name = tensor("op_2114_cast_fp16")]; + tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, 20, 64, -1])]; + tensor var_2116_cast_fp16 = reshape(shape = var_2115, x = k_35_cast_fp16)[name = tensor("op_2116_cast_fp16")]; + tensor var_2117 = const()[name = tensor("op_2117"), val = tensor([1, 20, 64, -1])]; + tensor var_2118_cast_fp16 = reshape(shape = var_2117, x = v_35_cast_fp16)[name = tensor("op_2118_cast_fp16")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast_fp16 = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2114_cast_fp16, y = var_2116_cast_fp16)[name = tensor("attn_weights_69_cast_fp16")]; + tensor attn_weights_71_cast_fp16 = mul(x = attn_weights_69_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_71_cast_fp16")]; + tensor var_2122_cast_fp16 = softmax(axis = var_1180, x = attn_weights_71_cast_fp16)[name = tensor("op_2122_cast_fp16")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2118_cast_fp16, y = var_2122_cast_fp16)[name = tensor("attn_35_cast_fp16")]; + tensor var_2126 = const()[name = tensor("op_2126"), val = tensor([1, 1280, 1, -1])]; + tensor input_169_cast_fp16 = reshape(shape = var_2126, x = attn_35_cast_fp16)[name = tensor("input_169_cast_fp16")]; + tensor var_2131 = const()[name = tensor("op_2131"), val = tensor([1, 1])]; + tensor var_2133 = const()[name = tensor("op_2133"), val = tensor([1, 1])]; + tensor var_2135_pad_type_0 = const()[name = tensor("op_2135_pad_type_0"), val = tensor("custom")]; + tensor var_2135_pad_0 = const()[name = tensor("op_2135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188844864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190073728))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190073920)))]; + tensor var_2135_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_2133, groups = var_1196, pad = var_2135_pad_0, pad_type = var_2135_pad_type_0, strides = var_2131, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = tensor("op_2135_cast_fp16")]; + tensor inputs_53_cast_fp16 = add(x = var_2135_cast_fp16, y = inputs_51_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; + tensor input_171_axes_0 = const()[name = tensor("input_171_axes_0"), val = tensor([1])]; + tensor input_171_gamma_0_to_fp16 = const()[name = tensor("input_171_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190076544)))]; + tensor input_171_beta_0_to_fp16 = const()[name = tensor("input_171_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190079168)))]; + tensor var_2145_to_fp16 = const()[name = tensor("op_2145_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_171_cast_fp16 = layer_norm(axes = input_171_axes_0, beta = input_171_beta_0_to_fp16, epsilon = var_2145_to_fp16, gamma = input_171_gamma_0_to_fp16, x = inputs_53_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor var_2161 = const()[name = tensor("op_2161"), val = tensor([1, 1])]; + tensor var_2163 = const()[name = tensor("op_2163"), val = tensor([1, 1])]; + tensor var_2165_pad_type_0 = const()[name = tensor("op_2165_pad_type_0"), val = tensor("custom")]; + tensor var_2165_pad_0 = const()[name = tensor("op_2165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190081792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199912256))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199912448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199920192))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2165_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2163, groups = var_1196, pad = var_2165_pad_0, pad_type = var_2165_pad_type_0, strides = var_2161, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = tensor("op_2165_cast_fp16")]; + tensor var_2166_split_sizes_0 = const()[name = tensor("op_2166_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2166_axis_0 = const()[name = tensor("op_2166_axis_0"), val = tensor(1)]; + tensor var_2166_cast_fp16_0, tensor var_2166_cast_fp16_1 = split(axis = var_2166_axis_0, split_sizes = var_2166_split_sizes_0, x = var_2165_cast_fp16)[name = tensor("op_2166_cast_fp16")]; + tensor var_2168_mode_0 = const()[name = tensor("op_2168_mode_0"), val = tensor("EXACT")]; + tensor var_2168_cast_fp16 = gelu(mode = var_2168_mode_0, x = var_2166_cast_fp16_1)[name = tensor("op_2168_cast_fp16")]; + tensor input_173_cast_fp16 = mul(x = var_2166_cast_fp16_0, y = var_2168_cast_fp16)[name = tensor("input_173_cast_fp16")]; + tensor var_2172 = const()[name = tensor("op_2172"), val = tensor([1, 1])]; + tensor var_2174 = const()[name = tensor("op_2174"), val = tensor([1, 1])]; + tensor var_2176_pad_type_0 = const()[name = tensor("op_2176_pad_type_0"), val = tensor("custom")]; + tensor var_2176_pad_0 = const()[name = tensor("op_2176_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199920384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204835648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204835840)))]; + tensor var_2176_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_2174, groups = var_1196, pad = var_2176_pad_0, pad_type = var_2176_pad_type_0, strides = var_2172, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = tensor("op_2176_cast_fp16")]; + tensor inputs_55_cast_fp16 = add(x = var_2176_cast_fp16, y = inputs_53_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; + tensor hidden_states_95_axes_0 = const()[name = tensor("hidden_states_95_axes_0"), val = tensor([1])]; + tensor hidden_states_95_gamma_0_to_fp16 = const()[name = tensor("hidden_states_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204838464)))]; + tensor hidden_states_95_beta_0_to_fp16 = const()[name = tensor("hidden_states_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204841088)))]; + tensor var_2192_to_fp16 = const()[name = tensor("op_2192_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_95_cast_fp16 = layer_norm(axes = hidden_states_95_axes_0, beta = hidden_states_95_beta_0_to_fp16, epsilon = var_2192_to_fp16, gamma = hidden_states_95_gamma_0_to_fp16, x = inputs_55_cast_fp16)[name = tensor("hidden_states_95_cast_fp16")]; + tensor var_2207 = const()[name = tensor("op_2207"), val = tensor([1, 1])]; + tensor var_2209 = const()[name = tensor("op_2209"), 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 down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204843712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206072576))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_37_cast_fp16 = conv(dilations = var_2209, groups = var_1196, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2207, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_95_cast_fp16)[name = tensor("q_37_cast_fp16")]; + tensor var_2213 = const()[name = tensor("op_2213"), val = tensor([1, 1])]; + tensor var_2215 = const()[name = tensor("op_2215"), 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_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206072768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207301632))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_37_cast_fp16 = conv(dilations = var_2215, groups = var_1196, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2213, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_95_cast_fp16)[name = tensor("k_37_cast_fp16")]; + tensor var_2219 = const()[name = tensor("op_2219"), val = tensor([1, 1])]; + tensor var_2221 = const()[name = tensor("op_2221"), 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 down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207301824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208530688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_37_cast_fp16 = conv(dilations = var_2221, groups = var_1196, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2219, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_95_cast_fp16)[name = tensor("v_37_cast_fp16")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 20, 64, -1])]; + tensor var_2226_cast_fp16 = reshape(shape = var_2225, x = q_37_cast_fp16)[name = tensor("op_2226_cast_fp16")]; + tensor var_2227 = const()[name = tensor("op_2227"), val = tensor([1, 20, 64, -1])]; + tensor var_2228_cast_fp16 = reshape(shape = var_2227, x = k_37_cast_fp16)[name = tensor("op_2228_cast_fp16")]; + tensor var_2229 = const()[name = tensor("op_2229"), val = tensor([1, 20, 64, -1])]; + tensor var_2230_cast_fp16 = reshape(shape = var_2229, x = v_37_cast_fp16)[name = tensor("op_2230_cast_fp16")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2226_cast_fp16, y = var_2228_cast_fp16)[name = tensor("attn_weights_73_cast_fp16")]; + tensor attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_75_cast_fp16")]; + tensor var_2234_cast_fp16 = softmax(axis = var_1180, x = attn_weights_75_cast_fp16)[name = tensor("op_2234_cast_fp16")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2230_cast_fp16, y = var_2234_cast_fp16)[name = tensor("attn_37_cast_fp16")]; + tensor var_2238 = const()[name = tensor("op_2238"), val = tensor([1, 1280, 1, -1])]; + tensor input_175_cast_fp16 = reshape(shape = var_2238, x = attn_37_cast_fp16)[name = tensor("input_175_cast_fp16")]; + tensor var_2243 = const()[name = tensor("op_2243"), val = tensor([1, 1])]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1, 1])]; + tensor var_2247_pad_type_0 = const()[name = tensor("op_2247_pad_type_0"), val = tensor("custom")]; + tensor var_2247_pad_0 = const()[name = tensor("op_2247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208530880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209759744))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209759936)))]; + tensor var_2247_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_2245, groups = var_1196, pad = var_2247_pad_0, pad_type = var_2247_pad_type_0, strides = var_2243, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = tensor("op_2247_cast_fp16")]; + tensor inputs_57_cast_fp16 = add(x = var_2247_cast_fp16, y = inputs_55_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; + tensor hidden_states_97_axes_0 = const()[name = tensor("hidden_states_97_axes_0"), val = tensor([1])]; + tensor hidden_states_97_gamma_0_to_fp16 = const()[name = tensor("hidden_states_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209762560)))]; + tensor hidden_states_97_beta_0_to_fp16 = const()[name = tensor("hidden_states_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209765184)))]; + tensor var_2257_to_fp16 = const()[name = tensor("op_2257_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_97_cast_fp16 = layer_norm(axes = hidden_states_97_axes_0, beta = hidden_states_97_beta_0_to_fp16, epsilon = var_2257_to_fp16, gamma = hidden_states_97_gamma_0_to_fp16, x = inputs_57_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor var_2274 = const()[name = tensor("op_2274"), 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 down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209767808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210996672))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_39_cast_fp16 = conv(dilations = var_2274, groups = var_1196, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_2272, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_97_cast_fp16)[name = tensor("q_39_cast_fp16")]; + tensor var_2278 = const()[name = tensor("op_2278"), val = tensor([1, 1])]; + tensor var_2280 = const()[name = tensor("op_2280"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210996864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212963008))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_39_cast_fp16 = conv(dilations = var_2280, groups = var_1196, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_2278, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_39_cast_fp16")]; + tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([1, 1])]; + tensor var_2286 = const()[name = tensor("op_2286"), 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 down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212963200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214929344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_39_cast_fp16 = conv(dilations = var_2286, groups = var_1196, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_2284, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_39_cast_fp16")]; + tensor var_2290 = const()[name = tensor("op_2290"), val = tensor([1, 20, 64, -1])]; + tensor var_2291_cast_fp16 = reshape(shape = var_2290, x = q_39_cast_fp16)[name = tensor("op_2291_cast_fp16")]; + tensor var_2292 = const()[name = tensor("op_2292"), val = tensor([1, 20, 64, -1])]; + tensor var_2293_cast_fp16 = reshape(shape = var_2292, x = k_39_cast_fp16)[name = tensor("op_2293_cast_fp16")]; + tensor var_2294 = const()[name = tensor("op_2294"), val = tensor([1, 20, 64, -1])]; + tensor var_2295_cast_fp16 = reshape(shape = var_2294, x = v_39_cast_fp16)[name = tensor("op_2295_cast_fp16")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast_fp16 = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_2291_cast_fp16, y = var_2293_cast_fp16)[name = tensor("attn_weights_77_cast_fp16")]; + tensor attn_weights_79_cast_fp16 = mul(x = attn_weights_77_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_79_cast_fp16")]; + tensor var_2299_cast_fp16 = softmax(axis = var_1180, x = attn_weights_79_cast_fp16)[name = tensor("op_2299_cast_fp16")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2295_cast_fp16, y = var_2299_cast_fp16)[name = tensor("attn_39_cast_fp16")]; + tensor var_2303 = const()[name = tensor("op_2303"), val = tensor([1, 1280, 1, -1])]; + tensor input_177_cast_fp16 = reshape(shape = var_2303, x = attn_39_cast_fp16)[name = tensor("input_177_cast_fp16")]; + tensor var_2308 = const()[name = tensor("op_2308"), val = tensor([1, 1])]; + tensor var_2310 = const()[name = tensor("op_2310"), val = tensor([1, 1])]; + tensor var_2312_pad_type_0 = const()[name = tensor("op_2312_pad_type_0"), val = tensor("custom")]; + tensor var_2312_pad_0 = const()[name = tensor("op_2312_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214929536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216158400))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216158592)))]; + tensor var_2312_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_2310, groups = var_1196, pad = var_2312_pad_0, pad_type = var_2312_pad_type_0, strides = var_2308, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = tensor("op_2312_cast_fp16")]; + tensor inputs_59_cast_fp16 = add(x = var_2312_cast_fp16, y = inputs_57_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; + tensor input_179_axes_0 = const()[name = tensor("input_179_axes_0"), val = tensor([1])]; + tensor input_179_gamma_0_to_fp16 = const()[name = tensor("input_179_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216161216)))]; + tensor input_179_beta_0_to_fp16 = const()[name = tensor("input_179_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216163840)))]; + tensor var_2322_to_fp16 = const()[name = tensor("op_2322_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_179_cast_fp16 = layer_norm(axes = input_179_axes_0, beta = input_179_beta_0_to_fp16, epsilon = var_2322_to_fp16, gamma = input_179_gamma_0_to_fp16, x = inputs_59_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor var_2338 = const()[name = tensor("op_2338"), val = tensor([1, 1])]; + tensor var_2340 = const()[name = tensor("op_2340"), val = tensor([1, 1])]; + tensor var_2342_pad_type_0 = const()[name = tensor("op_2342_pad_type_0"), val = tensor("custom")]; + tensor var_2342_pad_0 = const()[name = tensor("op_2342_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216166464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(225996928))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(225997120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226004864))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2342_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2340, groups = var_1196, pad = var_2342_pad_0, pad_type = var_2342_pad_type_0, strides = var_2338, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_179_cast_fp16)[name = tensor("op_2342_cast_fp16")]; + tensor var_2343_split_sizes_0 = const()[name = tensor("op_2343_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2343_axis_0 = const()[name = tensor("op_2343_axis_0"), val = tensor(1)]; + tensor var_2343_cast_fp16_0, tensor var_2343_cast_fp16_1 = split(axis = var_2343_axis_0, split_sizes = var_2343_split_sizes_0, x = var_2342_cast_fp16)[name = tensor("op_2343_cast_fp16")]; + tensor var_2345_mode_0 = const()[name = tensor("op_2345_mode_0"), val = tensor("EXACT")]; + tensor var_2345_cast_fp16 = gelu(mode = var_2345_mode_0, x = var_2343_cast_fp16_1)[name = tensor("op_2345_cast_fp16")]; + tensor input_181_cast_fp16 = mul(x = var_2343_cast_fp16_0, y = var_2345_cast_fp16)[name = tensor("input_181_cast_fp16")]; + tensor var_2349 = const()[name = tensor("op_2349"), val = tensor([1, 1])]; + tensor var_2351 = const()[name = tensor("op_2351"), val = tensor([1, 1])]; + tensor var_2353_pad_type_0 = const()[name = tensor("op_2353_pad_type_0"), val = tensor("custom")]; + tensor var_2353_pad_0 = const()[name = tensor("op_2353_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226005056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230920320))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230920512)))]; + tensor var_2353_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_2351, groups = var_1196, pad = var_2353_pad_0, pad_type = var_2353_pad_type_0, strides = var_2349, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = tensor("op_2353_cast_fp16")]; + tensor inputs_61_cast_fp16 = add(x = var_2353_cast_fp16, y = inputs_59_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; + tensor hidden_states_101_axes_0 = const()[name = tensor("hidden_states_101_axes_0"), val = tensor([1])]; + tensor hidden_states_101_gamma_0_to_fp16 = const()[name = tensor("hidden_states_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230923136)))]; + tensor hidden_states_101_beta_0_to_fp16 = const()[name = tensor("hidden_states_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230925760)))]; + tensor var_2369_to_fp16 = const()[name = tensor("op_2369_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_101_cast_fp16 = layer_norm(axes = hidden_states_101_axes_0, beta = hidden_states_101_beta_0_to_fp16, epsilon = var_2369_to_fp16, gamma = hidden_states_101_gamma_0_to_fp16, x = inputs_61_cast_fp16)[name = tensor("hidden_states_101_cast_fp16")]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor var_2386 = const()[name = tensor("op_2386"), 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 down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230928384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232157248))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_41_cast_fp16 = conv(dilations = var_2386, groups = var_1196, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_2384, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_101_cast_fp16)[name = tensor("q_41_cast_fp16")]; + tensor var_2390 = const()[name = tensor("op_2390"), val = tensor([1, 1])]; + tensor var_2392 = const()[name = tensor("op_2392"), 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_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232157440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233386304))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_41_cast_fp16 = conv(dilations = var_2392, groups = var_1196, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2390, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_101_cast_fp16)[name = tensor("k_41_cast_fp16")]; + tensor var_2396 = const()[name = tensor("op_2396"), val = tensor([1, 1])]; + tensor var_2398 = const()[name = tensor("op_2398"), 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 down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233386496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234615360))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_41_cast_fp16 = conv(dilations = var_2398, groups = var_1196, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_2396, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_101_cast_fp16)[name = tensor("v_41_cast_fp16")]; + tensor var_2402 = const()[name = tensor("op_2402"), val = tensor([1, 20, 64, -1])]; + tensor var_2403_cast_fp16 = reshape(shape = var_2402, x = q_41_cast_fp16)[name = tensor("op_2403_cast_fp16")]; + tensor var_2404 = const()[name = tensor("op_2404"), val = tensor([1, 20, 64, -1])]; + tensor var_2405_cast_fp16 = reshape(shape = var_2404, x = k_41_cast_fp16)[name = tensor("op_2405_cast_fp16")]; + tensor var_2406 = const()[name = tensor("op_2406"), val = tensor([1, 20, 64, -1])]; + tensor var_2407_cast_fp16 = reshape(shape = var_2406, x = v_41_cast_fp16)[name = tensor("op_2407_cast_fp16")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2403_cast_fp16, y = var_2405_cast_fp16)[name = tensor("attn_weights_81_cast_fp16")]; + tensor attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_83_cast_fp16")]; + tensor var_2411_cast_fp16 = softmax(axis = var_1180, x = attn_weights_83_cast_fp16)[name = tensor("op_2411_cast_fp16")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2407_cast_fp16, y = var_2411_cast_fp16)[name = tensor("attn_41_cast_fp16")]; + tensor var_2415 = const()[name = tensor("op_2415"), val = tensor([1, 1280, 1, -1])]; + tensor input_183_cast_fp16 = reshape(shape = var_2415, x = attn_41_cast_fp16)[name = tensor("input_183_cast_fp16")]; + tensor var_2420 = const()[name = tensor("op_2420"), val = tensor([1, 1])]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1, 1])]; + tensor var_2424_pad_type_0 = const()[name = tensor("op_2424_pad_type_0"), val = tensor("custom")]; + tensor var_2424_pad_0 = const()[name = tensor("op_2424_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234615552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235844416))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235844608)))]; + tensor var_2424_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_2422, groups = var_1196, pad = var_2424_pad_0, pad_type = var_2424_pad_type_0, strides = var_2420, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = tensor("op_2424_cast_fp16")]; + tensor inputs_63_cast_fp16 = add(x = var_2424_cast_fp16, y = inputs_61_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; + tensor hidden_states_103_axes_0 = const()[name = tensor("hidden_states_103_axes_0"), val = tensor([1])]; + tensor hidden_states_103_gamma_0_to_fp16 = const()[name = tensor("hidden_states_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235847232)))]; + tensor hidden_states_103_beta_0_to_fp16 = const()[name = tensor("hidden_states_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235849856)))]; + tensor var_2434_to_fp16 = const()[name = tensor("op_2434_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_103_cast_fp16 = layer_norm(axes = hidden_states_103_axes_0, beta = hidden_states_103_beta_0_to_fp16, epsilon = var_2434_to_fp16, gamma = hidden_states_103_gamma_0_to_fp16, x = inputs_63_cast_fp16)[name = tensor("hidden_states_103_cast_fp16")]; + tensor var_2449 = const()[name = tensor("op_2449"), val = tensor([1, 1])]; + tensor var_2451 = const()[name = tensor("op_2451"), 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 down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235852480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237081344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_43_cast_fp16 = conv(dilations = var_2451, groups = var_1196, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_2449, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_103_cast_fp16)[name = tensor("q_43_cast_fp16")]; + tensor var_2455 = const()[name = tensor("op_2455"), val = tensor([1, 1])]; + tensor var_2457 = const()[name = tensor("op_2457"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237081536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239047680))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_43_cast_fp16 = conv(dilations = var_2457, groups = var_1196, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_2455, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_43_cast_fp16")]; + tensor var_2461 = const()[name = tensor("op_2461"), val = tensor([1, 1])]; + tensor var_2463 = const()[name = tensor("op_2463"), 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 down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239047872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241014016))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_43_cast_fp16 = conv(dilations = var_2463, groups = var_1196, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_2461, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_43_cast_fp16")]; + tensor var_2467 = const()[name = tensor("op_2467"), val = tensor([1, 20, 64, -1])]; + tensor var_2468_cast_fp16 = reshape(shape = var_2467, x = q_43_cast_fp16)[name = tensor("op_2468_cast_fp16")]; + tensor var_2469 = const()[name = tensor("op_2469"), val = tensor([1, 20, 64, -1])]; + tensor var_2470_cast_fp16 = reshape(shape = var_2469, x = k_43_cast_fp16)[name = tensor("op_2470_cast_fp16")]; + tensor var_2471 = const()[name = tensor("op_2471"), val = tensor([1, 20, 64, -1])]; + tensor var_2472_cast_fp16 = reshape(shape = var_2471, x = v_43_cast_fp16)[name = tensor("op_2472_cast_fp16")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast_fp16 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_2468_cast_fp16, y = var_2470_cast_fp16)[name = tensor("attn_weights_85_cast_fp16")]; + tensor attn_weights_87_cast_fp16 = mul(x = attn_weights_85_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_87_cast_fp16")]; + tensor var_2476_cast_fp16 = softmax(axis = var_1180, x = attn_weights_87_cast_fp16)[name = tensor("op_2476_cast_fp16")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2472_cast_fp16, y = var_2476_cast_fp16)[name = tensor("attn_43_cast_fp16")]; + tensor var_2480 = const()[name = tensor("op_2480"), val = tensor([1, 1280, 1, -1])]; + tensor input_185_cast_fp16 = reshape(shape = var_2480, x = attn_43_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor var_2485 = const()[name = tensor("op_2485"), val = tensor([1, 1])]; + tensor var_2487 = const()[name = tensor("op_2487"), val = tensor([1, 1])]; + tensor var_2489_pad_type_0 = const()[name = tensor("op_2489_pad_type_0"), val = tensor("custom")]; + tensor var_2489_pad_0 = const()[name = tensor("op_2489_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241014208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242243072))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242243264)))]; + tensor var_2489_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_2487, groups = var_1196, pad = var_2489_pad_0, pad_type = var_2489_pad_type_0, strides = var_2485, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = tensor("op_2489_cast_fp16")]; + tensor inputs_65_cast_fp16 = add(x = var_2489_cast_fp16, y = inputs_63_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; + tensor input_187_axes_0 = const()[name = tensor("input_187_axes_0"), val = tensor([1])]; + tensor input_187_gamma_0_to_fp16 = const()[name = tensor("input_187_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242245888)))]; + tensor input_187_beta_0_to_fp16 = const()[name = tensor("input_187_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242248512)))]; + tensor var_2499_to_fp16 = const()[name = tensor("op_2499_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_187_cast_fp16 = layer_norm(axes = input_187_axes_0, beta = input_187_beta_0_to_fp16, epsilon = var_2499_to_fp16, gamma = input_187_gamma_0_to_fp16, x = inputs_65_cast_fp16)[name = tensor("input_187_cast_fp16")]; + tensor var_2515 = const()[name = tensor("op_2515"), val = tensor([1, 1])]; + tensor var_2517 = const()[name = tensor("op_2517"), val = tensor([1, 1])]; + tensor var_2519_pad_type_0 = const()[name = tensor("op_2519_pad_type_0"), val = tensor("custom")]; + tensor var_2519_pad_0 = const()[name = tensor("op_2519_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242251136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252081600))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252081792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252089536))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2519_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2517, groups = var_1196, pad = var_2519_pad_0, pad_type = var_2519_pad_type_0, strides = var_2515, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_187_cast_fp16)[name = tensor("op_2519_cast_fp16")]; + tensor var_2520_split_sizes_0 = const()[name = tensor("op_2520_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2520_axis_0 = const()[name = tensor("op_2520_axis_0"), val = tensor(1)]; + tensor var_2520_cast_fp16_0, tensor var_2520_cast_fp16_1 = split(axis = var_2520_axis_0, split_sizes = var_2520_split_sizes_0, x = var_2519_cast_fp16)[name = tensor("op_2520_cast_fp16")]; + tensor var_2522_mode_0 = const()[name = tensor("op_2522_mode_0"), val = tensor("EXACT")]; + tensor var_2522_cast_fp16 = gelu(mode = var_2522_mode_0, x = var_2520_cast_fp16_1)[name = tensor("op_2522_cast_fp16")]; + tensor input_189_cast_fp16 = mul(x = var_2520_cast_fp16_0, y = var_2522_cast_fp16)[name = tensor("input_189_cast_fp16")]; + tensor var_2526 = const()[name = tensor("op_2526"), val = tensor([1, 1])]; + tensor var_2528 = const()[name = tensor("op_2528"), val = tensor([1, 1])]; + tensor var_2530_pad_type_0 = const()[name = tensor("op_2530_pad_type_0"), val = tensor("custom")]; + tensor var_2530_pad_0 = const()[name = tensor("op_2530_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252089728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257004992))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257005184)))]; + tensor var_2530_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_2528, groups = var_1196, pad = var_2530_pad_0, pad_type = var_2530_pad_type_0, strides = var_2526, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = tensor("op_2530_cast_fp16")]; + tensor inputs_67_cast_fp16 = add(x = var_2530_cast_fp16, y = inputs_65_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; + tensor hidden_states_107_axes_0 = const()[name = tensor("hidden_states_107_axes_0"), val = tensor([1])]; + tensor hidden_states_107_gamma_0_to_fp16 = const()[name = tensor("hidden_states_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257007808)))]; + tensor hidden_states_107_beta_0_to_fp16 = const()[name = tensor("hidden_states_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257010432)))]; + tensor var_2546_to_fp16 = const()[name = tensor("op_2546_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_107_cast_fp16 = layer_norm(axes = hidden_states_107_axes_0, beta = hidden_states_107_beta_0_to_fp16, epsilon = var_2546_to_fp16, gamma = hidden_states_107_gamma_0_to_fp16, x = inputs_67_cast_fp16)[name = tensor("hidden_states_107_cast_fp16")]; + tensor var_2561 = const()[name = tensor("op_2561"), val = tensor([1, 1])]; + tensor var_2563 = const()[name = tensor("op_2563"), 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 down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257013056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258241920))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_45_cast_fp16 = conv(dilations = var_2563, groups = var_1196, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_2561, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_107_cast_fp16)[name = tensor("q_45_cast_fp16")]; + tensor var_2567 = const()[name = tensor("op_2567"), val = tensor([1, 1])]; + tensor var_2569 = const()[name = tensor("op_2569"), 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_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258242112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259470976))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_45_cast_fp16 = conv(dilations = var_2569, groups = var_1196, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_2567, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_107_cast_fp16)[name = tensor("k_45_cast_fp16")]; + tensor var_2573 = const()[name = tensor("op_2573"), val = tensor([1, 1])]; + tensor var_2575 = const()[name = tensor("op_2575"), 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 down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259471168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260700032))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_45_cast_fp16 = conv(dilations = var_2575, groups = var_1196, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_2573, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_107_cast_fp16)[name = tensor("v_45_cast_fp16")]; + tensor var_2579 = const()[name = tensor("op_2579"), val = tensor([1, 20, 64, -1])]; + tensor var_2580_cast_fp16 = reshape(shape = var_2579, x = q_45_cast_fp16)[name = tensor("op_2580_cast_fp16")]; + tensor var_2581 = const()[name = tensor("op_2581"), val = tensor([1, 20, 64, -1])]; + tensor var_2582_cast_fp16 = reshape(shape = var_2581, x = k_45_cast_fp16)[name = tensor("op_2582_cast_fp16")]; + tensor var_2583 = const()[name = tensor("op_2583"), val = tensor([1, 20, 64, -1])]; + tensor var_2584_cast_fp16 = reshape(shape = var_2583, x = v_45_cast_fp16)[name = tensor("op_2584_cast_fp16")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2580_cast_fp16, y = var_2582_cast_fp16)[name = tensor("attn_weights_89_cast_fp16")]; + tensor attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_91_cast_fp16")]; + tensor var_2588_cast_fp16 = softmax(axis = var_1180, x = attn_weights_91_cast_fp16)[name = tensor("op_2588_cast_fp16")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2584_cast_fp16, y = var_2588_cast_fp16)[name = tensor("attn_45_cast_fp16")]; + tensor var_2592 = const()[name = tensor("op_2592"), val = tensor([1, 1280, 1, -1])]; + tensor input_191_cast_fp16 = reshape(shape = var_2592, x = attn_45_cast_fp16)[name = tensor("input_191_cast_fp16")]; + tensor var_2597 = const()[name = tensor("op_2597"), val = tensor([1, 1])]; + tensor var_2599 = const()[name = tensor("op_2599"), val = tensor([1, 1])]; + tensor var_2601_pad_type_0 = const()[name = tensor("op_2601_pad_type_0"), val = tensor("custom")]; + tensor var_2601_pad_0 = const()[name = tensor("op_2601_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260700224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261929088))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261929280)))]; + tensor var_2601_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_2599, groups = var_1196, pad = var_2601_pad_0, pad_type = var_2601_pad_type_0, strides = var_2597, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_191_cast_fp16)[name = tensor("op_2601_cast_fp16")]; + tensor inputs_69_cast_fp16 = add(x = var_2601_cast_fp16, y = inputs_67_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; + tensor hidden_states_109_axes_0 = const()[name = tensor("hidden_states_109_axes_0"), val = tensor([1])]; + tensor hidden_states_109_gamma_0_to_fp16 = const()[name = tensor("hidden_states_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261931904)))]; + tensor hidden_states_109_beta_0_to_fp16 = const()[name = tensor("hidden_states_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261934528)))]; + tensor var_2611_to_fp16 = const()[name = tensor("op_2611_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_109_cast_fp16 = layer_norm(axes = hidden_states_109_axes_0, beta = hidden_states_109_beta_0_to_fp16, epsilon = var_2611_to_fp16, gamma = hidden_states_109_gamma_0_to_fp16, x = inputs_69_cast_fp16)[name = tensor("hidden_states_109_cast_fp16")]; + tensor var_2626 = const()[name = tensor("op_2626"), val = tensor([1, 1])]; + tensor var_2628 = const()[name = tensor("op_2628"), 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 down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261937152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263166016))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_47_cast_fp16 = conv(dilations = var_2628, groups = var_1196, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_2626, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_109_cast_fp16)[name = tensor("q_47_cast_fp16")]; + tensor var_2632 = const()[name = tensor("op_2632"), val = tensor([1, 1])]; + tensor var_2634 = const()[name = tensor("op_2634"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263166208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265132352))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_47_cast_fp16 = conv(dilations = var_2634, groups = var_1196, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_2632, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_47_cast_fp16")]; + tensor var_2638 = const()[name = tensor("op_2638"), val = tensor([1, 1])]; + tensor var_2640 = const()[name = tensor("op_2640"), 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 down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265132544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267098688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_47_cast_fp16 = conv(dilations = var_2640, groups = var_1196, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_2638, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_47_cast_fp16")]; + tensor var_2644 = const()[name = tensor("op_2644"), val = tensor([1, 20, 64, -1])]; + tensor var_2645_cast_fp16 = reshape(shape = var_2644, x = q_47_cast_fp16)[name = tensor("op_2645_cast_fp16")]; + tensor var_2646 = const()[name = tensor("op_2646"), val = tensor([1, 20, 64, -1])]; + tensor var_2647_cast_fp16 = reshape(shape = var_2646, x = k_47_cast_fp16)[name = tensor("op_2647_cast_fp16")]; + tensor var_2648 = const()[name = tensor("op_2648"), val = tensor([1, 20, 64, -1])]; + tensor var_2649_cast_fp16 = reshape(shape = var_2648, x = v_47_cast_fp16)[name = tensor("op_2649_cast_fp16")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast_fp16 = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_2645_cast_fp16, y = var_2647_cast_fp16)[name = tensor("attn_weights_93_cast_fp16")]; + tensor attn_weights_95_cast_fp16 = mul(x = attn_weights_93_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_95_cast_fp16")]; + tensor var_2653_cast_fp16 = softmax(axis = var_1180, x = attn_weights_95_cast_fp16)[name = tensor("op_2653_cast_fp16")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2649_cast_fp16, y = var_2653_cast_fp16)[name = tensor("attn_47_cast_fp16")]; + tensor var_2657 = const()[name = tensor("op_2657"), val = tensor([1, 1280, 1, -1])]; + tensor input_193_cast_fp16 = reshape(shape = var_2657, x = attn_47_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor var_2662 = const()[name = tensor("op_2662"), val = tensor([1, 1])]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1, 1])]; + tensor var_2666_pad_type_0 = const()[name = tensor("op_2666_pad_type_0"), val = tensor("custom")]; + tensor var_2666_pad_0 = const()[name = tensor("op_2666_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267098880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268327744))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268327936)))]; + tensor var_2666_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_2664, groups = var_1196, pad = var_2666_pad_0, pad_type = var_2666_pad_type_0, strides = var_2662, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = tensor("op_2666_cast_fp16")]; + tensor inputs_71_cast_fp16 = add(x = var_2666_cast_fp16, y = inputs_69_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; + tensor input_195_axes_0 = const()[name = tensor("input_195_axes_0"), val = tensor([1])]; + tensor input_195_gamma_0_to_fp16 = const()[name = tensor("input_195_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268330560)))]; + tensor input_195_beta_0_to_fp16 = const()[name = tensor("input_195_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268333184)))]; + tensor var_2676_to_fp16 = const()[name = tensor("op_2676_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_195_cast_fp16 = layer_norm(axes = input_195_axes_0, beta = input_195_beta_0_to_fp16, epsilon = var_2676_to_fp16, gamma = input_195_gamma_0_to_fp16, x = inputs_71_cast_fp16)[name = tensor("input_195_cast_fp16")]; + tensor var_2692 = const()[name = tensor("op_2692"), val = tensor([1, 1])]; + tensor var_2694 = const()[name = tensor("op_2694"), val = tensor([1, 1])]; + tensor var_2696_pad_type_0 = const()[name = tensor("op_2696_pad_type_0"), val = tensor("custom")]; + tensor var_2696_pad_0 = const()[name = tensor("op_2696_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268335808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278166272))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278166464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278174208))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2696_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2694, groups = var_1196, pad = var_2696_pad_0, pad_type = var_2696_pad_type_0, strides = var_2692, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = tensor("op_2696_cast_fp16")]; + tensor var_2697_split_sizes_0 = const()[name = tensor("op_2697_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2697_axis_0 = const()[name = tensor("op_2697_axis_0"), val = tensor(1)]; + tensor var_2697_cast_fp16_0, tensor var_2697_cast_fp16_1 = split(axis = var_2697_axis_0, split_sizes = var_2697_split_sizes_0, x = var_2696_cast_fp16)[name = tensor("op_2697_cast_fp16")]; + tensor var_2699_mode_0 = const()[name = tensor("op_2699_mode_0"), val = tensor("EXACT")]; + tensor var_2699_cast_fp16 = gelu(mode = var_2699_mode_0, x = var_2697_cast_fp16_1)[name = tensor("op_2699_cast_fp16")]; + tensor input_197_cast_fp16 = mul(x = var_2697_cast_fp16_0, y = var_2699_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor var_2703 = const()[name = tensor("op_2703"), val = tensor([1, 1])]; + tensor var_2705 = const()[name = tensor("op_2705"), val = tensor([1, 1])]; + tensor var_2707_pad_type_0 = const()[name = tensor("op_2707_pad_type_0"), val = tensor("custom")]; + tensor var_2707_pad_0 = const()[name = tensor("op_2707_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278174400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283089664))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283089856)))]; + tensor var_2707_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_2705, groups = var_1196, pad = var_2707_pad_0, pad_type = var_2707_pad_type_0, strides = var_2703, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor("op_2707_cast_fp16")]; + tensor inputs_73_cast_fp16 = add(x = var_2707_cast_fp16, y = inputs_71_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; + tensor hidden_states_113_axes_0 = const()[name = tensor("hidden_states_113_axes_0"), val = tensor([1])]; + tensor hidden_states_113_gamma_0_to_fp16 = const()[name = tensor("hidden_states_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283092480)))]; + tensor hidden_states_113_beta_0_to_fp16 = const()[name = tensor("hidden_states_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283095104)))]; + tensor var_2723_to_fp16 = const()[name = tensor("op_2723_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_113_cast_fp16 = layer_norm(axes = hidden_states_113_axes_0, beta = hidden_states_113_beta_0_to_fp16, epsilon = var_2723_to_fp16, gamma = hidden_states_113_gamma_0_to_fp16, x = inputs_73_cast_fp16)[name = tensor("hidden_states_113_cast_fp16")]; + tensor var_2738 = const()[name = tensor("op_2738"), val = tensor([1, 1])]; + tensor var_2740 = const()[name = tensor("op_2740"), 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 down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283097728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284326592))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_49_cast_fp16 = conv(dilations = var_2740, groups = var_1196, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_2738, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_113_cast_fp16)[name = tensor("q_49_cast_fp16")]; + tensor var_2744 = const()[name = tensor("op_2744"), val = tensor([1, 1])]; + tensor var_2746 = const()[name = tensor("op_2746"), 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 down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284326784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285555648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_49_cast_fp16 = conv(dilations = var_2746, groups = var_1196, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_2744, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_113_cast_fp16)[name = tensor("k_49_cast_fp16")]; + tensor var_2750 = const()[name = tensor("op_2750"), val = tensor([1, 1])]; + tensor var_2752 = const()[name = tensor("op_2752"), 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 down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285555840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286784704))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_49_cast_fp16 = conv(dilations = var_2752, groups = var_1196, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_2750, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_113_cast_fp16)[name = tensor("v_49_cast_fp16")]; + tensor var_2756 = const()[name = tensor("op_2756"), val = tensor([1, 20, 64, -1])]; + tensor var_2757_cast_fp16 = reshape(shape = var_2756, x = q_49_cast_fp16)[name = tensor("op_2757_cast_fp16")]; + tensor var_2758 = const()[name = tensor("op_2758"), val = tensor([1, 20, 64, -1])]; + tensor var_2759_cast_fp16 = reshape(shape = var_2758, x = k_49_cast_fp16)[name = tensor("op_2759_cast_fp16")]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([1, 20, 64, -1])]; + tensor var_2761_cast_fp16 = reshape(shape = var_2760, x = v_49_cast_fp16)[name = tensor("op_2761_cast_fp16")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_2757_cast_fp16, y = var_2759_cast_fp16)[name = tensor("attn_weights_97_cast_fp16")]; + tensor attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_99_cast_fp16")]; + tensor var_2765_cast_fp16 = softmax(axis = var_1180, x = attn_weights_99_cast_fp16)[name = tensor("op_2765_cast_fp16")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2761_cast_fp16, y = var_2765_cast_fp16)[name = tensor("attn_49_cast_fp16")]; + tensor var_2769 = const()[name = tensor("op_2769"), val = tensor([1, 1280, 1, -1])]; + tensor input_199_cast_fp16 = reshape(shape = var_2769, x = attn_49_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor var_2774 = const()[name = tensor("op_2774"), val = tensor([1, 1])]; + tensor var_2776 = const()[name = tensor("op_2776"), val = tensor([1, 1])]; + tensor var_2778_pad_type_0 = const()[name = tensor("op_2778_pad_type_0"), val = tensor("custom")]; + tensor var_2778_pad_0 = const()[name = tensor("op_2778_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286784896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288013760))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288013952)))]; + tensor var_2778_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_2776, groups = var_1196, pad = var_2778_pad_0, pad_type = var_2778_pad_type_0, strides = var_2774, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = tensor("op_2778_cast_fp16")]; + tensor inputs_75_cast_fp16 = add(x = var_2778_cast_fp16, y = inputs_73_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; + tensor hidden_states_115_axes_0 = const()[name = tensor("hidden_states_115_axes_0"), val = tensor([1])]; + tensor hidden_states_115_gamma_0_to_fp16 = const()[name = tensor("hidden_states_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288016576)))]; + tensor hidden_states_115_beta_0_to_fp16 = const()[name = tensor("hidden_states_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288019200)))]; + tensor var_2788_to_fp16 = const()[name = tensor("op_2788_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_115_cast_fp16 = layer_norm(axes = hidden_states_115_axes_0, beta = hidden_states_115_beta_0_to_fp16, epsilon = var_2788_to_fp16, gamma = hidden_states_115_gamma_0_to_fp16, x = inputs_75_cast_fp16)[name = tensor("hidden_states_115_cast_fp16")]; + tensor var_2803 = const()[name = tensor("op_2803"), val = tensor([1, 1])]; + tensor var_2805 = const()[name = tensor("op_2805"), 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 down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288021824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289250688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_51_cast_fp16 = conv(dilations = var_2805, groups = var_1196, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_2803, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_115_cast_fp16)[name = tensor("q_51_cast_fp16")]; + tensor var_2809 = const()[name = tensor("op_2809"), val = tensor([1, 1])]; + tensor var_2811 = const()[name = tensor("op_2811"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289250880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291217024))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_51_cast_fp16 = conv(dilations = var_2811, groups = var_1196, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_2809, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_51_cast_fp16")]; + tensor var_2815 = const()[name = tensor("op_2815"), val = tensor([1, 1])]; + tensor var_2817 = const()[name = tensor("op_2817"), 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 down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291217216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293183360))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_51_cast_fp16 = conv(dilations = var_2817, groups = var_1196, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_2815, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_51_cast_fp16")]; + tensor var_2821 = const()[name = tensor("op_2821"), val = tensor([1, 20, 64, -1])]; + tensor var_2822_cast_fp16 = reshape(shape = var_2821, x = q_51_cast_fp16)[name = tensor("op_2822_cast_fp16")]; + tensor var_2823 = const()[name = tensor("op_2823"), val = tensor([1, 20, 64, -1])]; + tensor var_2824_cast_fp16 = reshape(shape = var_2823, x = k_51_cast_fp16)[name = tensor("op_2824_cast_fp16")]; + tensor var_2825 = const()[name = tensor("op_2825"), val = tensor([1, 20, 64, -1])]; + tensor var_2826_cast_fp16 = reshape(shape = var_2825, x = v_51_cast_fp16)[name = tensor("op_2826_cast_fp16")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast_fp16 = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_2822_cast_fp16, y = var_2824_cast_fp16)[name = tensor("attn_weights_101_cast_fp16")]; + tensor attn_weights_103_cast_fp16 = mul(x = attn_weights_101_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_103_cast_fp16")]; + tensor var_2830_cast_fp16 = softmax(axis = var_1180, x = attn_weights_103_cast_fp16)[name = tensor("op_2830_cast_fp16")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2826_cast_fp16, y = var_2830_cast_fp16)[name = tensor("attn_51_cast_fp16")]; + tensor var_2834 = const()[name = tensor("op_2834"), val = tensor([1, 1280, 1, -1])]; + tensor input_201_cast_fp16 = reshape(shape = var_2834, x = attn_51_cast_fp16)[name = tensor("input_201_cast_fp16")]; + tensor var_2839 = const()[name = tensor("op_2839"), val = tensor([1, 1])]; + tensor var_2841 = const()[name = tensor("op_2841"), val = tensor([1, 1])]; + tensor var_2843_pad_type_0 = const()[name = tensor("op_2843_pad_type_0"), val = tensor("custom")]; + tensor var_2843_pad_0 = const()[name = tensor("op_2843_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293183552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294412416))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294412608)))]; + tensor var_2843_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_2841, groups = var_1196, pad = var_2843_pad_0, pad_type = var_2843_pad_type_0, strides = var_2839, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = tensor("op_2843_cast_fp16")]; + tensor inputs_77_cast_fp16 = add(x = var_2843_cast_fp16, y = inputs_75_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; + tensor input_203_axes_0 = const()[name = tensor("input_203_axes_0"), val = tensor([1])]; + tensor input_203_gamma_0_to_fp16 = const()[name = tensor("input_203_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294415232)))]; + tensor input_203_beta_0_to_fp16 = const()[name = tensor("input_203_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294417856)))]; + tensor var_2853_to_fp16 = const()[name = tensor("op_2853_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_203_cast_fp16 = layer_norm(axes = input_203_axes_0, beta = input_203_beta_0_to_fp16, epsilon = var_2853_to_fp16, gamma = input_203_gamma_0_to_fp16, x = inputs_77_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor var_2869 = const()[name = tensor("op_2869"), val = tensor([1, 1])]; + tensor var_2871 = const()[name = tensor("op_2871"), val = tensor([1, 1])]; + tensor var_2873_pad_type_0 = const()[name = tensor("op_2873_pad_type_0"), val = tensor("custom")]; + tensor var_2873_pad_0 = const()[name = tensor("op_2873_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294420480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304250944))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304251136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304258880))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2873_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2871, groups = var_1196, pad = var_2873_pad_0, pad_type = var_2873_pad_type_0, strides = var_2869, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_203_cast_fp16)[name = tensor("op_2873_cast_fp16")]; + tensor var_2874_split_sizes_0 = const()[name = tensor("op_2874_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2874_axis_0 = const()[name = tensor("op_2874_axis_0"), val = tensor(1)]; + tensor var_2874_cast_fp16_0, tensor var_2874_cast_fp16_1 = split(axis = var_2874_axis_0, split_sizes = var_2874_split_sizes_0, x = var_2873_cast_fp16)[name = tensor("op_2874_cast_fp16")]; + tensor var_2876_mode_0 = const()[name = tensor("op_2876_mode_0"), val = tensor("EXACT")]; + tensor var_2876_cast_fp16 = gelu(mode = var_2876_mode_0, x = var_2874_cast_fp16_1)[name = tensor("op_2876_cast_fp16")]; + tensor input_205_cast_fp16 = mul(x = var_2874_cast_fp16_0, y = var_2876_cast_fp16)[name = tensor("input_205_cast_fp16")]; + tensor var_2880 = const()[name = tensor("op_2880"), val = tensor([1, 1])]; + tensor var_2882 = const()[name = tensor("op_2882"), val = tensor([1, 1])]; + tensor var_2884_pad_type_0 = const()[name = tensor("op_2884_pad_type_0"), val = tensor("custom")]; + tensor var_2884_pad_0 = const()[name = tensor("op_2884_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304259072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309174336))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309174528)))]; + tensor var_2884_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_2882, groups = var_1196, pad = var_2884_pad_0, pad_type = var_2884_pad_type_0, strides = var_2880, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = tensor("op_2884_cast_fp16")]; + tensor inputs_79_cast_fp16 = add(x = var_2884_cast_fp16, y = inputs_77_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; + tensor hidden_states_119_axes_0 = const()[name = tensor("hidden_states_119_axes_0"), val = tensor([1])]; + tensor hidden_states_119_gamma_0_to_fp16 = const()[name = tensor("hidden_states_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309177152)))]; + tensor hidden_states_119_beta_0_to_fp16 = const()[name = tensor("hidden_states_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309179776)))]; + tensor var_2900_to_fp16 = const()[name = tensor("op_2900_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_119_cast_fp16 = layer_norm(axes = hidden_states_119_axes_0, beta = hidden_states_119_beta_0_to_fp16, epsilon = var_2900_to_fp16, gamma = hidden_states_119_gamma_0_to_fp16, x = inputs_79_cast_fp16)[name = tensor("hidden_states_119_cast_fp16")]; + tensor var_2915 = const()[name = tensor("op_2915"), val = tensor([1, 1])]; + tensor var_2917 = const()[name = tensor("op_2917"), 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 down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309182400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310411264))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_53_cast_fp16 = conv(dilations = var_2917, groups = var_1196, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_2915, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_119_cast_fp16)[name = tensor("q_53_cast_fp16")]; + tensor var_2921 = const()[name = tensor("op_2921"), val = tensor([1, 1])]; + tensor var_2923 = const()[name = tensor("op_2923"), 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 down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310411456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311640320))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_53_cast_fp16 = conv(dilations = var_2923, groups = var_1196, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_2921, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_119_cast_fp16)[name = tensor("k_53_cast_fp16")]; + tensor var_2927 = const()[name = tensor("op_2927"), val = tensor([1, 1])]; + tensor var_2929 = const()[name = tensor("op_2929"), 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 down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311640512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312869376))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_53_cast_fp16 = conv(dilations = var_2929, groups = var_1196, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_2927, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_119_cast_fp16)[name = tensor("v_53_cast_fp16")]; + tensor var_2933 = const()[name = tensor("op_2933"), val = tensor([1, 20, 64, -1])]; + tensor var_2934_cast_fp16 = reshape(shape = var_2933, x = q_53_cast_fp16)[name = tensor("op_2934_cast_fp16")]; + tensor var_2935 = const()[name = tensor("op_2935"), val = tensor([1, 20, 64, -1])]; + tensor var_2936_cast_fp16 = reshape(shape = var_2935, x = k_53_cast_fp16)[name = tensor("op_2936_cast_fp16")]; + tensor var_2937 = const()[name = tensor("op_2937"), val = tensor([1, 20, 64, -1])]; + tensor var_2938_cast_fp16 = reshape(shape = var_2937, x = v_53_cast_fp16)[name = tensor("op_2938_cast_fp16")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_2934_cast_fp16, y = var_2936_cast_fp16)[name = tensor("attn_weights_105_cast_fp16")]; + tensor attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_107_cast_fp16")]; + tensor var_2942_cast_fp16 = softmax(axis = var_1180, x = attn_weights_107_cast_fp16)[name = tensor("op_2942_cast_fp16")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_2938_cast_fp16, y = var_2942_cast_fp16)[name = tensor("attn_53_cast_fp16")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([1, 1280, 1, -1])]; + tensor input_207_cast_fp16 = reshape(shape = var_2946, x = attn_53_cast_fp16)[name = tensor("input_207_cast_fp16")]; + tensor var_2951 = const()[name = tensor("op_2951"), val = tensor([1, 1])]; + tensor var_2953 = const()[name = tensor("op_2953"), val = tensor([1, 1])]; + tensor var_2955_pad_type_0 = const()[name = tensor("op_2955_pad_type_0"), val = tensor("custom")]; + tensor var_2955_pad_0 = const()[name = tensor("op_2955_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312869568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314098432))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314098624)))]; + tensor var_2955_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_2953, groups = var_1196, pad = var_2955_pad_0, pad_type = var_2955_pad_type_0, strides = var_2951, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_207_cast_fp16)[name = tensor("op_2955_cast_fp16")]; + tensor inputs_81_cast_fp16 = add(x = var_2955_cast_fp16, y = inputs_79_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; + tensor hidden_states_121_axes_0 = const()[name = tensor("hidden_states_121_axes_0"), val = tensor([1])]; + tensor hidden_states_121_gamma_0_to_fp16 = const()[name = tensor("hidden_states_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314101248)))]; + tensor hidden_states_121_beta_0_to_fp16 = const()[name = tensor("hidden_states_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314103872)))]; + tensor var_2965_to_fp16 = const()[name = tensor("op_2965_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_121_cast_fp16 = layer_norm(axes = hidden_states_121_axes_0, beta = hidden_states_121_beta_0_to_fp16, epsilon = var_2965_to_fp16, gamma = hidden_states_121_gamma_0_to_fp16, x = inputs_81_cast_fp16)[name = tensor("hidden_states_121_cast_fp16")]; + tensor var_2980 = const()[name = tensor("op_2980"), val = tensor([1, 1])]; + tensor var_2982 = const()[name = tensor("op_2982"), 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 down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314106496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315335360))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_55_cast_fp16 = conv(dilations = var_2982, groups = var_1196, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_2980, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_121_cast_fp16)[name = tensor("q_55_cast_fp16")]; + tensor var_2986 = const()[name = tensor("op_2986"), val = tensor([1, 1])]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315335552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317301696))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_55_cast_fp16 = conv(dilations = var_2988, groups = var_1196, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_2986, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_55_cast_fp16")]; + tensor var_2992 = const()[name = tensor("op_2992"), val = tensor([1, 1])]; + tensor var_2994 = const()[name = tensor("op_2994"), 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 down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317301888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319268032))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_55_cast_fp16 = conv(dilations = var_2994, groups = var_1196, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_2992, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_55_cast_fp16")]; + tensor var_2998 = const()[name = tensor("op_2998"), val = tensor([1, 20, 64, -1])]; + tensor var_2999_cast_fp16 = reshape(shape = var_2998, x = q_55_cast_fp16)[name = tensor("op_2999_cast_fp16")]; + tensor var_3000 = const()[name = tensor("op_3000"), val = tensor([1, 20, 64, -1])]; + tensor var_3001_cast_fp16 = reshape(shape = var_3000, x = k_55_cast_fp16)[name = tensor("op_3001_cast_fp16")]; + tensor var_3002 = const()[name = tensor("op_3002"), val = tensor([1, 20, 64, -1])]; + tensor var_3003_cast_fp16 = reshape(shape = var_3002, x = v_55_cast_fp16)[name = tensor("op_3003_cast_fp16")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast_fp16 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_2999_cast_fp16, y = var_3001_cast_fp16)[name = tensor("attn_weights_109_cast_fp16")]; + tensor attn_weights_111_cast_fp16 = mul(x = attn_weights_109_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_111_cast_fp16")]; + tensor var_3007_cast_fp16 = softmax(axis = var_1180, x = attn_weights_111_cast_fp16)[name = tensor("op_3007_cast_fp16")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_3003_cast_fp16, y = var_3007_cast_fp16)[name = tensor("attn_55_cast_fp16")]; + tensor var_3011 = const()[name = tensor("op_3011"), val = tensor([1, 1280, 1, -1])]; + tensor input_209_cast_fp16 = reshape(shape = var_3011, x = attn_55_cast_fp16)[name = tensor("input_209_cast_fp16")]; + tensor var_3016 = const()[name = tensor("op_3016"), val = tensor([1, 1])]; + tensor var_3018 = const()[name = tensor("op_3018"), val = tensor([1, 1])]; + tensor var_3020_pad_type_0 = const()[name = tensor("op_3020_pad_type_0"), val = tensor("custom")]; + tensor var_3020_pad_0 = const()[name = tensor("op_3020_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319268224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320497088))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320497280)))]; + tensor var_3020_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_3018, groups = var_1196, pad = var_3020_pad_0, pad_type = var_3020_pad_type_0, strides = var_3016, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_209_cast_fp16)[name = tensor("op_3020_cast_fp16")]; + tensor inputs_83_cast_fp16 = add(x = var_3020_cast_fp16, y = inputs_81_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; + tensor input_211_axes_0 = const()[name = tensor("input_211_axes_0"), val = tensor([1])]; + tensor input_211_gamma_0_to_fp16 = const()[name = tensor("input_211_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320499904)))]; + tensor input_211_beta_0_to_fp16 = const()[name = tensor("input_211_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320502528)))]; + tensor var_3030_to_fp16 = const()[name = tensor("op_3030_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_211_cast_fp16 = layer_norm(axes = input_211_axes_0, beta = input_211_beta_0_to_fp16, epsilon = var_3030_to_fp16, gamma = input_211_gamma_0_to_fp16, x = inputs_83_cast_fp16)[name = tensor("input_211_cast_fp16")]; + tensor var_3046 = const()[name = tensor("op_3046"), val = tensor([1, 1])]; + tensor var_3048 = const()[name = tensor("op_3048"), val = tensor([1, 1])]; + tensor var_3050_pad_type_0 = const()[name = tensor("op_3050_pad_type_0"), val = tensor("custom")]; + tensor var_3050_pad_0 = const()[name = tensor("op_3050_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320505152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330335616))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330335808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330343552))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3050_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3048, groups = var_1196, pad = var_3050_pad_0, pad_type = var_3050_pad_type_0, strides = var_3046, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_211_cast_fp16)[name = tensor("op_3050_cast_fp16")]; + tensor var_3051_split_sizes_0 = const()[name = tensor("op_3051_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3051_axis_0 = const()[name = tensor("op_3051_axis_0"), val = tensor(1)]; + tensor var_3051_cast_fp16_0, tensor var_3051_cast_fp16_1 = split(axis = var_3051_axis_0, split_sizes = var_3051_split_sizes_0, x = var_3050_cast_fp16)[name = tensor("op_3051_cast_fp16")]; + tensor var_3053_mode_0 = const()[name = tensor("op_3053_mode_0"), val = tensor("EXACT")]; + tensor var_3053_cast_fp16 = gelu(mode = var_3053_mode_0, x = var_3051_cast_fp16_1)[name = tensor("op_3053_cast_fp16")]; + tensor input_213_cast_fp16 = mul(x = var_3051_cast_fp16_0, y = var_3053_cast_fp16)[name = tensor("input_213_cast_fp16")]; + tensor var_3057 = const()[name = tensor("op_3057"), val = tensor([1, 1])]; + tensor var_3059 = const()[name = tensor("op_3059"), val = tensor([1, 1])]; + tensor var_3061_pad_type_0 = const()[name = tensor("op_3061_pad_type_0"), val = tensor("custom")]; + tensor var_3061_pad_0 = const()[name = tensor("op_3061_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330343744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335259008))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335259200)))]; + tensor var_3061_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_3059, groups = var_1196, pad = var_3061_pad_0, pad_type = var_3061_pad_type_0, strides = var_3057, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = tensor("op_3061_cast_fp16")]; + tensor hidden_states_125_cast_fp16 = add(x = var_3061_cast_fp16, y = inputs_83_cast_fp16)[name = tensor("hidden_states_125_cast_fp16")]; + tensor var_3063 = const()[name = tensor("op_3063"), val = tensor([1, 1280, 32, 32])]; + tensor input_215_cast_fp16 = reshape(shape = var_3063, x = hidden_states_125_cast_fp16)[name = tensor("input_215_cast_fp16")]; + tensor var_3067 = const()[name = tensor("op_3067"), val = tensor([1, 1])]; + tensor var_3069 = const()[name = tensor("op_3069"), val = tensor([1, 1])]; + tensor hidden_states_127_pad_type_0 = const()[name = tensor("hidden_states_127_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_127_pad_0 = const()[name = tensor("hidden_states_127_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(335261824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336490688))), 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(336490880)))]; + tensor hidden_states_127_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3069, groups = var_1196, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = var_3067, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_215_cast_fp16)[name = tensor("hidden_states_127_cast_fp16")]; + tensor input_217_cast_fp16 = add(x = hidden_states_127_cast_fp16, y = hidden_states_61_cast_fp16)[name = tensor("input_217_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_217_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + 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(336493504)))]; + 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(336496128)))]; + 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_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor input_221_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("input_221_cast_fp16")]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor var_3086 = const()[name = tensor("op_3086"), val = tensor([1, 1])]; + tensor hidden_states_129_pad_type_0 = const()[name = tensor("hidden_states_129_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_129_pad_0 = const()[name = tensor("hidden_states_129_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(336498752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(347558016))), 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(347558208)))]; + tensor hidden_states_129_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3086, groups = var_1196, pad = hidden_states_129_pad_0, pad_type = hidden_states_129_pad_type_0, strides = var_3084, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_221_cast_fp16)[name = tensor("hidden_states_129_cast_fp16")]; + tensor var_3092 = const()[name = tensor("op_3092"), val = tensor([1, 1])]; + tensor var_3094 = const()[name = tensor("op_3094"), 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(347560832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348789696))), 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(348789888)))]; + tensor temb_11_cast_fp16 = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3094, groups = var_1196, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_3092, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_11_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = temb_11_cast_fp16)[name = tensor("input_225_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_225_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + 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(348792512)))]; + 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(348795136)))]; + 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_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor input_229_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor("input_229_cast_fp16")]; + tensor var_3104 = const()[name = tensor("op_3104"), val = tensor([1, 1])]; + tensor var_3106 = const()[name = tensor("op_3106"), val = tensor([1, 1])]; + tensor hidden_states_131_pad_type_0 = const()[name = tensor("hidden_states_131_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_131_pad_0 = const()[name = tensor("hidden_states_131_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(348797760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359857024))), 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(359857216)))]; + tensor hidden_states_131_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3106, groups = var_1196, pad = hidden_states_131_pad_0, pad_type = hidden_states_131_pad_type_0, strides = var_3104, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = tensor("hidden_states_131_cast_fp16")]; + tensor hidden_states_133_cast_fp16 = add(x = input_217_cast_fp16, y = hidden_states_131_cast_fp16)[name = tensor("hidden_states_133_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = hidden_states_133_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + 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(359859840)))]; + 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(359862464)))]; + 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_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor var_3144 = const()[name = tensor("op_3144"), val = tensor([1, 1])]; + tensor var_3146 = const()[name = tensor("op_3146"), 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([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(359865088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361093952))), 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(361094144)))]; + tensor hidden_states_135_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3146, groups = var_1196, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_3144, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_31_cast_fp16)[name = tensor("hidden_states_135_cast_fp16")]; + tensor var_3151 = const()[name = tensor("op_3151"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_85_cast_fp16 = reshape(shape = var_3151, x = hidden_states_135_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; + tensor hidden_states_137_axes_0 = const()[name = tensor("hidden_states_137_axes_0"), val = tensor([1])]; + tensor hidden_states_137_gamma_0_to_fp16 = const()[name = tensor("hidden_states_137_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361096768)))]; + tensor hidden_states_137_beta_0_to_fp16 = const()[name = tensor("hidden_states_137_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361099392)))]; + tensor var_3167_to_fp16 = const()[name = tensor("op_3167_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_137_cast_fp16 = layer_norm(axes = hidden_states_137_axes_0, beta = hidden_states_137_beta_0_to_fp16, epsilon = var_3167_to_fp16, gamma = hidden_states_137_gamma_0_to_fp16, x = inputs_85_cast_fp16)[name = tensor("hidden_states_137_cast_fp16")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([1, 1])]; + tensor var_3184 = const()[name = tensor("op_3184"), 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 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(361102016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362330880))), 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_57_cast_fp16 = conv(dilations = var_3184, groups = var_1196, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_3182, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_137_cast_fp16)[name = tensor("q_57_cast_fp16")]; + tensor var_3188 = const()[name = tensor("op_3188"), val = tensor([1, 1])]; + tensor var_3190 = const()[name = tensor("op_3190"), 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 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(362331072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(363559936))), 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_57_cast_fp16 = conv(dilations = var_3190, groups = var_1196, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_3188, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_137_cast_fp16)[name = tensor("k_57_cast_fp16")]; + tensor var_3194 = const()[name = tensor("op_3194"), val = tensor([1, 1])]; + tensor var_3196 = const()[name = tensor("op_3196"), 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 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(363560128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364788992))), 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_57_cast_fp16 = conv(dilations = var_3196, groups = var_1196, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_3194, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_137_cast_fp16)[name = tensor("v_57_cast_fp16")]; + tensor var_3200 = const()[name = tensor("op_3200"), val = tensor([1, 20, 64, -1])]; + tensor var_3201_cast_fp16 = reshape(shape = var_3200, x = q_57_cast_fp16)[name = tensor("op_3201_cast_fp16")]; + tensor var_3202 = const()[name = tensor("op_3202"), val = tensor([1, 20, 64, -1])]; + tensor var_3203_cast_fp16 = reshape(shape = var_3202, x = k_57_cast_fp16)[name = tensor("op_3203_cast_fp16")]; + tensor var_3204 = const()[name = tensor("op_3204"), val = tensor([1, 20, 64, -1])]; + tensor var_3205_cast_fp16 = reshape(shape = var_3204, x = v_57_cast_fp16)[name = tensor("op_3205_cast_fp16")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3201_cast_fp16, y = var_3203_cast_fp16)[name = tensor("attn_weights_113_cast_fp16")]; + tensor attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_115_cast_fp16")]; + tensor var_3209_cast_fp16 = softmax(axis = var_1180, x = attn_weights_115_cast_fp16)[name = tensor("op_3209_cast_fp16")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3205_cast_fp16, y = var_3209_cast_fp16)[name = tensor("attn_57_cast_fp16")]; + tensor var_3213 = const()[name = tensor("op_3213"), val = tensor([1, 1280, 1, -1])]; + tensor input_233_cast_fp16 = reshape(shape = var_3213, x = attn_57_cast_fp16)[name = tensor("input_233_cast_fp16")]; + tensor var_3218 = const()[name = tensor("op_3218"), val = tensor([1, 1])]; + tensor var_3220 = const()[name = tensor("op_3220"), val = tensor([1, 1])]; + tensor var_3222_pad_type_0 = const()[name = tensor("op_3222_pad_type_0"), val = tensor("custom")]; + tensor var_3222_pad_0 = const()[name = tensor("op_3222_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(364789184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366018048))), 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(366018240)))]; + tensor var_3222_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3220, groups = var_1196, pad = var_3222_pad_0, pad_type = var_3222_pad_type_0, strides = var_3218, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_233_cast_fp16)[name = tensor("op_3222_cast_fp16")]; + tensor inputs_87_cast_fp16 = add(x = var_3222_cast_fp16, y = inputs_85_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; + tensor hidden_states_139_axes_0 = const()[name = tensor("hidden_states_139_axes_0"), val = tensor([1])]; + tensor hidden_states_139_gamma_0_to_fp16 = const()[name = tensor("hidden_states_139_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366020864)))]; + tensor hidden_states_139_beta_0_to_fp16 = const()[name = tensor("hidden_states_139_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366023488)))]; + tensor var_3232_to_fp16 = const()[name = tensor("op_3232_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_139_cast_fp16 = layer_norm(axes = hidden_states_139_axes_0, beta = hidden_states_139_beta_0_to_fp16, epsilon = var_3232_to_fp16, gamma = hidden_states_139_gamma_0_to_fp16, x = inputs_87_cast_fp16)[name = tensor("hidden_states_139_cast_fp16")]; + tensor var_3247 = const()[name = tensor("op_3247"), val = tensor([1, 1])]; + tensor var_3249 = const()[name = tensor("op_3249"), 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 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(366026112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367254976))), 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_59_cast_fp16 = conv(dilations = var_3249, groups = var_1196, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_3247, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_139_cast_fp16)[name = tensor("q_59_cast_fp16")]; + tensor var_3253 = const()[name = tensor("op_3253"), val = tensor([1, 1])]; + tensor var_3255 = const()[name = tensor("op_3255"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_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(367255168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369221312))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_59_cast_fp16 = conv(dilations = var_3255, groups = var_1196, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_3253, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_59_cast_fp16")]; + tensor var_3259 = const()[name = tensor("op_3259"), val = tensor([1, 1])]; + tensor var_3261 = const()[name = tensor("op_3261"), 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 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(369221504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371187648))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_59_cast_fp16 = conv(dilations = var_3261, groups = var_1196, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_3259, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_59_cast_fp16")]; + tensor var_3265 = const()[name = tensor("op_3265"), val = tensor([1, 20, 64, -1])]; + tensor var_3266_cast_fp16 = reshape(shape = var_3265, x = q_59_cast_fp16)[name = tensor("op_3266_cast_fp16")]; + tensor var_3267 = const()[name = tensor("op_3267"), val = tensor([1, 20, 64, -1])]; + tensor var_3268_cast_fp16 = reshape(shape = var_3267, x = k_59_cast_fp16)[name = tensor("op_3268_cast_fp16")]; + tensor var_3269 = const()[name = tensor("op_3269"), val = tensor([1, 20, 64, -1])]; + tensor var_3270_cast_fp16 = reshape(shape = var_3269, x = v_59_cast_fp16)[name = tensor("op_3270_cast_fp16")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast_fp16 = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_3266_cast_fp16, y = var_3268_cast_fp16)[name = tensor("attn_weights_117_cast_fp16")]; + tensor attn_weights_119_cast_fp16 = mul(x = attn_weights_117_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_119_cast_fp16")]; + tensor var_3274_cast_fp16 = softmax(axis = var_1180, x = attn_weights_119_cast_fp16)[name = tensor("op_3274_cast_fp16")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3270_cast_fp16, y = var_3274_cast_fp16)[name = tensor("attn_59_cast_fp16")]; + tensor var_3278 = const()[name = tensor("op_3278"), val = tensor([1, 1280, 1, -1])]; + tensor input_235_cast_fp16 = reshape(shape = var_3278, x = attn_59_cast_fp16)[name = tensor("input_235_cast_fp16")]; + tensor var_3283 = const()[name = tensor("op_3283"), val = tensor([1, 1])]; + tensor var_3285 = const()[name = tensor("op_3285"), val = tensor([1, 1])]; + tensor var_3287_pad_type_0 = const()[name = tensor("op_3287_pad_type_0"), val = tensor("custom")]; + tensor var_3287_pad_0 = const()[name = tensor("op_3287_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(371187840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372416704))), 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(372416896)))]; + tensor var_3287_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3285, groups = var_1196, pad = var_3287_pad_0, pad_type = var_3287_pad_type_0, strides = var_3283, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_235_cast_fp16)[name = tensor("op_3287_cast_fp16")]; + tensor inputs_89_cast_fp16 = add(x = var_3287_cast_fp16, y = inputs_87_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; + tensor input_237_axes_0 = const()[name = tensor("input_237_axes_0"), val = tensor([1])]; + tensor input_237_gamma_0_to_fp16 = const()[name = tensor("input_237_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372419520)))]; + tensor input_237_beta_0_to_fp16 = const()[name = tensor("input_237_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372422144)))]; + tensor var_3297_to_fp16 = const()[name = tensor("op_3297_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = input_237_beta_0_to_fp16, epsilon = var_3297_to_fp16, gamma = input_237_gamma_0_to_fp16, x = inputs_89_cast_fp16)[name = tensor("input_237_cast_fp16")]; + tensor var_3313 = const()[name = tensor("op_3313"), val = tensor([1, 1])]; + tensor var_3315 = const()[name = tensor("op_3315"), val = tensor([1, 1])]; + tensor var_3317_pad_type_0 = const()[name = tensor("op_3317_pad_type_0"), val = tensor("custom")]; + tensor var_3317_pad_0 = const()[name = tensor("op_3317_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(372424768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382255232))), 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(382255424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382263168))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3317_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3315, groups = var_1196, pad = var_3317_pad_0, pad_type = var_3317_pad_type_0, strides = var_3313, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = tensor("op_3317_cast_fp16")]; + tensor var_3318_split_sizes_0 = const()[name = tensor("op_3318_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3318_axis_0 = const()[name = tensor("op_3318_axis_0"), val = tensor(1)]; + tensor var_3318_cast_fp16_0, tensor var_3318_cast_fp16_1 = split(axis = var_3318_axis_0, split_sizes = var_3318_split_sizes_0, x = var_3317_cast_fp16)[name = tensor("op_3318_cast_fp16")]; + tensor var_3320_mode_0 = const()[name = tensor("op_3320_mode_0"), val = tensor("EXACT")]; + tensor var_3320_cast_fp16 = gelu(mode = var_3320_mode_0, x = var_3318_cast_fp16_1)[name = tensor("op_3320_cast_fp16")]; + tensor input_239_cast_fp16 = mul(x = var_3318_cast_fp16_0, y = var_3320_cast_fp16)[name = tensor("input_239_cast_fp16")]; + tensor var_3324 = const()[name = tensor("op_3324"), val = tensor([1, 1])]; + tensor var_3326 = const()[name = tensor("op_3326"), val = tensor([1, 1])]; + tensor var_3328_pad_type_0 = const()[name = tensor("op_3328_pad_type_0"), val = tensor("custom")]; + tensor var_3328_pad_0 = const()[name = tensor("op_3328_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(382263360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387178624))), 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(387178816)))]; + tensor var_3328_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3326, groups = var_1196, pad = var_3328_pad_0, pad_type = var_3328_pad_type_0, strides = var_3324, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_239_cast_fp16)[name = tensor("op_3328_cast_fp16")]; + tensor inputs_91_cast_fp16 = add(x = var_3328_cast_fp16, y = inputs_89_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; + tensor hidden_states_143_axes_0 = const()[name = tensor("hidden_states_143_axes_0"), val = tensor([1])]; + tensor hidden_states_143_gamma_0_to_fp16 = const()[name = tensor("hidden_states_143_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387181440)))]; + tensor hidden_states_143_beta_0_to_fp16 = const()[name = tensor("hidden_states_143_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387184064)))]; + tensor var_3344_to_fp16 = const()[name = tensor("op_3344_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_143_cast_fp16 = layer_norm(axes = hidden_states_143_axes_0, beta = hidden_states_143_beta_0_to_fp16, epsilon = var_3344_to_fp16, gamma = hidden_states_143_gamma_0_to_fp16, x = inputs_91_cast_fp16)[name = tensor("hidden_states_143_cast_fp16")]; + tensor var_3359 = const()[name = tensor("op_3359"), val = tensor([1, 1])]; + tensor var_3361 = const()[name = tensor("op_3361"), 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 down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387186688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(388415552))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_61_cast_fp16 = conv(dilations = var_3361, groups = var_1196, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_3359, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_143_cast_fp16)[name = tensor("q_61_cast_fp16")]; + 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 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 down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(388415744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389644608))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_61_cast_fp16 = conv(dilations = var_3367, groups = var_1196, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_3365, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_143_cast_fp16)[name = tensor("k_61_cast_fp16")]; + tensor var_3371 = const()[name = tensor("op_3371"), val = tensor([1, 1])]; + tensor var_3373 = const()[name = tensor("op_3373"), 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 down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389644800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(390873664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_61_cast_fp16 = conv(dilations = var_3373, groups = var_1196, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_3371, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_143_cast_fp16)[name = tensor("v_61_cast_fp16")]; + tensor var_3377 = const()[name = tensor("op_3377"), val = tensor([1, 20, 64, -1])]; + tensor var_3378_cast_fp16 = reshape(shape = var_3377, x = q_61_cast_fp16)[name = tensor("op_3378_cast_fp16")]; + tensor var_3379 = const()[name = tensor("op_3379"), val = tensor([1, 20, 64, -1])]; + tensor var_3380_cast_fp16 = reshape(shape = var_3379, x = k_61_cast_fp16)[name = tensor("op_3380_cast_fp16")]; + tensor var_3381 = const()[name = tensor("op_3381"), val = tensor([1, 20, 64, -1])]; + tensor var_3382_cast_fp16 = reshape(shape = var_3381, x = v_61_cast_fp16)[name = tensor("op_3382_cast_fp16")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3378_cast_fp16, y = var_3380_cast_fp16)[name = tensor("attn_weights_121_cast_fp16")]; + tensor attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_123_cast_fp16")]; + tensor var_3386_cast_fp16 = softmax(axis = var_1180, x = attn_weights_123_cast_fp16)[name = tensor("op_3386_cast_fp16")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3382_cast_fp16, y = var_3386_cast_fp16)[name = tensor("attn_61_cast_fp16")]; + tensor var_3390 = const()[name = tensor("op_3390"), val = tensor([1, 1280, 1, -1])]; + tensor input_241_cast_fp16 = reshape(shape = var_3390, x = attn_61_cast_fp16)[name = tensor("input_241_cast_fp16")]; + tensor var_3395 = const()[name = tensor("op_3395"), val = tensor([1, 1])]; + tensor var_3397 = const()[name = tensor("op_3397"), val = tensor([1, 1])]; + tensor var_3399_pad_type_0 = const()[name = tensor("op_3399_pad_type_0"), val = tensor("custom")]; + tensor var_3399_pad_0 = const()[name = tensor("op_3399_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(390873856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392102720))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392102912)))]; + tensor var_3399_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_3397, groups = var_1196, pad = var_3399_pad_0, pad_type = var_3399_pad_type_0, strides = var_3395, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = tensor("op_3399_cast_fp16")]; + tensor inputs_93_cast_fp16 = add(x = var_3399_cast_fp16, y = inputs_91_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; + tensor hidden_states_145_axes_0 = const()[name = tensor("hidden_states_145_axes_0"), val = tensor([1])]; + tensor hidden_states_145_gamma_0_to_fp16 = const()[name = tensor("hidden_states_145_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392105536)))]; + tensor hidden_states_145_beta_0_to_fp16 = const()[name = tensor("hidden_states_145_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392108160)))]; + tensor var_3409_to_fp16 = const()[name = tensor("op_3409_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_145_cast_fp16 = layer_norm(axes = hidden_states_145_axes_0, beta = hidden_states_145_beta_0_to_fp16, epsilon = var_3409_to_fp16, gamma = hidden_states_145_gamma_0_to_fp16, x = inputs_93_cast_fp16)[name = tensor("hidden_states_145_cast_fp16")]; + tensor var_3424 = const()[name = tensor("op_3424"), val = tensor([1, 1])]; + tensor var_3426 = const()[name = tensor("op_3426"), val = tensor([1, 1])]; + tensor q_63_pad_type_0 = const()[name = tensor("q_63_pad_type_0"), val = tensor("custom")]; + tensor q_63_pad_0 = const()[name = tensor("q_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392110784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393339648))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_63_cast_fp16 = conv(dilations = var_3426, groups = var_1196, pad = q_63_pad_0, pad_type = q_63_pad_type_0, strides = var_3424, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = tensor("q_63_cast_fp16")]; + tensor var_3430 = const()[name = tensor("op_3430"), val = tensor([1, 1])]; + tensor var_3432 = const()[name = tensor("op_3432"), val = tensor([1, 1])]; + tensor k_63_pad_type_0 = const()[name = tensor("k_63_pad_type_0"), val = tensor("custom")]; + tensor k_63_pad_0 = const()[name = tensor("k_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393339840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395305984))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_63_cast_fp16 = conv(dilations = var_3432, groups = var_1196, pad = k_63_pad_0, pad_type = k_63_pad_type_0, strides = var_3430, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_63_cast_fp16")]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 1])]; + tensor var_3438 = const()[name = tensor("op_3438"), val = tensor([1, 1])]; + tensor v_63_pad_type_0 = const()[name = tensor("v_63_pad_type_0"), val = tensor("custom")]; + tensor v_63_pad_0 = const()[name = tensor("v_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395306176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397272320))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_63_cast_fp16 = conv(dilations = var_3438, groups = var_1196, pad = v_63_pad_0, pad_type = v_63_pad_type_0, strides = var_3436, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_63_cast_fp16")]; + tensor var_3442 = const()[name = tensor("op_3442"), val = tensor([1, 20, 64, -1])]; + tensor var_3443_cast_fp16 = reshape(shape = var_3442, x = q_63_cast_fp16)[name = tensor("op_3443_cast_fp16")]; + tensor var_3444 = const()[name = tensor("op_3444"), val = tensor([1, 20, 64, -1])]; + tensor var_3445_cast_fp16 = reshape(shape = var_3444, x = k_63_cast_fp16)[name = tensor("op_3445_cast_fp16")]; + tensor var_3446 = const()[name = tensor("op_3446"), val = tensor([1, 20, 64, -1])]; + tensor var_3447_cast_fp16 = reshape(shape = var_3446, x = v_63_cast_fp16)[name = tensor("op_3447_cast_fp16")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast_fp16 = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_3443_cast_fp16, y = var_3445_cast_fp16)[name = tensor("attn_weights_125_cast_fp16")]; + tensor attn_weights_127_cast_fp16 = mul(x = attn_weights_125_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_127_cast_fp16")]; + tensor var_3451_cast_fp16 = softmax(axis = var_1180, x = attn_weights_127_cast_fp16)[name = tensor("op_3451_cast_fp16")]; + tensor attn_63_transpose_x_0 = const()[name = tensor("attn_63_transpose_x_0"), val = tensor(false)]; + tensor attn_63_transpose_y_0 = const()[name = tensor("attn_63_transpose_y_0"), val = tensor(true)]; + tensor attn_63_cast_fp16 = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3447_cast_fp16, y = var_3451_cast_fp16)[name = tensor("attn_63_cast_fp16")]; + tensor var_3455 = const()[name = tensor("op_3455"), val = tensor([1, 1280, 1, -1])]; + tensor input_243_cast_fp16 = reshape(shape = var_3455, x = attn_63_cast_fp16)[name = tensor("input_243_cast_fp16")]; + tensor var_3460 = const()[name = tensor("op_3460"), val = tensor([1, 1])]; + tensor var_3462 = const()[name = tensor("op_3462"), val = tensor([1, 1])]; + tensor var_3464_pad_type_0 = const()[name = tensor("op_3464_pad_type_0"), val = tensor("custom")]; + tensor var_3464_pad_0 = const()[name = tensor("op_3464_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397272512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398501376))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398501568)))]; + tensor var_3464_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_3462, groups = var_1196, pad = var_3464_pad_0, pad_type = var_3464_pad_type_0, strides = var_3460, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_243_cast_fp16)[name = tensor("op_3464_cast_fp16")]; + tensor inputs_95_cast_fp16 = add(x = var_3464_cast_fp16, y = inputs_93_cast_fp16)[name = tensor("inputs_95_cast_fp16")]; + tensor input_245_axes_0 = const()[name = tensor("input_245_axes_0"), val = tensor([1])]; + tensor input_245_gamma_0_to_fp16 = const()[name = tensor("input_245_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398504192)))]; + tensor input_245_beta_0_to_fp16 = const()[name = tensor("input_245_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398506816)))]; + tensor var_3474_to_fp16 = const()[name = tensor("op_3474_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_245_cast_fp16 = layer_norm(axes = input_245_axes_0, beta = input_245_beta_0_to_fp16, epsilon = var_3474_to_fp16, gamma = input_245_gamma_0_to_fp16, x = inputs_95_cast_fp16)[name = tensor("input_245_cast_fp16")]; + tensor var_3490 = const()[name = tensor("op_3490"), val = tensor([1, 1])]; + tensor var_3492 = const()[name = tensor("op_3492"), val = tensor([1, 1])]; + tensor var_3494_pad_type_0 = const()[name = tensor("op_3494_pad_type_0"), val = tensor("custom")]; + tensor var_3494_pad_0 = const()[name = tensor("op_3494_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398509440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408339904))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408340096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408347840))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3494_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3492, groups = var_1196, pad = var_3494_pad_0, pad_type = var_3494_pad_type_0, strides = var_3490, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_245_cast_fp16)[name = tensor("op_3494_cast_fp16")]; + tensor var_3495_split_sizes_0 = const()[name = tensor("op_3495_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3495_axis_0 = const()[name = tensor("op_3495_axis_0"), val = tensor(1)]; + tensor var_3495_cast_fp16_0, tensor var_3495_cast_fp16_1 = split(axis = var_3495_axis_0, split_sizes = var_3495_split_sizes_0, x = var_3494_cast_fp16)[name = tensor("op_3495_cast_fp16")]; + tensor var_3497_mode_0 = const()[name = tensor("op_3497_mode_0"), val = tensor("EXACT")]; + tensor var_3497_cast_fp16 = gelu(mode = var_3497_mode_0, x = var_3495_cast_fp16_1)[name = tensor("op_3497_cast_fp16")]; + tensor input_247_cast_fp16 = mul(x = var_3495_cast_fp16_0, y = var_3497_cast_fp16)[name = tensor("input_247_cast_fp16")]; + tensor var_3501 = const()[name = tensor("op_3501"), val = tensor([1, 1])]; + tensor var_3503 = const()[name = tensor("op_3503"), val = tensor([1, 1])]; + tensor var_3505_pad_type_0 = const()[name = tensor("op_3505_pad_type_0"), val = tensor("custom")]; + tensor var_3505_pad_0 = const()[name = tensor("op_3505_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408348032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413263296))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413263488)))]; + tensor var_3505_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_3503, groups = var_1196, pad = var_3505_pad_0, pad_type = var_3505_pad_type_0, strides = var_3501, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = tensor("op_3505_cast_fp16")]; + tensor inputs_97_cast_fp16 = add(x = var_3505_cast_fp16, y = inputs_95_cast_fp16)[name = tensor("inputs_97_cast_fp16")]; + tensor hidden_states_149_axes_0 = const()[name = tensor("hidden_states_149_axes_0"), val = tensor([1])]; + tensor hidden_states_149_gamma_0_to_fp16 = const()[name = tensor("hidden_states_149_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413266112)))]; + tensor hidden_states_149_beta_0_to_fp16 = const()[name = tensor("hidden_states_149_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413268736)))]; + tensor var_3521_to_fp16 = const()[name = tensor("op_3521_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_149_cast_fp16 = layer_norm(axes = hidden_states_149_axes_0, beta = hidden_states_149_beta_0_to_fp16, epsilon = var_3521_to_fp16, gamma = hidden_states_149_gamma_0_to_fp16, x = inputs_97_cast_fp16)[name = tensor("hidden_states_149_cast_fp16")]; + tensor var_3536 = const()[name = tensor("op_3536"), val = tensor([1, 1])]; + tensor var_3538 = const()[name = tensor("op_3538"), val = tensor([1, 1])]; + tensor q_65_pad_type_0 = const()[name = tensor("q_65_pad_type_0"), val = tensor("custom")]; + tensor q_65_pad_0 = const()[name = tensor("q_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413271360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414500224))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_65_cast_fp16 = conv(dilations = var_3538, groups = var_1196, pad = q_65_pad_0, pad_type = q_65_pad_type_0, strides = var_3536, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_149_cast_fp16)[name = tensor("q_65_cast_fp16")]; + tensor var_3542 = const()[name = tensor("op_3542"), val = tensor([1, 1])]; + tensor var_3544 = const()[name = tensor("op_3544"), 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 down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414500416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415729280))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_65_cast_fp16 = conv(dilations = var_3544, groups = var_1196, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_3542, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_149_cast_fp16)[name = tensor("k_65_cast_fp16")]; + tensor var_3548 = const()[name = tensor("op_3548"), val = tensor([1, 1])]; + tensor var_3550 = const()[name = tensor("op_3550"), val = tensor([1, 1])]; + tensor v_65_pad_type_0 = const()[name = tensor("v_65_pad_type_0"), val = tensor("custom")]; + tensor v_65_pad_0 = const()[name = tensor("v_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415729472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416958336))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_65_cast_fp16 = conv(dilations = var_3550, groups = var_1196, pad = v_65_pad_0, pad_type = v_65_pad_type_0, strides = var_3548, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_149_cast_fp16)[name = tensor("v_65_cast_fp16")]; + tensor var_3554 = const()[name = tensor("op_3554"), val = tensor([1, 20, 64, -1])]; + tensor var_3555_cast_fp16 = reshape(shape = var_3554, x = q_65_cast_fp16)[name = tensor("op_3555_cast_fp16")]; + tensor var_3556 = const()[name = tensor("op_3556"), val = tensor([1, 20, 64, -1])]; + tensor var_3557_cast_fp16 = reshape(shape = var_3556, x = k_65_cast_fp16)[name = tensor("op_3557_cast_fp16")]; + tensor var_3558 = const()[name = tensor("op_3558"), val = tensor([1, 20, 64, -1])]; + tensor var_3559_cast_fp16 = reshape(shape = var_3558, x = v_65_cast_fp16)[name = tensor("op_3559_cast_fp16")]; + tensor attn_weights_129_transpose_x_0 = const()[name = tensor("attn_weights_129_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_129_transpose_y_0 = const()[name = tensor("attn_weights_129_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3555_cast_fp16, y = var_3557_cast_fp16)[name = tensor("attn_weights_129_cast_fp16")]; + tensor attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_131_cast_fp16")]; + tensor var_3563_cast_fp16 = softmax(axis = var_1180, x = attn_weights_131_cast_fp16)[name = tensor("op_3563_cast_fp16")]; + tensor attn_65_transpose_x_0 = const()[name = tensor("attn_65_transpose_x_0"), val = tensor(false)]; + tensor attn_65_transpose_y_0 = const()[name = tensor("attn_65_transpose_y_0"), val = tensor(true)]; + tensor attn_65_cast_fp16 = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3559_cast_fp16, y = var_3563_cast_fp16)[name = tensor("attn_65_cast_fp16")]; + tensor var_3567 = const()[name = tensor("op_3567"), val = tensor([1, 1280, 1, -1])]; + tensor input_249_cast_fp16 = reshape(shape = var_3567, x = attn_65_cast_fp16)[name = tensor("input_249_cast_fp16")]; + 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 var_3576_pad_type_0 = const()[name = tensor("op_3576_pad_type_0"), val = tensor("custom")]; + tensor var_3576_pad_0 = const()[name = tensor("op_3576_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416958528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418187392))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418187584)))]; + tensor var_3576_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_3574, groups = var_1196, pad = var_3576_pad_0, pad_type = var_3576_pad_type_0, strides = var_3572, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_249_cast_fp16)[name = tensor("op_3576_cast_fp16")]; + tensor inputs_99_cast_fp16 = add(x = var_3576_cast_fp16, y = inputs_97_cast_fp16)[name = tensor("inputs_99_cast_fp16")]; + tensor hidden_states_151_axes_0 = const()[name = tensor("hidden_states_151_axes_0"), val = tensor([1])]; + tensor hidden_states_151_gamma_0_to_fp16 = const()[name = tensor("hidden_states_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418190208)))]; + tensor hidden_states_151_beta_0_to_fp16 = const()[name = tensor("hidden_states_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418192832)))]; + tensor var_3586_to_fp16 = const()[name = tensor("op_3586_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_151_cast_fp16 = layer_norm(axes = hidden_states_151_axes_0, beta = hidden_states_151_beta_0_to_fp16, epsilon = var_3586_to_fp16, gamma = hidden_states_151_gamma_0_to_fp16, x = inputs_99_cast_fp16)[name = tensor("hidden_states_151_cast_fp16")]; + tensor var_3601 = const()[name = tensor("op_3601"), val = tensor([1, 1])]; + tensor var_3603 = const()[name = tensor("op_3603"), val = tensor([1, 1])]; + tensor q_67_pad_type_0 = const()[name = tensor("q_67_pad_type_0"), val = tensor("custom")]; + tensor q_67_pad_0 = const()[name = tensor("q_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418195456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419424320))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_67_cast_fp16 = conv(dilations = var_3603, groups = var_1196, pad = q_67_pad_0, pad_type = q_67_pad_type_0, strides = var_3601, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_151_cast_fp16)[name = tensor("q_67_cast_fp16")]; + tensor var_3607 = const()[name = tensor("op_3607"), val = tensor([1, 1])]; + tensor var_3609 = const()[name = tensor("op_3609"), val = tensor([1, 1])]; + tensor k_67_pad_type_0 = const()[name = tensor("k_67_pad_type_0"), val = tensor("custom")]; + tensor k_67_pad_0 = const()[name = tensor("k_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419424512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421390656))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_67_cast_fp16 = conv(dilations = var_3609, groups = var_1196, pad = k_67_pad_0, pad_type = k_67_pad_type_0, strides = var_3607, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_67_cast_fp16")]; + tensor var_3613 = const()[name = tensor("op_3613"), val = tensor([1, 1])]; + tensor var_3615 = const()[name = tensor("op_3615"), val = tensor([1, 1])]; + tensor v_67_pad_type_0 = const()[name = tensor("v_67_pad_type_0"), val = tensor("custom")]; + tensor v_67_pad_0 = const()[name = tensor("v_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421390848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423356992))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_67_cast_fp16 = conv(dilations = var_3615, groups = var_1196, pad = v_67_pad_0, pad_type = v_67_pad_type_0, strides = var_3613, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_67_cast_fp16")]; + tensor var_3619 = const()[name = tensor("op_3619"), val = tensor([1, 20, 64, -1])]; + tensor var_3620_cast_fp16 = reshape(shape = var_3619, x = q_67_cast_fp16)[name = tensor("op_3620_cast_fp16")]; + tensor var_3621 = const()[name = tensor("op_3621"), val = tensor([1, 20, 64, -1])]; + tensor var_3622_cast_fp16 = reshape(shape = var_3621, x = k_67_cast_fp16)[name = tensor("op_3622_cast_fp16")]; + tensor var_3623 = const()[name = tensor("op_3623"), val = tensor([1, 20, 64, -1])]; + tensor var_3624_cast_fp16 = reshape(shape = var_3623, x = v_67_cast_fp16)[name = tensor("op_3624_cast_fp16")]; + tensor attn_weights_133_transpose_x_0 = const()[name = tensor("attn_weights_133_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_133_transpose_y_0 = const()[name = tensor("attn_weights_133_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_133_cast_fp16 = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = var_3620_cast_fp16, y = var_3622_cast_fp16)[name = tensor("attn_weights_133_cast_fp16")]; + tensor attn_weights_135_cast_fp16 = mul(x = attn_weights_133_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_135_cast_fp16")]; + tensor var_3628_cast_fp16 = softmax(axis = var_1180, x = attn_weights_135_cast_fp16)[name = tensor("op_3628_cast_fp16")]; + tensor attn_67_transpose_x_0 = const()[name = tensor("attn_67_transpose_x_0"), val = tensor(false)]; + tensor attn_67_transpose_y_0 = const()[name = tensor("attn_67_transpose_y_0"), val = tensor(true)]; + tensor attn_67_cast_fp16 = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3624_cast_fp16, y = var_3628_cast_fp16)[name = tensor("attn_67_cast_fp16")]; + tensor var_3632 = const()[name = tensor("op_3632"), val = tensor([1, 1280, 1, -1])]; + tensor input_251_cast_fp16 = reshape(shape = var_3632, x = attn_67_cast_fp16)[name = tensor("input_251_cast_fp16")]; + tensor var_3637 = const()[name = tensor("op_3637"), val = tensor([1, 1])]; + tensor var_3639 = const()[name = tensor("op_3639"), val = tensor([1, 1])]; + tensor var_3641_pad_type_0 = const()[name = tensor("op_3641_pad_type_0"), val = tensor("custom")]; + tensor var_3641_pad_0 = const()[name = tensor("op_3641_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423357184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424586048))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424586240)))]; + tensor var_3641_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_3639, groups = var_1196, pad = var_3641_pad_0, pad_type = var_3641_pad_type_0, strides = var_3637, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = tensor("op_3641_cast_fp16")]; + tensor inputs_101_cast_fp16 = add(x = var_3641_cast_fp16, y = inputs_99_cast_fp16)[name = tensor("inputs_101_cast_fp16")]; + tensor input_253_axes_0 = const()[name = tensor("input_253_axes_0"), val = tensor([1])]; + tensor input_253_gamma_0_to_fp16 = const()[name = tensor("input_253_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424588864)))]; + tensor input_253_beta_0_to_fp16 = const()[name = tensor("input_253_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424591488)))]; + tensor var_3651_to_fp16 = const()[name = tensor("op_3651_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_253_cast_fp16 = layer_norm(axes = input_253_axes_0, beta = input_253_beta_0_to_fp16, epsilon = var_3651_to_fp16, gamma = input_253_gamma_0_to_fp16, x = inputs_101_cast_fp16)[name = tensor("input_253_cast_fp16")]; + tensor var_3667 = const()[name = tensor("op_3667"), val = tensor([1, 1])]; + tensor var_3669 = const()[name = tensor("op_3669"), val = tensor([1, 1])]; + tensor var_3671_pad_type_0 = const()[name = tensor("op_3671_pad_type_0"), val = tensor("custom")]; + tensor var_3671_pad_0 = const()[name = tensor("op_3671_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424594112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434424576))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434424768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434432512))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3671_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3669, groups = var_1196, pad = var_3671_pad_0, pad_type = var_3671_pad_type_0, strides = var_3667, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_253_cast_fp16)[name = tensor("op_3671_cast_fp16")]; + tensor var_3672_split_sizes_0 = const()[name = tensor("op_3672_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3672_axis_0 = const()[name = tensor("op_3672_axis_0"), val = tensor(1)]; + tensor var_3672_cast_fp16_0, tensor var_3672_cast_fp16_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = var_3671_cast_fp16)[name = tensor("op_3672_cast_fp16")]; + tensor var_3674_mode_0 = const()[name = tensor("op_3674_mode_0"), val = tensor("EXACT")]; + tensor var_3674_cast_fp16 = gelu(mode = var_3674_mode_0, x = var_3672_cast_fp16_1)[name = tensor("op_3674_cast_fp16")]; + tensor input_255_cast_fp16 = mul(x = var_3672_cast_fp16_0, y = var_3674_cast_fp16)[name = tensor("input_255_cast_fp16")]; + tensor var_3678 = const()[name = tensor("op_3678"), val = tensor([1, 1])]; + tensor var_3680 = const()[name = tensor("op_3680"), val = tensor([1, 1])]; + tensor var_3682_pad_type_0 = const()[name = tensor("op_3682_pad_type_0"), val = tensor("custom")]; + tensor var_3682_pad_0 = const()[name = tensor("op_3682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434432704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439347968))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439348160)))]; + tensor var_3682_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_3680, groups = var_1196, pad = var_3682_pad_0, pad_type = var_3682_pad_type_0, strides = var_3678, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = tensor("op_3682_cast_fp16")]; + tensor inputs_103_cast_fp16 = add(x = var_3682_cast_fp16, y = inputs_101_cast_fp16)[name = tensor("inputs_103_cast_fp16")]; + tensor hidden_states_155_axes_0 = const()[name = tensor("hidden_states_155_axes_0"), val = tensor([1])]; + tensor hidden_states_155_gamma_0_to_fp16 = const()[name = tensor("hidden_states_155_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439350784)))]; + tensor hidden_states_155_beta_0_to_fp16 = const()[name = tensor("hidden_states_155_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439353408)))]; + tensor var_3698_to_fp16 = const()[name = tensor("op_3698_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_155_cast_fp16 = layer_norm(axes = hidden_states_155_axes_0, beta = hidden_states_155_beta_0_to_fp16, epsilon = var_3698_to_fp16, gamma = hidden_states_155_gamma_0_to_fp16, x = inputs_103_cast_fp16)[name = tensor("hidden_states_155_cast_fp16")]; + tensor var_3713 = const()[name = tensor("op_3713"), val = tensor([1, 1])]; + tensor var_3715 = const()[name = tensor("op_3715"), val = tensor([1, 1])]; + tensor q_69_pad_type_0 = const()[name = tensor("q_69_pad_type_0"), val = tensor("custom")]; + tensor q_69_pad_0 = const()[name = tensor("q_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439356032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440584896))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_69_cast_fp16 = conv(dilations = var_3715, groups = var_1196, pad = q_69_pad_0, pad_type = q_69_pad_type_0, strides = var_3713, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_155_cast_fp16)[name = tensor("q_69_cast_fp16")]; + tensor var_3719 = const()[name = tensor("op_3719"), val = tensor([1, 1])]; + tensor var_3721 = const()[name = tensor("op_3721"), 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 down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440585088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441813952))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_69_cast_fp16 = conv(dilations = var_3721, groups = var_1196, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_3719, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_155_cast_fp16)[name = tensor("k_69_cast_fp16")]; + tensor var_3725 = const()[name = tensor("op_3725"), val = tensor([1, 1])]; + tensor var_3727 = const()[name = tensor("op_3727"), val = tensor([1, 1])]; + tensor v_69_pad_type_0 = const()[name = tensor("v_69_pad_type_0"), val = tensor("custom")]; + tensor v_69_pad_0 = const()[name = tensor("v_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441814144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(443043008))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_69_cast_fp16 = conv(dilations = var_3727, groups = var_1196, pad = v_69_pad_0, pad_type = v_69_pad_type_0, strides = var_3725, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_155_cast_fp16)[name = tensor("v_69_cast_fp16")]; + tensor var_3731 = const()[name = tensor("op_3731"), val = tensor([1, 20, 64, -1])]; + tensor var_3732_cast_fp16 = reshape(shape = var_3731, x = q_69_cast_fp16)[name = tensor("op_3732_cast_fp16")]; + tensor var_3733 = const()[name = tensor("op_3733"), val = tensor([1, 20, 64, -1])]; + tensor var_3734_cast_fp16 = reshape(shape = var_3733, x = k_69_cast_fp16)[name = tensor("op_3734_cast_fp16")]; + tensor var_3735 = const()[name = tensor("op_3735"), val = tensor([1, 20, 64, -1])]; + tensor var_3736_cast_fp16 = reshape(shape = var_3735, x = v_69_cast_fp16)[name = tensor("op_3736_cast_fp16")]; + tensor attn_weights_137_transpose_x_0 = const()[name = tensor("attn_weights_137_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_137_transpose_y_0 = const()[name = tensor("attn_weights_137_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3732_cast_fp16, y = var_3734_cast_fp16)[name = tensor("attn_weights_137_cast_fp16")]; + tensor attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_139_cast_fp16")]; + tensor var_3740_cast_fp16 = softmax(axis = var_1180, x = attn_weights_139_cast_fp16)[name = tensor("op_3740_cast_fp16")]; + tensor attn_69_transpose_x_0 = const()[name = tensor("attn_69_transpose_x_0"), val = tensor(false)]; + tensor attn_69_transpose_y_0 = const()[name = tensor("attn_69_transpose_y_0"), val = tensor(true)]; + tensor attn_69_cast_fp16 = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3736_cast_fp16, y = var_3740_cast_fp16)[name = tensor("attn_69_cast_fp16")]; + tensor var_3744 = const()[name = tensor("op_3744"), val = tensor([1, 1280, 1, -1])]; + tensor input_257_cast_fp16 = reshape(shape = var_3744, x = attn_69_cast_fp16)[name = tensor("input_257_cast_fp16")]; + tensor var_3749 = const()[name = tensor("op_3749"), val = tensor([1, 1])]; + tensor var_3751 = const()[name = tensor("op_3751"), val = tensor([1, 1])]; + tensor var_3753_pad_type_0 = const()[name = tensor("op_3753_pad_type_0"), val = tensor("custom")]; + tensor var_3753_pad_0 = const()[name = tensor("op_3753_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(443043200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444272064))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444272256)))]; + tensor var_3753_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_3751, groups = var_1196, pad = var_3753_pad_0, pad_type = var_3753_pad_type_0, strides = var_3749, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_257_cast_fp16)[name = tensor("op_3753_cast_fp16")]; + tensor inputs_105_cast_fp16 = add(x = var_3753_cast_fp16, y = inputs_103_cast_fp16)[name = tensor("inputs_105_cast_fp16")]; + tensor hidden_states_157_axes_0 = const()[name = tensor("hidden_states_157_axes_0"), val = tensor([1])]; + tensor hidden_states_157_gamma_0_to_fp16 = const()[name = tensor("hidden_states_157_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444274880)))]; + tensor hidden_states_157_beta_0_to_fp16 = const()[name = tensor("hidden_states_157_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444277504)))]; + tensor var_3763_to_fp16 = const()[name = tensor("op_3763_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_157_cast_fp16 = layer_norm(axes = hidden_states_157_axes_0, beta = hidden_states_157_beta_0_to_fp16, epsilon = var_3763_to_fp16, gamma = hidden_states_157_gamma_0_to_fp16, x = inputs_105_cast_fp16)[name = tensor("hidden_states_157_cast_fp16")]; + tensor var_3778 = const()[name = tensor("op_3778"), val = tensor([1, 1])]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([1, 1])]; + tensor q_71_pad_type_0 = const()[name = tensor("q_71_pad_type_0"), val = tensor("custom")]; + tensor q_71_pad_0 = const()[name = tensor("q_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444280128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445508992))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_71_cast_fp16 = conv(dilations = var_3780, groups = var_1196, pad = q_71_pad_0, pad_type = q_71_pad_type_0, strides = var_3778, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_157_cast_fp16)[name = tensor("q_71_cast_fp16")]; + tensor var_3784 = const()[name = tensor("op_3784"), val = tensor([1, 1])]; + tensor var_3786 = const()[name = tensor("op_3786"), val = tensor([1, 1])]; + tensor k_71_pad_type_0 = const()[name = tensor("k_71_pad_type_0"), val = tensor("custom")]; + tensor k_71_pad_0 = const()[name = tensor("k_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445509184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(447475328))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_71_cast_fp16 = conv(dilations = var_3786, groups = var_1196, pad = k_71_pad_0, pad_type = k_71_pad_type_0, strides = var_3784, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_71_cast_fp16")]; + tensor var_3790 = const()[name = tensor("op_3790"), val = tensor([1, 1])]; + tensor var_3792 = const()[name = tensor("op_3792"), val = tensor([1, 1])]; + tensor v_71_pad_type_0 = const()[name = tensor("v_71_pad_type_0"), val = tensor("custom")]; + tensor v_71_pad_0 = const()[name = tensor("v_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(447475520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(449441664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_71_cast_fp16 = conv(dilations = var_3792, groups = var_1196, pad = v_71_pad_0, pad_type = v_71_pad_type_0, strides = var_3790, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_71_cast_fp16")]; + tensor var_3796 = const()[name = tensor("op_3796"), val = tensor([1, 20, 64, -1])]; + tensor var_3797_cast_fp16 = reshape(shape = var_3796, x = q_71_cast_fp16)[name = tensor("op_3797_cast_fp16")]; + tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([1, 20, 64, -1])]; + tensor var_3799_cast_fp16 = reshape(shape = var_3798, x = k_71_cast_fp16)[name = tensor("op_3799_cast_fp16")]; + tensor var_3800 = const()[name = tensor("op_3800"), val = tensor([1, 20, 64, -1])]; + tensor var_3801_cast_fp16 = reshape(shape = var_3800, x = v_71_cast_fp16)[name = tensor("op_3801_cast_fp16")]; + tensor attn_weights_141_transpose_x_0 = const()[name = tensor("attn_weights_141_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_141_transpose_y_0 = const()[name = tensor("attn_weights_141_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_141_cast_fp16 = matmul(transpose_x = attn_weights_141_transpose_x_0, transpose_y = attn_weights_141_transpose_y_0, x = var_3797_cast_fp16, y = var_3799_cast_fp16)[name = tensor("attn_weights_141_cast_fp16")]; + tensor attn_weights_143_cast_fp16 = mul(x = attn_weights_141_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_143_cast_fp16")]; + tensor var_3805_cast_fp16 = softmax(axis = var_1180, x = attn_weights_143_cast_fp16)[name = tensor("op_3805_cast_fp16")]; + tensor attn_71_transpose_x_0 = const()[name = tensor("attn_71_transpose_x_0"), val = tensor(false)]; + tensor attn_71_transpose_y_0 = const()[name = tensor("attn_71_transpose_y_0"), val = tensor(true)]; + tensor attn_71_cast_fp16 = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3801_cast_fp16, y = var_3805_cast_fp16)[name = tensor("attn_71_cast_fp16")]; + tensor var_3809 = const()[name = tensor("op_3809"), val = tensor([1, 1280, 1, -1])]; + tensor input_259_cast_fp16 = reshape(shape = var_3809, x = attn_71_cast_fp16)[name = tensor("input_259_cast_fp16")]; + 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 var_3818_pad_type_0 = const()[name = tensor("op_3818_pad_type_0"), val = tensor("custom")]; + tensor var_3818_pad_0 = const()[name = tensor("op_3818_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(449441856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450670720))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450670912)))]; + tensor var_3818_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_3816, groups = var_1196, pad = var_3818_pad_0, pad_type = var_3818_pad_type_0, strides = var_3814, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = tensor("op_3818_cast_fp16")]; + tensor inputs_107_cast_fp16 = add(x = var_3818_cast_fp16, y = inputs_105_cast_fp16)[name = tensor("inputs_107_cast_fp16")]; + tensor input_261_axes_0 = const()[name = tensor("input_261_axes_0"), val = tensor([1])]; + tensor input_261_gamma_0_to_fp16 = const()[name = tensor("input_261_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450673536)))]; + tensor input_261_beta_0_to_fp16 = const()[name = tensor("input_261_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450676160)))]; + tensor var_3828_to_fp16 = const()[name = tensor("op_3828_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_261_cast_fp16 = layer_norm(axes = input_261_axes_0, beta = input_261_beta_0_to_fp16, epsilon = var_3828_to_fp16, gamma = input_261_gamma_0_to_fp16, x = inputs_107_cast_fp16)[name = tensor("input_261_cast_fp16")]; + tensor var_3844 = const()[name = tensor("op_3844"), val = tensor([1, 1])]; + tensor var_3846 = const()[name = tensor("op_3846"), val = tensor([1, 1])]; + tensor var_3848_pad_type_0 = const()[name = tensor("op_3848_pad_type_0"), val = tensor("custom")]; + tensor var_3848_pad_0 = const()[name = tensor("op_3848_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450678784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460509248))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460509440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460517184))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3848_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3846, groups = var_1196, pad = var_3848_pad_0, pad_type = var_3848_pad_type_0, strides = var_3844, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_261_cast_fp16)[name = tensor("op_3848_cast_fp16")]; + tensor var_3849_split_sizes_0 = const()[name = tensor("op_3849_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3849_axis_0 = const()[name = tensor("op_3849_axis_0"), val = tensor(1)]; + tensor var_3849_cast_fp16_0, tensor var_3849_cast_fp16_1 = split(axis = var_3849_axis_0, split_sizes = var_3849_split_sizes_0, x = var_3848_cast_fp16)[name = tensor("op_3849_cast_fp16")]; + tensor var_3851_mode_0 = const()[name = tensor("op_3851_mode_0"), val = tensor("EXACT")]; + tensor var_3851_cast_fp16 = gelu(mode = var_3851_mode_0, x = var_3849_cast_fp16_1)[name = tensor("op_3851_cast_fp16")]; + tensor input_263_cast_fp16 = mul(x = var_3849_cast_fp16_0, y = var_3851_cast_fp16)[name = tensor("input_263_cast_fp16")]; + tensor var_3855 = const()[name = tensor("op_3855"), val = tensor([1, 1])]; + tensor var_3857 = const()[name = tensor("op_3857"), val = tensor([1, 1])]; + tensor var_3859_pad_type_0 = const()[name = tensor("op_3859_pad_type_0"), val = tensor("custom")]; + tensor var_3859_pad_0 = const()[name = tensor("op_3859_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460517376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465432640))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465432832)))]; + tensor var_3859_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_3857, groups = var_1196, pad = var_3859_pad_0, pad_type = var_3859_pad_type_0, strides = var_3855, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_263_cast_fp16)[name = tensor("op_3859_cast_fp16")]; + tensor inputs_109_cast_fp16 = add(x = var_3859_cast_fp16, y = inputs_107_cast_fp16)[name = tensor("inputs_109_cast_fp16")]; + tensor hidden_states_161_axes_0 = const()[name = tensor("hidden_states_161_axes_0"), val = tensor([1])]; + tensor hidden_states_161_gamma_0_to_fp16 = const()[name = tensor("hidden_states_161_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465435456)))]; + tensor hidden_states_161_beta_0_to_fp16 = const()[name = tensor("hidden_states_161_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465438080)))]; + tensor var_3875_to_fp16 = const()[name = tensor("op_3875_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_161_cast_fp16 = layer_norm(axes = hidden_states_161_axes_0, beta = hidden_states_161_beta_0_to_fp16, epsilon = var_3875_to_fp16, gamma = hidden_states_161_gamma_0_to_fp16, x = inputs_109_cast_fp16)[name = tensor("hidden_states_161_cast_fp16")]; + tensor var_3890 = const()[name = tensor("op_3890"), val = tensor([1, 1])]; + tensor var_3892 = const()[name = tensor("op_3892"), val = tensor([1, 1])]; + tensor q_73_pad_type_0 = const()[name = tensor("q_73_pad_type_0"), val = tensor("custom")]; + tensor q_73_pad_0 = const()[name = tensor("q_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465440704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(466669568))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_73_cast_fp16 = conv(dilations = var_3892, groups = var_1196, pad = q_73_pad_0, pad_type = q_73_pad_type_0, strides = var_3890, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = tensor("q_73_cast_fp16")]; + tensor var_3896 = const()[name = tensor("op_3896"), val = tensor([1, 1])]; + tensor var_3898 = const()[name = tensor("op_3898"), 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 down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(466669760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467898624))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_73_cast_fp16 = conv(dilations = var_3898, groups = var_1196, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_3896, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = tensor("k_73_cast_fp16")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor var_3904 = const()[name = tensor("op_3904"), val = tensor([1, 1])]; + tensor v_73_pad_type_0 = const()[name = tensor("v_73_pad_type_0"), val = tensor("custom")]; + tensor v_73_pad_0 = const()[name = tensor("v_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467898816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469127680))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_73_cast_fp16 = conv(dilations = var_3904, groups = var_1196, pad = v_73_pad_0, pad_type = v_73_pad_type_0, strides = var_3902, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = tensor("v_73_cast_fp16")]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([1, 20, 64, -1])]; + tensor var_3909_cast_fp16 = reshape(shape = var_3908, x = q_73_cast_fp16)[name = tensor("op_3909_cast_fp16")]; + tensor var_3910 = const()[name = tensor("op_3910"), val = tensor([1, 20, 64, -1])]; + tensor var_3911_cast_fp16 = reshape(shape = var_3910, x = k_73_cast_fp16)[name = tensor("op_3911_cast_fp16")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([1, 20, 64, -1])]; + tensor var_3913_cast_fp16 = reshape(shape = var_3912, x = v_73_cast_fp16)[name = tensor("op_3913_cast_fp16")]; + tensor attn_weights_145_transpose_x_0 = const()[name = tensor("attn_weights_145_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_145_transpose_y_0 = const()[name = tensor("attn_weights_145_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3909_cast_fp16, y = var_3911_cast_fp16)[name = tensor("attn_weights_145_cast_fp16")]; + tensor attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_147_cast_fp16")]; + tensor var_3917_cast_fp16 = softmax(axis = var_1180, x = attn_weights_147_cast_fp16)[name = tensor("op_3917_cast_fp16")]; + tensor attn_73_transpose_x_0 = const()[name = tensor("attn_73_transpose_x_0"), val = tensor(false)]; + tensor attn_73_transpose_y_0 = const()[name = tensor("attn_73_transpose_y_0"), val = tensor(true)]; + tensor attn_73_cast_fp16 = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_3913_cast_fp16, y = var_3917_cast_fp16)[name = tensor("attn_73_cast_fp16")]; + tensor var_3921 = const()[name = tensor("op_3921"), val = tensor([1, 1280, 1, -1])]; + tensor input_265_cast_fp16 = reshape(shape = var_3921, x = attn_73_cast_fp16)[name = tensor("input_265_cast_fp16")]; + tensor var_3926 = const()[name = tensor("op_3926"), val = tensor([1, 1])]; + tensor var_3928 = const()[name = tensor("op_3928"), val = tensor([1, 1])]; + tensor var_3930_pad_type_0 = const()[name = tensor("op_3930_pad_type_0"), val = tensor("custom")]; + tensor var_3930_pad_0 = const()[name = tensor("op_3930_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469127872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470356736))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470356928)))]; + tensor var_3930_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_3928, groups = var_1196, pad = var_3930_pad_0, pad_type = var_3930_pad_type_0, strides = var_3926, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_265_cast_fp16)[name = tensor("op_3930_cast_fp16")]; + tensor inputs_111_cast_fp16 = add(x = var_3930_cast_fp16, y = inputs_109_cast_fp16)[name = tensor("inputs_111_cast_fp16")]; + tensor hidden_states_163_axes_0 = const()[name = tensor("hidden_states_163_axes_0"), val = tensor([1])]; + tensor hidden_states_163_gamma_0_to_fp16 = const()[name = tensor("hidden_states_163_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470359552)))]; + tensor hidden_states_163_beta_0_to_fp16 = const()[name = tensor("hidden_states_163_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470362176)))]; + tensor var_3940_to_fp16 = const()[name = tensor("op_3940_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_163_cast_fp16 = layer_norm(axes = hidden_states_163_axes_0, beta = hidden_states_163_beta_0_to_fp16, epsilon = var_3940_to_fp16, gamma = hidden_states_163_gamma_0_to_fp16, x = inputs_111_cast_fp16)[name = tensor("hidden_states_163_cast_fp16")]; + tensor var_3955 = const()[name = tensor("op_3955"), val = tensor([1, 1])]; + tensor var_3957 = const()[name = tensor("op_3957"), val = tensor([1, 1])]; + tensor q_75_pad_type_0 = const()[name = tensor("q_75_pad_type_0"), val = tensor("custom")]; + tensor q_75_pad_0 = const()[name = tensor("q_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470364800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(471593664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_75_cast_fp16 = conv(dilations = var_3957, groups = var_1196, pad = q_75_pad_0, pad_type = q_75_pad_type_0, strides = var_3955, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_163_cast_fp16)[name = tensor("q_75_cast_fp16")]; + tensor var_3961 = const()[name = tensor("op_3961"), val = tensor([1, 1])]; + tensor var_3963 = const()[name = tensor("op_3963"), val = tensor([1, 1])]; + tensor k_75_pad_type_0 = const()[name = tensor("k_75_pad_type_0"), val = tensor("custom")]; + tensor k_75_pad_0 = const()[name = tensor("k_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(471593856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(473560000))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_75_cast_fp16 = conv(dilations = var_3963, groups = var_1196, pad = k_75_pad_0, pad_type = k_75_pad_type_0, strides = var_3961, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_75_cast_fp16")]; + tensor var_3967 = const()[name = tensor("op_3967"), val = tensor([1, 1])]; + tensor var_3969 = const()[name = tensor("op_3969"), val = tensor([1, 1])]; + tensor v_75_pad_type_0 = const()[name = tensor("v_75_pad_type_0"), val = tensor("custom")]; + tensor v_75_pad_0 = const()[name = tensor("v_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(473560192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475526336))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_75_cast_fp16 = conv(dilations = var_3969, groups = var_1196, pad = v_75_pad_0, pad_type = v_75_pad_type_0, strides = var_3967, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_75_cast_fp16")]; + tensor var_3973 = const()[name = tensor("op_3973"), val = tensor([1, 20, 64, -1])]; + tensor var_3974_cast_fp16 = reshape(shape = var_3973, x = q_75_cast_fp16)[name = tensor("op_3974_cast_fp16")]; + tensor var_3975 = const()[name = tensor("op_3975"), val = tensor([1, 20, 64, -1])]; + tensor var_3976_cast_fp16 = reshape(shape = var_3975, x = k_75_cast_fp16)[name = tensor("op_3976_cast_fp16")]; + tensor var_3977 = const()[name = tensor("op_3977"), val = tensor([1, 20, 64, -1])]; + tensor var_3978_cast_fp16 = reshape(shape = var_3977, x = v_75_cast_fp16)[name = tensor("op_3978_cast_fp16")]; + tensor attn_weights_149_transpose_x_0 = const()[name = tensor("attn_weights_149_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_149_transpose_y_0 = const()[name = tensor("attn_weights_149_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_149_cast_fp16 = matmul(transpose_x = attn_weights_149_transpose_x_0, transpose_y = attn_weights_149_transpose_y_0, x = var_3974_cast_fp16, y = var_3976_cast_fp16)[name = tensor("attn_weights_149_cast_fp16")]; + tensor attn_weights_151_cast_fp16 = mul(x = attn_weights_149_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_151_cast_fp16")]; + tensor var_3982_cast_fp16 = softmax(axis = var_1180, x = attn_weights_151_cast_fp16)[name = tensor("op_3982_cast_fp16")]; + tensor attn_75_transpose_x_0 = const()[name = tensor("attn_75_transpose_x_0"), val = tensor(false)]; + tensor attn_75_transpose_y_0 = const()[name = tensor("attn_75_transpose_y_0"), val = tensor(true)]; + tensor attn_75_cast_fp16 = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_3978_cast_fp16, y = var_3982_cast_fp16)[name = tensor("attn_75_cast_fp16")]; + tensor var_3986 = const()[name = tensor("op_3986"), val = tensor([1, 1280, 1, -1])]; + tensor input_267_cast_fp16 = reshape(shape = var_3986, x = attn_75_cast_fp16)[name = tensor("input_267_cast_fp16")]; + tensor var_3991 = const()[name = tensor("op_3991"), val = tensor([1, 1])]; + tensor var_3993 = const()[name = tensor("op_3993"), val = tensor([1, 1])]; + tensor var_3995_pad_type_0 = const()[name = tensor("op_3995_pad_type_0"), val = tensor("custom")]; + tensor var_3995_pad_0 = const()[name = tensor("op_3995_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475526528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476755392))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476755584)))]; + tensor var_3995_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_3993, groups = var_1196, pad = var_3995_pad_0, pad_type = var_3995_pad_type_0, strides = var_3991, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = tensor("op_3995_cast_fp16")]; + tensor inputs_113_cast_fp16 = add(x = var_3995_cast_fp16, y = inputs_111_cast_fp16)[name = tensor("inputs_113_cast_fp16")]; + tensor input_269_axes_0 = const()[name = tensor("input_269_axes_0"), val = tensor([1])]; + tensor input_269_gamma_0_to_fp16 = const()[name = tensor("input_269_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476758208)))]; + tensor input_269_beta_0_to_fp16 = const()[name = tensor("input_269_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476760832)))]; + tensor var_4005_to_fp16 = const()[name = tensor("op_4005_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_269_cast_fp16 = layer_norm(axes = input_269_axes_0, beta = input_269_beta_0_to_fp16, epsilon = var_4005_to_fp16, gamma = input_269_gamma_0_to_fp16, x = inputs_113_cast_fp16)[name = tensor("input_269_cast_fp16")]; + tensor var_4021 = const()[name = tensor("op_4021"), val = tensor([1, 1])]; + tensor var_4023 = const()[name = tensor("op_4023"), val = tensor([1, 1])]; + tensor var_4025_pad_type_0 = const()[name = tensor("op_4025_pad_type_0"), val = tensor("custom")]; + tensor var_4025_pad_0 = const()[name = tensor("op_4025_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476763456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486593920))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486594112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486601856))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4025_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4023, groups = var_1196, pad = var_4025_pad_0, pad_type = var_4025_pad_type_0, strides = var_4021, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_269_cast_fp16)[name = tensor("op_4025_cast_fp16")]; + tensor var_4026_split_sizes_0 = const()[name = tensor("op_4026_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4026_axis_0 = const()[name = tensor("op_4026_axis_0"), val = tensor(1)]; + tensor var_4026_cast_fp16_0, tensor var_4026_cast_fp16_1 = split(axis = var_4026_axis_0, split_sizes = var_4026_split_sizes_0, x = var_4025_cast_fp16)[name = tensor("op_4026_cast_fp16")]; + tensor var_4028_mode_0 = const()[name = tensor("op_4028_mode_0"), val = tensor("EXACT")]; + tensor var_4028_cast_fp16 = gelu(mode = var_4028_mode_0, x = var_4026_cast_fp16_1)[name = tensor("op_4028_cast_fp16")]; + tensor input_271_cast_fp16 = mul(x = var_4026_cast_fp16_0, y = var_4028_cast_fp16)[name = tensor("input_271_cast_fp16")]; + tensor var_4032 = const()[name = tensor("op_4032"), val = tensor([1, 1])]; + tensor var_4034 = const()[name = tensor("op_4034"), val = tensor([1, 1])]; + tensor var_4036_pad_type_0 = const()[name = tensor("op_4036_pad_type_0"), val = tensor("custom")]; + tensor var_4036_pad_0 = const()[name = tensor("op_4036_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486602048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491517312))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491517504)))]; + tensor var_4036_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_4034, groups = var_1196, pad = var_4036_pad_0, pad_type = var_4036_pad_type_0, strides = var_4032, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_271_cast_fp16)[name = tensor("op_4036_cast_fp16")]; + tensor inputs_115_cast_fp16 = add(x = var_4036_cast_fp16, y = inputs_113_cast_fp16)[name = tensor("inputs_115_cast_fp16")]; + tensor hidden_states_167_axes_0 = const()[name = tensor("hidden_states_167_axes_0"), val = tensor([1])]; + tensor hidden_states_167_gamma_0_to_fp16 = const()[name = tensor("hidden_states_167_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491520128)))]; + tensor hidden_states_167_beta_0_to_fp16 = const()[name = tensor("hidden_states_167_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491522752)))]; + tensor var_4052_to_fp16 = const()[name = tensor("op_4052_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_167_cast_fp16 = layer_norm(axes = hidden_states_167_axes_0, beta = hidden_states_167_beta_0_to_fp16, epsilon = var_4052_to_fp16, gamma = hidden_states_167_gamma_0_to_fp16, x = inputs_115_cast_fp16)[name = tensor("hidden_states_167_cast_fp16")]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor var_4069 = const()[name = tensor("op_4069"), val = tensor([1, 1])]; + tensor q_77_pad_type_0 = const()[name = tensor("q_77_pad_type_0"), val = tensor("custom")]; + tensor q_77_pad_0 = const()[name = tensor("q_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491525376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492754240))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_77_cast_fp16 = conv(dilations = var_4069, groups = var_1196, pad = q_77_pad_0, pad_type = q_77_pad_type_0, strides = var_4067, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("q_77_cast_fp16")]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor var_4075 = const()[name = tensor("op_4075"), 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 down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492754432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493983296))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_77_cast_fp16 = conv(dilations = var_4075, groups = var_1196, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_4073, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("k_77_cast_fp16")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([1, 1])]; + tensor var_4081 = const()[name = tensor("op_4081"), val = tensor([1, 1])]; + tensor v_77_pad_type_0 = const()[name = tensor("v_77_pad_type_0"), val = tensor("custom")]; + tensor v_77_pad_0 = const()[name = tensor("v_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493983488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(495212352))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_77_cast_fp16 = conv(dilations = var_4081, groups = var_1196, pad = v_77_pad_0, pad_type = v_77_pad_type_0, strides = var_4079, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("v_77_cast_fp16")]; + tensor var_4085 = const()[name = tensor("op_4085"), val = tensor([1, 20, 64, -1])]; + tensor var_4086_cast_fp16 = reshape(shape = var_4085, x = q_77_cast_fp16)[name = tensor("op_4086_cast_fp16")]; + tensor var_4087 = const()[name = tensor("op_4087"), val = tensor([1, 20, 64, -1])]; + tensor var_4088_cast_fp16 = reshape(shape = var_4087, x = k_77_cast_fp16)[name = tensor("op_4088_cast_fp16")]; + tensor var_4089 = const()[name = tensor("op_4089"), val = tensor([1, 20, 64, -1])]; + tensor var_4090_cast_fp16 = reshape(shape = var_4089, x = v_77_cast_fp16)[name = tensor("op_4090_cast_fp16")]; + tensor attn_weights_153_transpose_x_0 = const()[name = tensor("attn_weights_153_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_153_transpose_y_0 = const()[name = tensor("attn_weights_153_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_4086_cast_fp16, y = var_4088_cast_fp16)[name = tensor("attn_weights_153_cast_fp16")]; + tensor attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_155_cast_fp16")]; + tensor var_4094_cast_fp16 = softmax(axis = var_1180, x = attn_weights_155_cast_fp16)[name = tensor("op_4094_cast_fp16")]; + tensor attn_77_transpose_x_0 = const()[name = tensor("attn_77_transpose_x_0"), val = tensor(false)]; + tensor attn_77_transpose_y_0 = const()[name = tensor("attn_77_transpose_y_0"), val = tensor(true)]; + tensor attn_77_cast_fp16 = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4090_cast_fp16, y = var_4094_cast_fp16)[name = tensor("attn_77_cast_fp16")]; + tensor var_4098 = const()[name = tensor("op_4098"), val = tensor([1, 1280, 1, -1])]; + tensor input_273_cast_fp16 = reshape(shape = var_4098, x = attn_77_cast_fp16)[name = tensor("input_273_cast_fp16")]; + tensor var_4103 = const()[name = tensor("op_4103"), val = tensor([1, 1])]; + tensor var_4105 = const()[name = tensor("op_4105"), val = tensor([1, 1])]; + tensor var_4107_pad_type_0 = const()[name = tensor("op_4107_pad_type_0"), val = tensor("custom")]; + tensor var_4107_pad_0 = const()[name = tensor("op_4107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(495212544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496441408))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496441600)))]; + tensor var_4107_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_4105, groups = var_1196, pad = var_4107_pad_0, pad_type = var_4107_pad_type_0, strides = var_4103, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = tensor("op_4107_cast_fp16")]; + tensor inputs_117_cast_fp16 = add(x = var_4107_cast_fp16, y = inputs_115_cast_fp16)[name = tensor("inputs_117_cast_fp16")]; + tensor hidden_states_169_axes_0 = const()[name = tensor("hidden_states_169_axes_0"), val = tensor([1])]; + tensor hidden_states_169_gamma_0_to_fp16 = const()[name = tensor("hidden_states_169_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496444224)))]; + tensor hidden_states_169_beta_0_to_fp16 = const()[name = tensor("hidden_states_169_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496446848)))]; + tensor var_4117_to_fp16 = const()[name = tensor("op_4117_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_169_cast_fp16 = layer_norm(axes = hidden_states_169_axes_0, beta = hidden_states_169_beta_0_to_fp16, epsilon = var_4117_to_fp16, gamma = hidden_states_169_gamma_0_to_fp16, x = inputs_117_cast_fp16)[name = tensor("hidden_states_169_cast_fp16")]; + tensor var_4132 = const()[name = tensor("op_4132"), val = tensor([1, 1])]; + tensor var_4134 = const()[name = tensor("op_4134"), val = tensor([1, 1])]; + tensor q_79_pad_type_0 = const()[name = tensor("q_79_pad_type_0"), val = tensor("custom")]; + tensor q_79_pad_0 = const()[name = tensor("q_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496449472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(497678336))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_79_cast_fp16 = conv(dilations = var_4134, groups = var_1196, pad = q_79_pad_0, pad_type = q_79_pad_type_0, strides = var_4132, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_169_cast_fp16)[name = tensor("q_79_cast_fp16")]; + tensor var_4138 = const()[name = tensor("op_4138"), val = tensor([1, 1])]; + tensor var_4140 = const()[name = tensor("op_4140"), val = tensor([1, 1])]; + tensor k_79_pad_type_0 = const()[name = tensor("k_79_pad_type_0"), val = tensor("custom")]; + tensor k_79_pad_0 = const()[name = tensor("k_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(497678528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(499644672))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_79_cast_fp16 = conv(dilations = var_4140, groups = var_1196, pad = k_79_pad_0, pad_type = k_79_pad_type_0, strides = var_4138, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_79_cast_fp16")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([1, 1])]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor([1, 1])]; + tensor v_79_pad_type_0 = const()[name = tensor("v_79_pad_type_0"), val = tensor("custom")]; + tensor v_79_pad_0 = const()[name = tensor("v_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(499644864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(501611008))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_79_cast_fp16 = conv(dilations = var_4146, groups = var_1196, pad = v_79_pad_0, pad_type = v_79_pad_type_0, strides = var_4144, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_79_cast_fp16")]; + tensor var_4150 = const()[name = tensor("op_4150"), val = tensor([1, 20, 64, -1])]; + tensor var_4151_cast_fp16 = reshape(shape = var_4150, x = q_79_cast_fp16)[name = tensor("op_4151_cast_fp16")]; + tensor var_4152 = const()[name = tensor("op_4152"), val = tensor([1, 20, 64, -1])]; + tensor var_4153_cast_fp16 = reshape(shape = var_4152, x = k_79_cast_fp16)[name = tensor("op_4153_cast_fp16")]; + tensor var_4154 = const()[name = tensor("op_4154"), val = tensor([1, 20, 64, -1])]; + tensor var_4155_cast_fp16 = reshape(shape = var_4154, x = v_79_cast_fp16)[name = tensor("op_4155_cast_fp16")]; + tensor attn_weights_157_transpose_x_0 = const()[name = tensor("attn_weights_157_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_157_transpose_y_0 = const()[name = tensor("attn_weights_157_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_157_cast_fp16 = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = var_4151_cast_fp16, y = var_4153_cast_fp16)[name = tensor("attn_weights_157_cast_fp16")]; + tensor attn_weights_159_cast_fp16 = mul(x = attn_weights_157_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_159_cast_fp16")]; + tensor var_4159_cast_fp16 = softmax(axis = var_1180, x = attn_weights_159_cast_fp16)[name = tensor("op_4159_cast_fp16")]; + tensor attn_79_transpose_x_0 = const()[name = tensor("attn_79_transpose_x_0"), val = tensor(false)]; + tensor attn_79_transpose_y_0 = const()[name = tensor("attn_79_transpose_y_0"), val = tensor(true)]; + tensor attn_79_cast_fp16 = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4155_cast_fp16, y = var_4159_cast_fp16)[name = tensor("attn_79_cast_fp16")]; + tensor var_4163 = const()[name = tensor("op_4163"), val = tensor([1, 1280, 1, -1])]; + tensor input_275_cast_fp16 = reshape(shape = var_4163, x = attn_79_cast_fp16)[name = tensor("input_275_cast_fp16")]; + 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 var_4172_pad_type_0 = const()[name = tensor("op_4172_pad_type_0"), val = tensor("custom")]; + tensor var_4172_pad_0 = const()[name = tensor("op_4172_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(501611200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502840064))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502840256)))]; + tensor var_4172_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_4170, groups = var_1196, pad = var_4172_pad_0, pad_type = var_4172_pad_type_0, strides = var_4168, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = tensor("op_4172_cast_fp16")]; + tensor inputs_119_cast_fp16 = add(x = var_4172_cast_fp16, y = inputs_117_cast_fp16)[name = tensor("inputs_119_cast_fp16")]; + tensor input_277_axes_0 = const()[name = tensor("input_277_axes_0"), val = tensor([1])]; + tensor input_277_gamma_0_to_fp16 = const()[name = tensor("input_277_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502842880)))]; + tensor input_277_beta_0_to_fp16 = const()[name = tensor("input_277_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502845504)))]; + tensor var_4182_to_fp16 = const()[name = tensor("op_4182_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_277_cast_fp16 = layer_norm(axes = input_277_axes_0, beta = input_277_beta_0_to_fp16, epsilon = var_4182_to_fp16, gamma = input_277_gamma_0_to_fp16, x = inputs_119_cast_fp16)[name = tensor("input_277_cast_fp16")]; + tensor var_4198 = const()[name = tensor("op_4198"), val = tensor([1, 1])]; + tensor var_4200 = const()[name = tensor("op_4200"), val = tensor([1, 1])]; + tensor var_4202_pad_type_0 = const()[name = tensor("op_4202_pad_type_0"), val = tensor("custom")]; + tensor var_4202_pad_0 = const()[name = tensor("op_4202_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502848128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512678592))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512678784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512686528))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4202_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4200, groups = var_1196, pad = var_4202_pad_0, pad_type = var_4202_pad_type_0, strides = var_4198, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = tensor("op_4202_cast_fp16")]; + tensor var_4203_split_sizes_0 = const()[name = tensor("op_4203_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4203_axis_0 = const()[name = tensor("op_4203_axis_0"), val = tensor(1)]; + tensor var_4203_cast_fp16_0, tensor var_4203_cast_fp16_1 = split(axis = var_4203_axis_0, split_sizes = var_4203_split_sizes_0, x = var_4202_cast_fp16)[name = tensor("op_4203_cast_fp16")]; + tensor var_4205_mode_0 = const()[name = tensor("op_4205_mode_0"), val = tensor("EXACT")]; + tensor var_4205_cast_fp16 = gelu(mode = var_4205_mode_0, x = var_4203_cast_fp16_1)[name = tensor("op_4205_cast_fp16")]; + tensor input_279_cast_fp16 = mul(x = var_4203_cast_fp16_0, y = var_4205_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor var_4209 = const()[name = tensor("op_4209"), val = tensor([1, 1])]; + tensor var_4211 = const()[name = tensor("op_4211"), val = tensor([1, 1])]; + tensor var_4213_pad_type_0 = const()[name = tensor("op_4213_pad_type_0"), val = tensor("custom")]; + tensor var_4213_pad_0 = const()[name = tensor("op_4213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512686720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517601984))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517602176)))]; + tensor var_4213_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_4211, groups = var_1196, pad = var_4213_pad_0, pad_type = var_4213_pad_type_0, strides = var_4209, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = tensor("op_4213_cast_fp16")]; + tensor inputs_121_cast_fp16 = add(x = var_4213_cast_fp16, y = inputs_119_cast_fp16)[name = tensor("inputs_121_cast_fp16")]; + tensor hidden_states_173_axes_0 = const()[name = tensor("hidden_states_173_axes_0"), val = tensor([1])]; + tensor hidden_states_173_gamma_0_to_fp16 = const()[name = tensor("hidden_states_173_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517604800)))]; + tensor hidden_states_173_beta_0_to_fp16 = const()[name = tensor("hidden_states_173_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517607424)))]; + tensor var_4229_to_fp16 = const()[name = tensor("op_4229_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_173_cast_fp16 = layer_norm(axes = hidden_states_173_axes_0, beta = hidden_states_173_beta_0_to_fp16, epsilon = var_4229_to_fp16, gamma = hidden_states_173_gamma_0_to_fp16, x = inputs_121_cast_fp16)[name = tensor("hidden_states_173_cast_fp16")]; + tensor var_4244 = const()[name = tensor("op_4244"), val = tensor([1, 1])]; + tensor var_4246 = const()[name = tensor("op_4246"), val = tensor([1, 1])]; + tensor q_81_pad_type_0 = const()[name = tensor("q_81_pad_type_0"), val = tensor("custom")]; + tensor q_81_pad_0 = const()[name = tensor("q_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517610048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(518838912))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_81_cast_fp16 = conv(dilations = var_4246, groups = var_1196, pad = q_81_pad_0, pad_type = q_81_pad_type_0, strides = var_4244, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_173_cast_fp16)[name = tensor("q_81_cast_fp16")]; + tensor var_4250 = const()[name = tensor("op_4250"), val = tensor([1, 1])]; + tensor var_4252 = const()[name = tensor("op_4252"), 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 down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(518839104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520067968))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_81_cast_fp16 = conv(dilations = var_4252, groups = var_1196, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_4250, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_173_cast_fp16)[name = tensor("k_81_cast_fp16")]; + tensor var_4256 = const()[name = tensor("op_4256"), val = tensor([1, 1])]; + tensor var_4258 = const()[name = tensor("op_4258"), val = tensor([1, 1])]; + tensor v_81_pad_type_0 = const()[name = tensor("v_81_pad_type_0"), val = tensor("custom")]; + tensor v_81_pad_0 = const()[name = tensor("v_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520068160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521297024))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_81_cast_fp16 = conv(dilations = var_4258, groups = var_1196, pad = v_81_pad_0, pad_type = v_81_pad_type_0, strides = var_4256, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_173_cast_fp16)[name = tensor("v_81_cast_fp16")]; + tensor var_4262 = const()[name = tensor("op_4262"), val = tensor([1, 20, 64, -1])]; + tensor var_4263_cast_fp16 = reshape(shape = var_4262, x = q_81_cast_fp16)[name = tensor("op_4263_cast_fp16")]; + tensor var_4264 = const()[name = tensor("op_4264"), val = tensor([1, 20, 64, -1])]; + tensor var_4265_cast_fp16 = reshape(shape = var_4264, x = k_81_cast_fp16)[name = tensor("op_4265_cast_fp16")]; + tensor var_4266 = const()[name = tensor("op_4266"), val = tensor([1, 20, 64, -1])]; + tensor var_4267_cast_fp16 = reshape(shape = var_4266, x = v_81_cast_fp16)[name = tensor("op_4267_cast_fp16")]; + tensor attn_weights_161_transpose_x_0 = const()[name = tensor("attn_weights_161_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_161_transpose_y_0 = const()[name = tensor("attn_weights_161_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4263_cast_fp16, y = var_4265_cast_fp16)[name = tensor("attn_weights_161_cast_fp16")]; + tensor attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_163_cast_fp16")]; + tensor var_4271_cast_fp16 = softmax(axis = var_1180, x = attn_weights_163_cast_fp16)[name = tensor("op_4271_cast_fp16")]; + tensor attn_81_transpose_x_0 = const()[name = tensor("attn_81_transpose_x_0"), val = tensor(false)]; + tensor attn_81_transpose_y_0 = const()[name = tensor("attn_81_transpose_y_0"), val = tensor(true)]; + tensor attn_81_cast_fp16 = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4267_cast_fp16, y = var_4271_cast_fp16)[name = tensor("attn_81_cast_fp16")]; + tensor var_4275 = const()[name = tensor("op_4275"), val = tensor([1, 1280, 1, -1])]; + tensor input_281_cast_fp16 = reshape(shape = var_4275, x = attn_81_cast_fp16)[name = tensor("input_281_cast_fp16")]; + tensor var_4280 = const()[name = tensor("op_4280"), val = tensor([1, 1])]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1, 1])]; + tensor var_4284_pad_type_0 = const()[name = tensor("op_4284_pad_type_0"), val = tensor("custom")]; + tensor var_4284_pad_0 = const()[name = tensor("op_4284_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521297216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522526080))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522526272)))]; + tensor var_4284_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_4282, groups = var_1196, pad = var_4284_pad_0, pad_type = var_4284_pad_type_0, strides = var_4280, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_281_cast_fp16)[name = tensor("op_4284_cast_fp16")]; + tensor inputs_123_cast_fp16 = add(x = var_4284_cast_fp16, y = inputs_121_cast_fp16)[name = tensor("inputs_123_cast_fp16")]; + tensor hidden_states_175_axes_0 = const()[name = tensor("hidden_states_175_axes_0"), val = tensor([1])]; + tensor hidden_states_175_gamma_0_to_fp16 = const()[name = tensor("hidden_states_175_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522528896)))]; + tensor hidden_states_175_beta_0_to_fp16 = const()[name = tensor("hidden_states_175_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522531520)))]; + tensor var_4294_to_fp16 = const()[name = tensor("op_4294_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_175_cast_fp16 = layer_norm(axes = hidden_states_175_axes_0, beta = hidden_states_175_beta_0_to_fp16, epsilon = var_4294_to_fp16, gamma = hidden_states_175_gamma_0_to_fp16, x = inputs_123_cast_fp16)[name = tensor("hidden_states_175_cast_fp16")]; + 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 q_83_pad_type_0 = const()[name = tensor("q_83_pad_type_0"), val = tensor("custom")]; + tensor q_83_pad_0 = const()[name = tensor("q_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522534144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(523763008))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_83_cast_fp16 = conv(dilations = var_4311, groups = var_1196, pad = q_83_pad_0, pad_type = q_83_pad_type_0, strides = var_4309, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_175_cast_fp16)[name = tensor("q_83_cast_fp16")]; + tensor var_4315 = const()[name = tensor("op_4315"), val = tensor([1, 1])]; + tensor var_4317 = const()[name = tensor("op_4317"), val = tensor([1, 1])]; + tensor k_83_pad_type_0 = const()[name = tensor("k_83_pad_type_0"), val = tensor("custom")]; + tensor k_83_pad_0 = const()[name = tensor("k_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(523763200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(525729344))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_83_cast_fp16 = conv(dilations = var_4317, groups = var_1196, pad = k_83_pad_0, pad_type = k_83_pad_type_0, strides = var_4315, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_83_cast_fp16")]; + 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 v_83_pad_type_0 = const()[name = tensor("v_83_pad_type_0"), val = tensor("custom")]; + tensor v_83_pad_0 = const()[name = tensor("v_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(525729536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527695680))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_83_cast_fp16 = conv(dilations = var_4323, groups = var_1196, pad = v_83_pad_0, pad_type = v_83_pad_type_0, strides = var_4321, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_83_cast_fp16")]; + tensor var_4327 = const()[name = tensor("op_4327"), val = tensor([1, 20, 64, -1])]; + tensor var_4328_cast_fp16 = reshape(shape = var_4327, x = q_83_cast_fp16)[name = tensor("op_4328_cast_fp16")]; + tensor var_4329 = const()[name = tensor("op_4329"), val = tensor([1, 20, 64, -1])]; + tensor var_4330_cast_fp16 = reshape(shape = var_4329, x = k_83_cast_fp16)[name = tensor("op_4330_cast_fp16")]; + tensor var_4331 = const()[name = tensor("op_4331"), val = tensor([1, 20, 64, -1])]; + tensor var_4332_cast_fp16 = reshape(shape = var_4331, x = v_83_cast_fp16)[name = tensor("op_4332_cast_fp16")]; + tensor attn_weights_165_transpose_x_0 = const()[name = tensor("attn_weights_165_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_165_transpose_y_0 = const()[name = tensor("attn_weights_165_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_165_cast_fp16 = matmul(transpose_x = attn_weights_165_transpose_x_0, transpose_y = attn_weights_165_transpose_y_0, x = var_4328_cast_fp16, y = var_4330_cast_fp16)[name = tensor("attn_weights_165_cast_fp16")]; + tensor attn_weights_167_cast_fp16 = mul(x = attn_weights_165_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_167_cast_fp16")]; + tensor var_4336_cast_fp16 = softmax(axis = var_1180, x = attn_weights_167_cast_fp16)[name = tensor("op_4336_cast_fp16")]; + tensor attn_83_transpose_x_0 = const()[name = tensor("attn_83_transpose_x_0"), val = tensor(false)]; + tensor attn_83_transpose_y_0 = const()[name = tensor("attn_83_transpose_y_0"), val = tensor(true)]; + tensor attn_83_cast_fp16 = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4332_cast_fp16, y = var_4336_cast_fp16)[name = tensor("attn_83_cast_fp16")]; + tensor var_4340 = const()[name = tensor("op_4340"), val = tensor([1, 1280, 1, -1])]; + tensor input_283_cast_fp16 = reshape(shape = var_4340, x = attn_83_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor var_4345 = const()[name = tensor("op_4345"), val = tensor([1, 1])]; + tensor var_4347 = const()[name = tensor("op_4347"), val = tensor([1, 1])]; + tensor var_4349_pad_type_0 = const()[name = tensor("op_4349_pad_type_0"), val = tensor("custom")]; + tensor var_4349_pad_0 = const()[name = tensor("op_4349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527695872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528924736))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528924928)))]; + tensor var_4349_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_4347, groups = var_1196, pad = var_4349_pad_0, pad_type = var_4349_pad_type_0, strides = var_4345, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = tensor("op_4349_cast_fp16")]; + tensor inputs_125_cast_fp16 = add(x = var_4349_cast_fp16, y = inputs_123_cast_fp16)[name = tensor("inputs_125_cast_fp16")]; + tensor input_285_axes_0 = const()[name = tensor("input_285_axes_0"), val = tensor([1])]; + tensor input_285_gamma_0_to_fp16 = const()[name = tensor("input_285_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528927552)))]; + tensor input_285_beta_0_to_fp16 = const()[name = tensor("input_285_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528930176)))]; + tensor var_4359_to_fp16 = const()[name = tensor("op_4359_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_285_cast_fp16 = layer_norm(axes = input_285_axes_0, beta = input_285_beta_0_to_fp16, epsilon = var_4359_to_fp16, gamma = input_285_gamma_0_to_fp16, x = inputs_125_cast_fp16)[name = tensor("input_285_cast_fp16")]; + tensor var_4375 = const()[name = tensor("op_4375"), val = tensor([1, 1])]; + tensor var_4377 = const()[name = tensor("op_4377"), val = tensor([1, 1])]; + tensor var_4379_pad_type_0 = const()[name = tensor("op_4379_pad_type_0"), val = tensor("custom")]; + tensor var_4379_pad_0 = const()[name = tensor("op_4379_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528932800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538763264))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538763456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538771200))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4379_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4377, groups = var_1196, pad = var_4379_pad_0, pad_type = var_4379_pad_type_0, strides = var_4375, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_285_cast_fp16)[name = tensor("op_4379_cast_fp16")]; + tensor var_4380_split_sizes_0 = const()[name = tensor("op_4380_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4380_axis_0 = const()[name = tensor("op_4380_axis_0"), val = tensor(1)]; + tensor var_4380_cast_fp16_0, tensor var_4380_cast_fp16_1 = split(axis = var_4380_axis_0, split_sizes = var_4380_split_sizes_0, x = var_4379_cast_fp16)[name = tensor("op_4380_cast_fp16")]; + tensor var_4382_mode_0 = const()[name = tensor("op_4382_mode_0"), val = tensor("EXACT")]; + tensor var_4382_cast_fp16 = gelu(mode = var_4382_mode_0, x = var_4380_cast_fp16_1)[name = tensor("op_4382_cast_fp16")]; + tensor input_287_cast_fp16 = mul(x = var_4380_cast_fp16_0, y = var_4382_cast_fp16)[name = tensor("input_287_cast_fp16")]; + tensor var_4386 = const()[name = tensor("op_4386"), val = tensor([1, 1])]; + tensor var_4388 = const()[name = tensor("op_4388"), val = tensor([1, 1])]; + tensor var_4390_pad_type_0 = const()[name = tensor("op_4390_pad_type_0"), val = tensor("custom")]; + tensor var_4390_pad_0 = const()[name = tensor("op_4390_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538771392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543686656))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543686848)))]; + tensor var_4390_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_4388, groups = var_1196, pad = var_4390_pad_0, pad_type = var_4390_pad_type_0, strides = var_4386, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = tensor("op_4390_cast_fp16")]; + tensor inputs_127_cast_fp16 = add(x = var_4390_cast_fp16, y = inputs_125_cast_fp16)[name = tensor("inputs_127_cast_fp16")]; + tensor hidden_states_179_axes_0 = const()[name = tensor("hidden_states_179_axes_0"), val = tensor([1])]; + tensor hidden_states_179_gamma_0_to_fp16 = const()[name = tensor("hidden_states_179_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543689472)))]; + tensor hidden_states_179_beta_0_to_fp16 = const()[name = tensor("hidden_states_179_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543692096)))]; + tensor var_4406_to_fp16 = const()[name = tensor("op_4406_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_179_cast_fp16 = layer_norm(axes = hidden_states_179_axes_0, beta = hidden_states_179_beta_0_to_fp16, epsilon = var_4406_to_fp16, gamma = hidden_states_179_gamma_0_to_fp16, x = inputs_127_cast_fp16)[name = tensor("hidden_states_179_cast_fp16")]; + tensor var_4421 = const()[name = tensor("op_4421"), val = tensor([1, 1])]; + tensor var_4423 = const()[name = tensor("op_4423"), val = tensor([1, 1])]; + tensor q_85_pad_type_0 = const()[name = tensor("q_85_pad_type_0"), val = tensor("custom")]; + tensor q_85_pad_0 = const()[name = tensor("q_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543694720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(544923584))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_85_cast_fp16 = conv(dilations = var_4423, groups = var_1196, pad = q_85_pad_0, pad_type = q_85_pad_type_0, strides = var_4421, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_179_cast_fp16)[name = tensor("q_85_cast_fp16")]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1, 1])]; + tensor var_4429 = const()[name = tensor("op_4429"), 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 down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(544923776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546152640))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_85_cast_fp16 = conv(dilations = var_4429, groups = var_1196, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_4427, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_179_cast_fp16)[name = tensor("k_85_cast_fp16")]; + tensor var_4433 = const()[name = tensor("op_4433"), val = tensor([1, 1])]; + tensor var_4435 = const()[name = tensor("op_4435"), val = tensor([1, 1])]; + tensor v_85_pad_type_0 = const()[name = tensor("v_85_pad_type_0"), val = tensor("custom")]; + tensor v_85_pad_0 = const()[name = tensor("v_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546152832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547381696))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_85_cast_fp16 = conv(dilations = var_4435, groups = var_1196, pad = v_85_pad_0, pad_type = v_85_pad_type_0, strides = var_4433, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_179_cast_fp16)[name = tensor("v_85_cast_fp16")]; + tensor var_4439 = const()[name = tensor("op_4439"), val = tensor([1, 20, 64, -1])]; + tensor var_4440_cast_fp16 = reshape(shape = var_4439, x = q_85_cast_fp16)[name = tensor("op_4440_cast_fp16")]; + tensor var_4441 = const()[name = tensor("op_4441"), val = tensor([1, 20, 64, -1])]; + tensor var_4442_cast_fp16 = reshape(shape = var_4441, x = k_85_cast_fp16)[name = tensor("op_4442_cast_fp16")]; + tensor var_4443 = const()[name = tensor("op_4443"), val = tensor([1, 20, 64, -1])]; + tensor var_4444_cast_fp16 = reshape(shape = var_4443, x = v_85_cast_fp16)[name = tensor("op_4444_cast_fp16")]; + tensor attn_weights_169_transpose_x_0 = const()[name = tensor("attn_weights_169_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_169_transpose_y_0 = const()[name = tensor("attn_weights_169_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4440_cast_fp16, y = var_4442_cast_fp16)[name = tensor("attn_weights_169_cast_fp16")]; + tensor attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_171_cast_fp16")]; + tensor var_4448_cast_fp16 = softmax(axis = var_1180, x = attn_weights_171_cast_fp16)[name = tensor("op_4448_cast_fp16")]; + tensor attn_85_transpose_x_0 = const()[name = tensor("attn_85_transpose_x_0"), val = tensor(false)]; + tensor attn_85_transpose_y_0 = const()[name = tensor("attn_85_transpose_y_0"), val = tensor(true)]; + tensor attn_85_cast_fp16 = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4444_cast_fp16, y = var_4448_cast_fp16)[name = tensor("attn_85_cast_fp16")]; + tensor var_4452 = const()[name = tensor("op_4452"), val = tensor([1, 1280, 1, -1])]; + tensor input_289_cast_fp16 = reshape(shape = var_4452, x = attn_85_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor var_4457 = const()[name = tensor("op_4457"), val = tensor([1, 1])]; + tensor var_4459 = const()[name = tensor("op_4459"), val = tensor([1, 1])]; + tensor var_4461_pad_type_0 = const()[name = tensor("op_4461_pad_type_0"), val = tensor("custom")]; + tensor var_4461_pad_0 = const()[name = tensor("op_4461_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547381888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548610752))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548610944)))]; + tensor var_4461_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_4459, groups = var_1196, pad = var_4461_pad_0, pad_type = var_4461_pad_type_0, strides = var_4457, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_289_cast_fp16)[name = tensor("op_4461_cast_fp16")]; + tensor inputs_129_cast_fp16 = add(x = var_4461_cast_fp16, y = inputs_127_cast_fp16)[name = tensor("inputs_129_cast_fp16")]; + tensor hidden_states_181_axes_0 = const()[name = tensor("hidden_states_181_axes_0"), val = tensor([1])]; + tensor hidden_states_181_gamma_0_to_fp16 = const()[name = tensor("hidden_states_181_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548613568)))]; + tensor hidden_states_181_beta_0_to_fp16 = const()[name = tensor("hidden_states_181_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548616192)))]; + tensor var_4471_to_fp16 = const()[name = tensor("op_4471_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_181_cast_fp16 = layer_norm(axes = hidden_states_181_axes_0, beta = hidden_states_181_beta_0_to_fp16, epsilon = var_4471_to_fp16, gamma = hidden_states_181_gamma_0_to_fp16, x = inputs_129_cast_fp16)[name = tensor("hidden_states_181_cast_fp16")]; + tensor var_4486 = const()[name = tensor("op_4486"), val = tensor([1, 1])]; + tensor var_4488 = const()[name = tensor("op_4488"), val = tensor([1, 1])]; + tensor q_87_pad_type_0 = const()[name = tensor("q_87_pad_type_0"), val = tensor("custom")]; + tensor q_87_pad_0 = const()[name = tensor("q_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548618816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549847680))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_87_cast_fp16 = conv(dilations = var_4488, groups = var_1196, pad = q_87_pad_0, pad_type = q_87_pad_type_0, strides = var_4486, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = tensor("q_87_cast_fp16")]; + tensor var_4492 = const()[name = tensor("op_4492"), val = tensor([1, 1])]; + tensor var_4494 = const()[name = tensor("op_4494"), val = tensor([1, 1])]; + tensor k_87_pad_type_0 = const()[name = tensor("k_87_pad_type_0"), val = tensor("custom")]; + tensor k_87_pad_0 = const()[name = tensor("k_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549847872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(551814016))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_87_cast_fp16 = conv(dilations = var_4494, groups = var_1196, pad = k_87_pad_0, pad_type = k_87_pad_type_0, strides = var_4492, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_87_cast_fp16")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 1])]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor v_87_pad_type_0 = const()[name = tensor("v_87_pad_type_0"), val = tensor("custom")]; + tensor v_87_pad_0 = const()[name = tensor("v_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(551814208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553780352))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_87_cast_fp16 = conv(dilations = var_4500, groups = var_1196, pad = v_87_pad_0, pad_type = v_87_pad_type_0, strides = var_4498, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_87_cast_fp16")]; + tensor var_4504 = const()[name = tensor("op_4504"), val = tensor([1, 20, 64, -1])]; + tensor var_4505_cast_fp16 = reshape(shape = var_4504, x = q_87_cast_fp16)[name = tensor("op_4505_cast_fp16")]; + tensor var_4506 = const()[name = tensor("op_4506"), val = tensor([1, 20, 64, -1])]; + tensor var_4507_cast_fp16 = reshape(shape = var_4506, x = k_87_cast_fp16)[name = tensor("op_4507_cast_fp16")]; + tensor var_4508 = const()[name = tensor("op_4508"), val = tensor([1, 20, 64, -1])]; + tensor var_4509_cast_fp16 = reshape(shape = var_4508, x = v_87_cast_fp16)[name = tensor("op_4509_cast_fp16")]; + tensor attn_weights_173_transpose_x_0 = const()[name = tensor("attn_weights_173_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_173_transpose_y_0 = const()[name = tensor("attn_weights_173_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_173_cast_fp16 = matmul(transpose_x = attn_weights_173_transpose_x_0, transpose_y = attn_weights_173_transpose_y_0, x = var_4505_cast_fp16, y = var_4507_cast_fp16)[name = tensor("attn_weights_173_cast_fp16")]; + tensor attn_weights_175_cast_fp16 = mul(x = attn_weights_173_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_175_cast_fp16")]; + tensor var_4513_cast_fp16 = softmax(axis = var_1180, x = attn_weights_175_cast_fp16)[name = tensor("op_4513_cast_fp16")]; + tensor attn_87_transpose_x_0 = const()[name = tensor("attn_87_transpose_x_0"), val = tensor(false)]; + tensor attn_87_transpose_y_0 = const()[name = tensor("attn_87_transpose_y_0"), val = tensor(true)]; + tensor attn_87_cast_fp16 = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_4509_cast_fp16, y = var_4513_cast_fp16)[name = tensor("attn_87_cast_fp16")]; + tensor var_4517 = const()[name = tensor("op_4517"), val = tensor([1, 1280, 1, -1])]; + tensor input_291_cast_fp16 = reshape(shape = var_4517, x = attn_87_cast_fp16)[name = tensor("input_291_cast_fp16")]; + tensor var_4522 = const()[name = tensor("op_4522"), val = tensor([1, 1])]; + tensor var_4524 = const()[name = tensor("op_4524"), val = tensor([1, 1])]; + tensor var_4526_pad_type_0 = const()[name = tensor("op_4526_pad_type_0"), val = tensor("custom")]; + tensor var_4526_pad_0 = const()[name = tensor("op_4526_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553780544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555009408))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555009600)))]; + tensor var_4526_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_4524, groups = var_1196, pad = var_4526_pad_0, pad_type = var_4526_pad_type_0, strides = var_4522, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_291_cast_fp16)[name = tensor("op_4526_cast_fp16")]; + tensor inputs_131_cast_fp16 = add(x = var_4526_cast_fp16, y = inputs_129_cast_fp16)[name = tensor("inputs_131_cast_fp16")]; + tensor input_293_axes_0 = const()[name = tensor("input_293_axes_0"), val = tensor([1])]; + tensor input_293_gamma_0_to_fp16 = const()[name = tensor("input_293_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555012224)))]; + tensor input_293_beta_0_to_fp16 = const()[name = tensor("input_293_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555014848)))]; + tensor var_4536_to_fp16 = const()[name = tensor("op_4536_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_293_cast_fp16 = layer_norm(axes = input_293_axes_0, beta = input_293_beta_0_to_fp16, epsilon = var_4536_to_fp16, gamma = input_293_gamma_0_to_fp16, x = inputs_131_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor var_4552 = const()[name = tensor("op_4552"), val = tensor([1, 1])]; + tensor var_4554 = const()[name = tensor("op_4554"), val = tensor([1, 1])]; + tensor var_4556_pad_type_0 = const()[name = tensor("op_4556_pad_type_0"), val = tensor("custom")]; + tensor var_4556_pad_0 = const()[name = tensor("op_4556_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555017472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564847936))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564848128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564855872))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4556_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4554, groups = var_1196, pad = var_4556_pad_0, pad_type = var_4556_pad_type_0, strides = var_4552, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_293_cast_fp16)[name = tensor("op_4556_cast_fp16")]; + tensor var_4557_split_sizes_0 = const()[name = tensor("op_4557_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4557_axis_0 = const()[name = tensor("op_4557_axis_0"), val = tensor(1)]; + tensor var_4557_cast_fp16_0, tensor var_4557_cast_fp16_1 = split(axis = var_4557_axis_0, split_sizes = var_4557_split_sizes_0, x = var_4556_cast_fp16)[name = tensor("op_4557_cast_fp16")]; + tensor var_4559_mode_0 = const()[name = tensor("op_4559_mode_0"), val = tensor("EXACT")]; + tensor var_4559_cast_fp16 = gelu(mode = var_4559_mode_0, x = var_4557_cast_fp16_1)[name = tensor("op_4559_cast_fp16")]; + tensor input_295_cast_fp16 = mul(x = var_4557_cast_fp16_0, y = var_4559_cast_fp16)[name = tensor("input_295_cast_fp16")]; + 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 down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564856064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569771328))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569771520)))]; + tensor var_4567_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_4565, groups = var_1196, pad = var_4567_pad_0, pad_type = var_4567_pad_type_0, strides = var_4563, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = tensor("op_4567_cast_fp16")]; + tensor inputs_133_cast_fp16 = add(x = var_4567_cast_fp16, y = inputs_131_cast_fp16)[name = tensor("inputs_133_cast_fp16")]; + tensor hidden_states_185_axes_0 = const()[name = tensor("hidden_states_185_axes_0"), val = tensor([1])]; + tensor hidden_states_185_gamma_0_to_fp16 = const()[name = tensor("hidden_states_185_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569774144)))]; + tensor hidden_states_185_beta_0_to_fp16 = const()[name = tensor("hidden_states_185_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569776768)))]; + tensor var_4583_to_fp16 = const()[name = tensor("op_4583_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_185_cast_fp16 = layer_norm(axes = hidden_states_185_axes_0, beta = hidden_states_185_beta_0_to_fp16, epsilon = var_4583_to_fp16, gamma = hidden_states_185_gamma_0_to_fp16, x = inputs_133_cast_fp16)[name = tensor("hidden_states_185_cast_fp16")]; + 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 q_89_pad_type_0 = const()[name = tensor("q_89_pad_type_0"), val = tensor("custom")]; + tensor q_89_pad_0 = const()[name = tensor("q_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569779392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571008256))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_89_cast_fp16 = conv(dilations = var_4600, groups = var_1196, pad = q_89_pad_0, pad_type = q_89_pad_type_0, strides = var_4598, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_185_cast_fp16)[name = tensor("q_89_cast_fp16")]; + 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 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 down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571008448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572237312))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_89_cast_fp16 = conv(dilations = var_4606, groups = var_1196, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_4604, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_185_cast_fp16)[name = tensor("k_89_cast_fp16")]; + tensor var_4610 = const()[name = tensor("op_4610"), val = tensor([1, 1])]; + tensor var_4612 = const()[name = tensor("op_4612"), val = tensor([1, 1])]; + tensor v_89_pad_type_0 = const()[name = tensor("v_89_pad_type_0"), val = tensor("custom")]; + tensor v_89_pad_0 = const()[name = tensor("v_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572237504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573466368))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_89_cast_fp16 = conv(dilations = var_4612, groups = var_1196, pad = v_89_pad_0, pad_type = v_89_pad_type_0, strides = var_4610, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_185_cast_fp16)[name = tensor("v_89_cast_fp16")]; + tensor var_4616 = const()[name = tensor("op_4616"), val = tensor([1, 20, 64, -1])]; + tensor var_4617_cast_fp16 = reshape(shape = var_4616, x = q_89_cast_fp16)[name = tensor("op_4617_cast_fp16")]; + tensor var_4618 = const()[name = tensor("op_4618"), val = tensor([1, 20, 64, -1])]; + tensor var_4619_cast_fp16 = reshape(shape = var_4618, x = k_89_cast_fp16)[name = tensor("op_4619_cast_fp16")]; + tensor var_4620 = const()[name = tensor("op_4620"), val = tensor([1, 20, 64, -1])]; + tensor var_4621_cast_fp16 = reshape(shape = var_4620, x = v_89_cast_fp16)[name = tensor("op_4621_cast_fp16")]; + tensor attn_weights_177_transpose_x_0 = const()[name = tensor("attn_weights_177_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_177_transpose_y_0 = const()[name = tensor("attn_weights_177_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4617_cast_fp16, y = var_4619_cast_fp16)[name = tensor("attn_weights_177_cast_fp16")]; + tensor attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_179_cast_fp16")]; + tensor var_4625_cast_fp16 = softmax(axis = var_1180, x = attn_weights_179_cast_fp16)[name = tensor("op_4625_cast_fp16")]; + tensor attn_89_transpose_x_0 = const()[name = tensor("attn_89_transpose_x_0"), val = tensor(false)]; + tensor attn_89_transpose_y_0 = const()[name = tensor("attn_89_transpose_y_0"), val = tensor(true)]; + tensor attn_89_cast_fp16 = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_4621_cast_fp16, y = var_4625_cast_fp16)[name = tensor("attn_89_cast_fp16")]; + tensor var_4629 = const()[name = tensor("op_4629"), val = tensor([1, 1280, 1, -1])]; + tensor input_297_cast_fp16 = reshape(shape = var_4629, x = attn_89_cast_fp16)[name = tensor("input_297_cast_fp16")]; + tensor var_4634 = const()[name = tensor("op_4634"), val = tensor([1, 1])]; + tensor var_4636 = const()[name = tensor("op_4636"), val = tensor([1, 1])]; + tensor var_4638_pad_type_0 = const()[name = tensor("op_4638_pad_type_0"), val = tensor("custom")]; + tensor var_4638_pad_0 = const()[name = tensor("op_4638_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573466560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574695424))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574695616)))]; + tensor var_4638_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_4636, groups = var_1196, pad = var_4638_pad_0, pad_type = var_4638_pad_type_0, strides = var_4634, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_297_cast_fp16)[name = tensor("op_4638_cast_fp16")]; + tensor inputs_135_cast_fp16 = add(x = var_4638_cast_fp16, y = inputs_133_cast_fp16)[name = tensor("inputs_135_cast_fp16")]; + tensor hidden_states_187_axes_0 = const()[name = tensor("hidden_states_187_axes_0"), val = tensor([1])]; + tensor hidden_states_187_gamma_0_to_fp16 = const()[name = tensor("hidden_states_187_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574698240)))]; + tensor hidden_states_187_beta_0_to_fp16 = const()[name = tensor("hidden_states_187_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574700864)))]; + tensor var_4648_to_fp16 = const()[name = tensor("op_4648_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_187_cast_fp16 = layer_norm(axes = hidden_states_187_axes_0, beta = hidden_states_187_beta_0_to_fp16, epsilon = var_4648_to_fp16, gamma = hidden_states_187_gamma_0_to_fp16, x = inputs_135_cast_fp16)[name = tensor("hidden_states_187_cast_fp16")]; + tensor var_4663 = const()[name = tensor("op_4663"), val = tensor([1, 1])]; + tensor var_4665 = const()[name = tensor("op_4665"), val = tensor([1, 1])]; + tensor q_91_pad_type_0 = const()[name = tensor("q_91_pad_type_0"), val = tensor("custom")]; + tensor q_91_pad_0 = const()[name = tensor("q_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574703488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575932352))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_91_cast_fp16 = conv(dilations = var_4665, groups = var_1196, pad = q_91_pad_0, pad_type = q_91_pad_type_0, strides = var_4663, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor("q_91_cast_fp16")]; + tensor var_4669 = const()[name = tensor("op_4669"), val = tensor([1, 1])]; + tensor var_4671 = const()[name = tensor("op_4671"), val = tensor([1, 1])]; + tensor k_91_pad_type_0 = const()[name = tensor("k_91_pad_type_0"), val = tensor("custom")]; + tensor k_91_pad_0 = const()[name = tensor("k_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575932544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577898688))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_91_cast_fp16 = conv(dilations = var_4671, groups = var_1196, pad = k_91_pad_0, pad_type = k_91_pad_type_0, strides = var_4669, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_91_cast_fp16")]; + tensor var_4675 = const()[name = tensor("op_4675"), val = tensor([1, 1])]; + tensor var_4677 = const()[name = tensor("op_4677"), val = tensor([1, 1])]; + tensor v_91_pad_type_0 = const()[name = tensor("v_91_pad_type_0"), val = tensor("custom")]; + tensor v_91_pad_0 = const()[name = tensor("v_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577898880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579865024))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_91_cast_fp16 = conv(dilations = var_4677, groups = var_1196, pad = v_91_pad_0, pad_type = v_91_pad_type_0, strides = var_4675, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_91_cast_fp16")]; + tensor var_4681 = const()[name = tensor("op_4681"), val = tensor([1, 20, 64, -1])]; + tensor var_4682_cast_fp16 = reshape(shape = var_4681, x = q_91_cast_fp16)[name = tensor("op_4682_cast_fp16")]; + tensor var_4683 = const()[name = tensor("op_4683"), val = tensor([1, 20, 64, -1])]; + tensor var_4684_cast_fp16 = reshape(shape = var_4683, x = k_91_cast_fp16)[name = tensor("op_4684_cast_fp16")]; + tensor var_4685 = const()[name = tensor("op_4685"), val = tensor([1, 20, 64, -1])]; + tensor var_4686_cast_fp16 = reshape(shape = var_4685, x = v_91_cast_fp16)[name = tensor("op_4686_cast_fp16")]; + tensor attn_weights_181_transpose_x_0 = const()[name = tensor("attn_weights_181_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_181_transpose_y_0 = const()[name = tensor("attn_weights_181_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_181_cast_fp16 = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = var_4682_cast_fp16, y = var_4684_cast_fp16)[name = tensor("attn_weights_181_cast_fp16")]; + tensor attn_weights_183_cast_fp16 = mul(x = attn_weights_181_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_183_cast_fp16")]; + tensor var_4690_cast_fp16 = softmax(axis = var_1180, x = attn_weights_183_cast_fp16)[name = tensor("op_4690_cast_fp16")]; + tensor attn_91_transpose_x_0 = const()[name = tensor("attn_91_transpose_x_0"), val = tensor(false)]; + tensor attn_91_transpose_y_0 = const()[name = tensor("attn_91_transpose_y_0"), val = tensor(true)]; + tensor attn_91_cast_fp16 = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_4686_cast_fp16, y = var_4690_cast_fp16)[name = tensor("attn_91_cast_fp16")]; + tensor var_4694 = const()[name = tensor("op_4694"), val = tensor([1, 1280, 1, -1])]; + tensor input_299_cast_fp16 = reshape(shape = var_4694, x = attn_91_cast_fp16)[name = tensor("input_299_cast_fp16")]; + tensor var_4699 = const()[name = tensor("op_4699"), val = tensor([1, 1])]; + tensor var_4701 = const()[name = tensor("op_4701"), val = tensor([1, 1])]; + tensor var_4703_pad_type_0 = const()[name = tensor("op_4703_pad_type_0"), val = tensor("custom")]; + tensor var_4703_pad_0 = const()[name = tensor("op_4703_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579865216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581094080))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581094272)))]; + tensor var_4703_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_4701, groups = var_1196, pad = var_4703_pad_0, pad_type = var_4703_pad_type_0, strides = var_4699, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = tensor("op_4703_cast_fp16")]; + tensor inputs_137_cast_fp16 = add(x = var_4703_cast_fp16, y = inputs_135_cast_fp16)[name = tensor("inputs_137_cast_fp16")]; + tensor input_301_axes_0 = const()[name = tensor("input_301_axes_0"), val = tensor([1])]; + tensor input_301_gamma_0_to_fp16 = const()[name = tensor("input_301_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581096896)))]; + tensor input_301_beta_0_to_fp16 = const()[name = tensor("input_301_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581099520)))]; + tensor var_4713_to_fp16 = const()[name = tensor("op_4713_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_301_cast_fp16 = layer_norm(axes = input_301_axes_0, beta = input_301_beta_0_to_fp16, epsilon = var_4713_to_fp16, gamma = input_301_gamma_0_to_fp16, x = inputs_137_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor var_4729 = const()[name = tensor("op_4729"), val = tensor([1, 1])]; + tensor var_4731 = const()[name = tensor("op_4731"), val = tensor([1, 1])]; + tensor var_4733_pad_type_0 = const()[name = tensor("op_4733_pad_type_0"), val = tensor("custom")]; + tensor var_4733_pad_0 = const()[name = tensor("op_4733_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581102144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590932608))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590932800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590940544))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4733_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4731, groups = var_1196, pad = var_4733_pad_0, pad_type = var_4733_pad_type_0, strides = var_4729, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_301_cast_fp16)[name = tensor("op_4733_cast_fp16")]; + tensor var_4734_split_sizes_0 = const()[name = tensor("op_4734_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4734_axis_0 = const()[name = tensor("op_4734_axis_0"), val = tensor(1)]; + tensor var_4734_cast_fp16_0, tensor var_4734_cast_fp16_1 = split(axis = var_4734_axis_0, split_sizes = var_4734_split_sizes_0, x = var_4733_cast_fp16)[name = tensor("op_4734_cast_fp16")]; + tensor var_4736_mode_0 = const()[name = tensor("op_4736_mode_0"), val = tensor("EXACT")]; + tensor var_4736_cast_fp16 = gelu(mode = var_4736_mode_0, x = var_4734_cast_fp16_1)[name = tensor("op_4736_cast_fp16")]; + tensor input_303_cast_fp16 = mul(x = var_4734_cast_fp16_0, y = var_4736_cast_fp16)[name = tensor("input_303_cast_fp16")]; + tensor var_4740 = const()[name = tensor("op_4740"), val = tensor([1, 1])]; + tensor var_4742 = const()[name = tensor("op_4742"), val = tensor([1, 1])]; + tensor var_4744_pad_type_0 = const()[name = tensor("op_4744_pad_type_0"), val = tensor("custom")]; + tensor var_4744_pad_0 = const()[name = tensor("op_4744_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590940736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595856000))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595856192)))]; + tensor var_4744_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_4742, groups = var_1196, pad = var_4744_pad_0, pad_type = var_4744_pad_type_0, strides = var_4740, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = tensor("op_4744_cast_fp16")]; + tensor inputs_139_cast_fp16 = add(x = var_4744_cast_fp16, y = inputs_137_cast_fp16)[name = tensor("inputs_139_cast_fp16")]; + tensor hidden_states_191_axes_0 = const()[name = tensor("hidden_states_191_axes_0"), val = tensor([1])]; + tensor hidden_states_191_gamma_0_to_fp16 = const()[name = tensor("hidden_states_191_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595858816)))]; + tensor hidden_states_191_beta_0_to_fp16 = const()[name = tensor("hidden_states_191_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595861440)))]; + tensor var_4760_to_fp16 = const()[name = tensor("op_4760_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_191_cast_fp16 = layer_norm(axes = hidden_states_191_axes_0, beta = hidden_states_191_beta_0_to_fp16, epsilon = var_4760_to_fp16, gamma = hidden_states_191_gamma_0_to_fp16, x = inputs_139_cast_fp16)[name = tensor("hidden_states_191_cast_fp16")]; + tensor var_4775 = const()[name = tensor("op_4775"), val = tensor([1, 1])]; + tensor var_4777 = const()[name = tensor("op_4777"), val = tensor([1, 1])]; + tensor q_93_pad_type_0 = const()[name = tensor("q_93_pad_type_0"), val = tensor("custom")]; + tensor q_93_pad_0 = const()[name = tensor("q_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595864064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(597092928))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_93_cast_fp16 = conv(dilations = var_4777, groups = var_1196, pad = q_93_pad_0, pad_type = q_93_pad_type_0, strides = var_4775, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_191_cast_fp16)[name = tensor("q_93_cast_fp16")]; + tensor var_4781 = const()[name = tensor("op_4781"), val = tensor([1, 1])]; + tensor var_4783 = const()[name = tensor("op_4783"), 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 down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(597093120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598321984))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_93_cast_fp16 = conv(dilations = var_4783, groups = var_1196, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_4781, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_191_cast_fp16)[name = tensor("k_93_cast_fp16")]; + tensor var_4787 = const()[name = tensor("op_4787"), val = tensor([1, 1])]; + tensor var_4789 = const()[name = tensor("op_4789"), val = tensor([1, 1])]; + tensor v_93_pad_type_0 = const()[name = tensor("v_93_pad_type_0"), val = tensor("custom")]; + tensor v_93_pad_0 = const()[name = tensor("v_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598322176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599551040))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_93_cast_fp16 = conv(dilations = var_4789, groups = var_1196, pad = v_93_pad_0, pad_type = v_93_pad_type_0, strides = var_4787, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_191_cast_fp16)[name = tensor("v_93_cast_fp16")]; + tensor var_4793 = const()[name = tensor("op_4793"), val = tensor([1, 20, 64, -1])]; + tensor var_4794_cast_fp16 = reshape(shape = var_4793, x = q_93_cast_fp16)[name = tensor("op_4794_cast_fp16")]; + tensor var_4795 = const()[name = tensor("op_4795"), val = tensor([1, 20, 64, -1])]; + tensor var_4796_cast_fp16 = reshape(shape = var_4795, x = k_93_cast_fp16)[name = tensor("op_4796_cast_fp16")]; + tensor var_4797 = const()[name = tensor("op_4797"), val = tensor([1, 20, 64, -1])]; + tensor var_4798_cast_fp16 = reshape(shape = var_4797, x = v_93_cast_fp16)[name = tensor("op_4798_cast_fp16")]; + tensor attn_weights_185_transpose_x_0 = const()[name = tensor("attn_weights_185_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_185_transpose_y_0 = const()[name = tensor("attn_weights_185_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4794_cast_fp16, y = var_4796_cast_fp16)[name = tensor("attn_weights_185_cast_fp16")]; + tensor attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_187_cast_fp16")]; + tensor var_4802_cast_fp16 = softmax(axis = var_1180, x = attn_weights_187_cast_fp16)[name = tensor("op_4802_cast_fp16")]; + tensor attn_93_transpose_x_0 = const()[name = tensor("attn_93_transpose_x_0"), val = tensor(false)]; + tensor attn_93_transpose_y_0 = const()[name = tensor("attn_93_transpose_y_0"), val = tensor(true)]; + tensor attn_93_cast_fp16 = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_4798_cast_fp16, y = var_4802_cast_fp16)[name = tensor("attn_93_cast_fp16")]; + tensor var_4806 = const()[name = tensor("op_4806"), val = tensor([1, 1280, 1, -1])]; + tensor input_305_cast_fp16 = reshape(shape = var_4806, x = attn_93_cast_fp16)[name = tensor("input_305_cast_fp16")]; + tensor var_4811 = const()[name = tensor("op_4811"), val = tensor([1, 1])]; + tensor var_4813 = const()[name = tensor("op_4813"), val = tensor([1, 1])]; + tensor var_4815_pad_type_0 = const()[name = tensor("op_4815_pad_type_0"), val = tensor("custom")]; + tensor var_4815_pad_0 = const()[name = tensor("op_4815_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599551232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600780096))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600780288)))]; + tensor var_4815_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_4813, groups = var_1196, pad = var_4815_pad_0, pad_type = var_4815_pad_type_0, strides = var_4811, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_305_cast_fp16)[name = tensor("op_4815_cast_fp16")]; + tensor inputs_141_cast_fp16 = add(x = var_4815_cast_fp16, y = inputs_139_cast_fp16)[name = tensor("inputs_141_cast_fp16")]; + tensor hidden_states_193_axes_0 = const()[name = tensor("hidden_states_193_axes_0"), val = tensor([1])]; + tensor hidden_states_193_gamma_0_to_fp16 = const()[name = tensor("hidden_states_193_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600782912)))]; + tensor hidden_states_193_beta_0_to_fp16 = const()[name = tensor("hidden_states_193_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600785536)))]; + tensor var_4825_to_fp16 = const()[name = tensor("op_4825_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_193_cast_fp16 = layer_norm(axes = hidden_states_193_axes_0, beta = hidden_states_193_beta_0_to_fp16, epsilon = var_4825_to_fp16, gamma = hidden_states_193_gamma_0_to_fp16, x = inputs_141_cast_fp16)[name = tensor("hidden_states_193_cast_fp16")]; + tensor var_4840 = const()[name = tensor("op_4840"), val = tensor([1, 1])]; + tensor var_4842 = const()[name = tensor("op_4842"), val = tensor([1, 1])]; + tensor q_95_pad_type_0 = const()[name = tensor("q_95_pad_type_0"), val = tensor("custom")]; + tensor q_95_pad_0 = const()[name = tensor("q_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600788160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602017024))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_95_cast_fp16 = conv(dilations = var_4842, groups = var_1196, pad = q_95_pad_0, pad_type = q_95_pad_type_0, strides = var_4840, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_193_cast_fp16)[name = tensor("q_95_cast_fp16")]; + 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 k_95_pad_type_0 = const()[name = tensor("k_95_pad_type_0"), val = tensor("custom")]; + tensor k_95_pad_0 = const()[name = tensor("k_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602017216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603983360))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_95_cast_fp16 = conv(dilations = var_4848, groups = var_1196, pad = k_95_pad_0, pad_type = k_95_pad_type_0, strides = var_4846, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_95_cast_fp16")]; + tensor var_4852 = const()[name = tensor("op_4852"), val = tensor([1, 1])]; + tensor var_4854 = const()[name = tensor("op_4854"), val = tensor([1, 1])]; + tensor v_95_pad_type_0 = const()[name = tensor("v_95_pad_type_0"), val = tensor("custom")]; + tensor v_95_pad_0 = const()[name = tensor("v_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603983552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605949696))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_95_cast_fp16 = conv(dilations = var_4854, groups = var_1196, pad = v_95_pad_0, pad_type = v_95_pad_type_0, strides = var_4852, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_95_cast_fp16")]; + tensor var_4858 = const()[name = tensor("op_4858"), val = tensor([1, 20, 64, -1])]; + tensor var_4859_cast_fp16 = reshape(shape = var_4858, x = q_95_cast_fp16)[name = tensor("op_4859_cast_fp16")]; + tensor var_4860 = const()[name = tensor("op_4860"), val = tensor([1, 20, 64, -1])]; + tensor var_4861_cast_fp16 = reshape(shape = var_4860, x = k_95_cast_fp16)[name = tensor("op_4861_cast_fp16")]; + tensor var_4862 = const()[name = tensor("op_4862"), val = tensor([1, 20, 64, -1])]; + tensor var_4863_cast_fp16 = reshape(shape = var_4862, x = v_95_cast_fp16)[name = tensor("op_4863_cast_fp16")]; + tensor attn_weights_189_transpose_x_0 = const()[name = tensor("attn_weights_189_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_189_transpose_y_0 = const()[name = tensor("attn_weights_189_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_189_cast_fp16 = matmul(transpose_x = attn_weights_189_transpose_x_0, transpose_y = attn_weights_189_transpose_y_0, x = var_4859_cast_fp16, y = var_4861_cast_fp16)[name = tensor("attn_weights_189_cast_fp16")]; + tensor attn_weights_191_cast_fp16 = mul(x = attn_weights_189_cast_fp16, y = var_1187_to_fp16)[name = tensor("attn_weights_191_cast_fp16")]; + tensor var_4867_cast_fp16 = softmax(axis = var_1180, x = attn_weights_191_cast_fp16)[name = tensor("op_4867_cast_fp16")]; + tensor attn_95_transpose_x_0 = const()[name = tensor("attn_95_transpose_x_0"), val = tensor(false)]; + tensor attn_95_transpose_y_0 = const()[name = tensor("attn_95_transpose_y_0"), val = tensor(true)]; + tensor attn_95_cast_fp16 = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_4863_cast_fp16, y = var_4867_cast_fp16)[name = tensor("attn_95_cast_fp16")]; + tensor var_4871 = const()[name = tensor("op_4871"), val = tensor([1, 1280, 1, -1])]; + tensor input_307_cast_fp16 = reshape(shape = var_4871, x = attn_95_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor var_4876 = const()[name = tensor("op_4876"), val = tensor([1, 1])]; + tensor var_4878 = const()[name = tensor("op_4878"), val = tensor([1, 1])]; + tensor var_4880_pad_type_0 = const()[name = tensor("op_4880_pad_type_0"), val = tensor("custom")]; + tensor var_4880_pad_0 = const()[name = tensor("op_4880_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605949888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607178752))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607178944)))]; + tensor var_4880_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_4878, groups = var_1196, pad = var_4880_pad_0, pad_type = var_4880_pad_type_0, strides = var_4876, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = tensor("op_4880_cast_fp16")]; + tensor inputs_143_cast_fp16 = add(x = var_4880_cast_fp16, y = inputs_141_cast_fp16)[name = tensor("inputs_143_cast_fp16")]; + tensor input_309_axes_0 = const()[name = tensor("input_309_axes_0"), val = tensor([1])]; + tensor input_309_gamma_0_to_fp16 = const()[name = tensor("input_309_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607181568)))]; + tensor input_309_beta_0_to_fp16 = const()[name = tensor("input_309_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607184192)))]; + tensor var_4890_to_fp16 = const()[name = tensor("op_4890_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_309_cast_fp16 = layer_norm(axes = input_309_axes_0, beta = input_309_beta_0_to_fp16, epsilon = var_4890_to_fp16, gamma = input_309_gamma_0_to_fp16, x = inputs_143_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor var_4906 = const()[name = tensor("op_4906"), val = tensor([1, 1])]; + tensor var_4908 = const()[name = tensor("op_4908"), val = tensor([1, 1])]; + tensor var_4910_pad_type_0 = const()[name = tensor("op_4910_pad_type_0"), val = tensor("custom")]; + tensor var_4910_pad_0 = const()[name = tensor("op_4910_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607186816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617017280))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617017472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617025216))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4910_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4908, groups = var_1196, pad = var_4910_pad_0, pad_type = var_4910_pad_type_0, strides = var_4906, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_309_cast_fp16)[name = tensor("op_4910_cast_fp16")]; + tensor var_4911_split_sizes_0 = const()[name = tensor("op_4911_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4911_axis_0 = const()[name = tensor("op_4911_axis_0"), val = tensor(1)]; + tensor var_4911_cast_fp16_0, tensor var_4911_cast_fp16_1 = split(axis = var_4911_axis_0, split_sizes = var_4911_split_sizes_0, x = var_4910_cast_fp16)[name = tensor("op_4911_cast_fp16")]; + tensor var_4913_mode_0 = const()[name = tensor("op_4913_mode_0"), val = tensor("EXACT")]; + tensor var_4913_cast_fp16 = gelu(mode = var_4913_mode_0, x = var_4911_cast_fp16_1)[name = tensor("op_4913_cast_fp16")]; + tensor input_311_cast_fp16 = mul(x = var_4911_cast_fp16_0, y = var_4913_cast_fp16)[name = tensor("input_311_cast_fp16")]; + tensor var_4917 = const()[name = tensor("op_4917"), val = tensor([1, 1])]; + tensor var_4919 = const()[name = tensor("op_4919"), val = tensor([1, 1])]; + tensor var_4921_pad_type_0 = const()[name = tensor("op_4921_pad_type_0"), val = tensor("custom")]; + tensor var_4921_pad_0 = const()[name = tensor("op_4921_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617025408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621940672))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621940864)))]; + tensor var_4921_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_4919, groups = var_1196, pad = var_4921_pad_0, pad_type = var_4921_pad_type_0, strides = var_4917, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = tensor("op_4921_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = var_4921_cast_fp16, y = inputs_143_cast_fp16)[name = tensor("hidden_states_197_cast_fp16")]; + tensor var_4923 = const()[name = tensor("op_4923"), val = tensor([1, 1280, 32, 32])]; + tensor input_313_cast_fp16 = reshape(shape = var_4923, x = hidden_states_197_cast_fp16)[name = tensor("input_313_cast_fp16")]; + tensor var_4927 = const()[name = tensor("op_4927"), val = tensor([1, 1])]; + tensor var_4929 = const()[name = tensor("op_4929"), 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([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(621943488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623172352))), 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(623172544)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_4929, groups = var_1196, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_4927, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_313_cast_fp16)[name = tensor("hidden_states_199_cast_fp16")]; + tensor sample_5_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_133_cast_fp16)[name = tensor("sample_5_cast_fp16")]; + tensor res_hidden_states_cast_fp16 = add(x = input_15_cast_fp16, y = additional_residual_0)[name = tensor("res_hidden_states_cast_fp16")]; + tensor res_hidden_states_15_cast_fp16 = add(x = input_31_cast_fp16, y = additional_residual_1)[name = tensor("res_hidden_states_15_cast_fp16")]; + tensor res_hidden_states_13_cast_fp16 = add(x = input_45_cast_fp16, y = additional_residual_2)[name = tensor("res_hidden_states_13_cast_fp16")]; + tensor res_hidden_states_11_cast_fp16 = add(x = input_47_cast_fp16, y = additional_residual_3)[name = tensor("res_hidden_states_11_cast_fp16")]; + tensor res_hidden_states_9_cast_fp16 = add(x = input_83_cast_fp16, y = additional_residual_4)[name = tensor("res_hidden_states_9_cast_fp16")]; + tensor res_hidden_states_7_cast_fp16 = add(x = input_117_cast_fp16, y = additional_residual_5)[name = tensor("res_hidden_states_7_cast_fp16")]; + tensor res_hidden_states_5_cast_fp16 = add(x = input_119_cast_fp16, y = additional_residual_6)[name = tensor("res_hidden_states_5_cast_fp16")]; + tensor res_hidden_states_3_cast_fp16 = add(x = input_217_cast_fp16, y = additional_residual_7)[name = tensor("res_hidden_states_3_cast_fp16")]; + tensor res_hidden_states_1_cast_fp16 = add(x = sample_5_cast_fp16, y = additional_residual_8)[name = tensor("res_hidden_states_1_cast_fp16")]; + tensor var_4955 = const()[name = tensor("op_4955"), val = tensor(3)]; + tensor var_4971 = const()[name = tensor("op_4971"), val = tensor(1)]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = sample_5_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + 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(623175168)))]; + 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(623177792)))]; + 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_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor input_317_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_317_cast_fp16")]; + tensor var_4989 = const()[name = tensor("op_4989"), val = tensor([1, 1])]; + tensor var_4991 = const()[name = tensor("op_4991"), 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 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(623180416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634239680))), 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(634239872)))]; + tensor hidden_states_201_cast_fp16 = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_4991, groups = var_4971, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_4989, weight = mid_block_resnets_0_conv1_weight_to_fp16_palettized, x = input_317_cast_fp16)[name = tensor("hidden_states_201_cast_fp16")]; + tensor var_4997 = const()[name = tensor("op_4997"), val = tensor([1, 1])]; + tensor var_4999 = const()[name = tensor("op_4999"), 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 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(634242496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635471360))), 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(635471552)))]; + tensor temb_13_cast_fp16 = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4999, groups = var_4971, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_4997, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_13_cast_fp16")]; + tensor input_321_cast_fp16 = add(x = hidden_states_201_cast_fp16, y = temb_13_cast_fp16)[name = tensor("input_321_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_321_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + 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(635474176)))]; + 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(635476800)))]; + 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_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor input_325_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor("input_325_cast_fp16")]; + tensor var_5009 = const()[name = tensor("op_5009"), val = tensor([1, 1])]; + tensor var_5011 = const()[name = tensor("op_5011"), val = tensor([1, 1])]; + tensor hidden_states_203_pad_type_0 = const()[name = tensor("hidden_states_203_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_203_pad_0 = const()[name = tensor("hidden_states_203_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(635479424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(646538688))), 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(646538880)))]; + tensor hidden_states_203_cast_fp16 = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_5011, groups = var_4971, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = var_5009, weight = mid_block_resnets_0_conv2_weight_to_fp16_palettized, x = input_325_cast_fp16)[name = tensor("hidden_states_203_cast_fp16")]; + tensor hidden_states_205_cast_fp16 = add(x = sample_5_cast_fp16, y = hidden_states_203_cast_fp16)[name = tensor("hidden_states_205_cast_fp16")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = hidden_states_205_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + 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(646541504)))]; + 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(646544128)))]; + 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_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; + tensor var_5049 = const()[name = tensor("op_5049"), val = tensor([1, 1])]; + tensor var_5051 = const()[name = tensor("op_5051"), val = tensor([1, 1])]; + tensor hidden_states_207_pad_type_0 = const()[name = tensor("hidden_states_207_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_207_pad_0 = const()[name = tensor("hidden_states_207_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(646546752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647775616))), 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(647775808)))]; + tensor hidden_states_207_cast_fp16 = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_5051, groups = var_4971, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = var_5049, weight = mid_block_attentions_0_proj_in_weight_to_fp16_palettized, x = add_37_cast_fp16)[name = tensor("hidden_states_207_cast_fp16")]; + tensor var_5056 = const()[name = tensor("op_5056"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_145_cast_fp16 = reshape(shape = var_5056, x = hidden_states_207_cast_fp16)[name = tensor("inputs_145_cast_fp16")]; + tensor hidden_states_209_axes_0 = const()[name = tensor("hidden_states_209_axes_0"), val = tensor([1])]; + tensor hidden_states_209_gamma_0_to_fp16 = const()[name = tensor("hidden_states_209_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647778432)))]; + tensor hidden_states_209_beta_0_to_fp16 = const()[name = tensor("hidden_states_209_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647781056)))]; + tensor var_5072_to_fp16 = const()[name = tensor("op_5072_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_209_cast_fp16 = layer_norm(axes = hidden_states_209_axes_0, beta = hidden_states_209_beta_0_to_fp16, epsilon = var_5072_to_fp16, gamma = hidden_states_209_gamma_0_to_fp16, x = inputs_145_cast_fp16)[name = tensor("hidden_states_209_cast_fp16")]; + tensor var_5087 = const()[name = tensor("op_5087"), val = tensor([1, 1])]; + tensor var_5089 = const()[name = tensor("op_5089"), val = tensor([1, 1])]; + tensor q_97_pad_type_0 = const()[name = tensor("q_97_pad_type_0"), val = tensor("custom")]; + tensor q_97_pad_0 = const()[name = tensor("q_97_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(647783680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(649012544))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_97_cast_fp16 = conv(dilations = var_5089, groups = var_4971, pad = q_97_pad_0, pad_type = q_97_pad_type_0, strides = var_5087, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_209_cast_fp16)[name = tensor("q_97_cast_fp16")]; + tensor var_5093 = const()[name = tensor("op_5093"), val = tensor([1, 1])]; + tensor var_5095 = const()[name = tensor("op_5095"), 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 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(649012736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(650241600))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_97_cast_fp16 = conv(dilations = var_5095, groups = var_4971, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_5093, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_209_cast_fp16)[name = tensor("k_97_cast_fp16")]; + tensor var_5099 = const()[name = tensor("op_5099"), val = tensor([1, 1])]; + tensor var_5101 = const()[name = tensor("op_5101"), val = tensor([1, 1])]; + tensor v_97_pad_type_0 = const()[name = tensor("v_97_pad_type_0"), val = tensor("custom")]; + tensor v_97_pad_0 = const()[name = tensor("v_97_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(650241792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(651470656))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_97_cast_fp16 = conv(dilations = var_5101, groups = var_4971, pad = v_97_pad_0, pad_type = v_97_pad_type_0, strides = var_5099, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_209_cast_fp16)[name = tensor("v_97_cast_fp16")]; + tensor var_5105 = const()[name = tensor("op_5105"), val = tensor([1, 20, 64, -1])]; + tensor var_5106_cast_fp16 = reshape(shape = var_5105, x = q_97_cast_fp16)[name = tensor("op_5106_cast_fp16")]; + tensor var_5107 = const()[name = tensor("op_5107"), val = tensor([1, 20, 64, -1])]; + tensor var_5108_cast_fp16 = reshape(shape = var_5107, x = k_97_cast_fp16)[name = tensor("op_5108_cast_fp16")]; + tensor var_5109 = const()[name = tensor("op_5109"), val = tensor([1, 20, 64, -1])]; + tensor var_5110_cast_fp16 = reshape(shape = var_5109, x = v_97_cast_fp16)[name = tensor("op_5110_cast_fp16")]; + tensor attn_weights_193_transpose_x_0 = const()[name = tensor("attn_weights_193_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_193_transpose_y_0 = const()[name = tensor("attn_weights_193_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_5106_cast_fp16, y = var_5108_cast_fp16)[name = tensor("attn_weights_193_cast_fp16")]; + tensor var_4962_to_fp16 = const()[name = tensor("op_4962_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_195_cast_fp16")]; + tensor var_5114_cast_fp16 = softmax(axis = var_4955, x = attn_weights_195_cast_fp16)[name = tensor("op_5114_cast_fp16")]; + tensor attn_97_transpose_x_0 = const()[name = tensor("attn_97_transpose_x_0"), val = tensor(false)]; + tensor attn_97_transpose_y_0 = const()[name = tensor("attn_97_transpose_y_0"), val = tensor(true)]; + tensor attn_97_cast_fp16 = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5110_cast_fp16, y = var_5114_cast_fp16)[name = tensor("attn_97_cast_fp16")]; + tensor var_5118 = const()[name = tensor("op_5118"), val = tensor([1, 1280, 1, -1])]; + tensor input_329_cast_fp16 = reshape(shape = var_5118, x = attn_97_cast_fp16)[name = tensor("input_329_cast_fp16")]; + tensor var_5123 = const()[name = tensor("op_5123"), val = tensor([1, 1])]; + tensor var_5125 = const()[name = tensor("op_5125"), val = tensor([1, 1])]; + tensor var_5127_pad_type_0 = const()[name = tensor("op_5127_pad_type_0"), val = tensor("custom")]; + tensor var_5127_pad_0 = const()[name = tensor("op_5127_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(651470848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652699712))), 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(652699904)))]; + tensor var_5127_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5125, groups = var_4971, pad = var_5127_pad_0, pad_type = var_5127_pad_type_0, strides = var_5123, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = tensor("op_5127_cast_fp16")]; + tensor inputs_147_cast_fp16 = add(x = var_5127_cast_fp16, y = inputs_145_cast_fp16)[name = tensor("inputs_147_cast_fp16")]; + tensor hidden_states_211_axes_0 = const()[name = tensor("hidden_states_211_axes_0"), val = tensor([1])]; + tensor hidden_states_211_gamma_0_to_fp16 = const()[name = tensor("hidden_states_211_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652702528)))]; + tensor hidden_states_211_beta_0_to_fp16 = const()[name = tensor("hidden_states_211_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652705152)))]; + tensor var_5137_to_fp16 = const()[name = tensor("op_5137_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_211_cast_fp16 = layer_norm(axes = hidden_states_211_axes_0, beta = hidden_states_211_beta_0_to_fp16, epsilon = var_5137_to_fp16, gamma = hidden_states_211_gamma_0_to_fp16, x = inputs_147_cast_fp16)[name = tensor("hidden_states_211_cast_fp16")]; + tensor var_5152 = const()[name = tensor("op_5152"), val = tensor([1, 1])]; + tensor var_5154 = const()[name = tensor("op_5154"), val = tensor([1, 1])]; + tensor q_99_pad_type_0 = const()[name = tensor("q_99_pad_type_0"), val = tensor("custom")]; + tensor q_99_pad_0 = const()[name = tensor("q_99_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(652707776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(653936640))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_99_cast_fp16 = conv(dilations = var_5154, groups = var_4971, pad = q_99_pad_0, pad_type = q_99_pad_type_0, strides = var_5152, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_211_cast_fp16)[name = tensor("q_99_cast_fp16")]; + tensor var_5158 = const()[name = tensor("op_5158"), val = tensor([1, 1])]; + tensor var_5160 = const()[name = tensor("op_5160"), val = tensor([1, 1])]; + tensor k_99_pad_type_0 = const()[name = tensor("k_99_pad_type_0"), val = tensor("custom")]; + tensor k_99_pad_0 = const()[name = tensor("k_99_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(653936832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655902976))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_99_cast_fp16 = conv(dilations = var_5160, groups = var_4971, pad = k_99_pad_0, pad_type = k_99_pad_type_0, strides = var_5158, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_99_cast_fp16")]; + tensor var_5164 = const()[name = tensor("op_5164"), val = tensor([1, 1])]; + tensor var_5166 = const()[name = tensor("op_5166"), val = tensor([1, 1])]; + tensor v_99_pad_type_0 = const()[name = tensor("v_99_pad_type_0"), val = tensor("custom")]; + tensor v_99_pad_0 = const()[name = tensor("v_99_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(655903168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(657869312))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_99_cast_fp16 = conv(dilations = var_5166, groups = var_4971, pad = v_99_pad_0, pad_type = v_99_pad_type_0, strides = var_5164, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_99_cast_fp16")]; + tensor var_5170 = const()[name = tensor("op_5170"), val = tensor([1, 20, 64, -1])]; + tensor var_5171_cast_fp16 = reshape(shape = var_5170, x = q_99_cast_fp16)[name = tensor("op_5171_cast_fp16")]; + tensor var_5172 = const()[name = tensor("op_5172"), val = tensor([1, 20, 64, -1])]; + tensor var_5173_cast_fp16 = reshape(shape = var_5172, x = k_99_cast_fp16)[name = tensor("op_5173_cast_fp16")]; + tensor var_5174 = const()[name = tensor("op_5174"), val = tensor([1, 20, 64, -1])]; + tensor var_5175_cast_fp16 = reshape(shape = var_5174, x = v_99_cast_fp16)[name = tensor("op_5175_cast_fp16")]; + tensor attn_weights_197_transpose_x_0 = const()[name = tensor("attn_weights_197_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_197_transpose_y_0 = const()[name = tensor("attn_weights_197_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_197_cast_fp16 = matmul(transpose_x = attn_weights_197_transpose_x_0, transpose_y = attn_weights_197_transpose_y_0, x = var_5171_cast_fp16, y = var_5173_cast_fp16)[name = tensor("attn_weights_197_cast_fp16")]; + tensor attn_weights_199_cast_fp16 = mul(x = attn_weights_197_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_199_cast_fp16")]; + tensor var_5179_cast_fp16 = softmax(axis = var_4955, x = attn_weights_199_cast_fp16)[name = tensor("op_5179_cast_fp16")]; + tensor attn_99_transpose_x_0 = const()[name = tensor("attn_99_transpose_x_0"), val = tensor(false)]; + tensor attn_99_transpose_y_0 = const()[name = tensor("attn_99_transpose_y_0"), val = tensor(true)]; + tensor attn_99_cast_fp16 = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5175_cast_fp16, y = var_5179_cast_fp16)[name = tensor("attn_99_cast_fp16")]; + tensor var_5183 = const()[name = tensor("op_5183"), val = tensor([1, 1280, 1, -1])]; + tensor input_331_cast_fp16 = reshape(shape = var_5183, x = attn_99_cast_fp16)[name = tensor("input_331_cast_fp16")]; + tensor var_5188 = const()[name = tensor("op_5188"), val = tensor([1, 1])]; + tensor var_5190 = const()[name = tensor("op_5190"), val = tensor([1, 1])]; + tensor var_5192_pad_type_0 = const()[name = tensor("op_5192_pad_type_0"), val = tensor("custom")]; + tensor var_5192_pad_0 = const()[name = tensor("op_5192_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(657869504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659098368))), 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(659098560)))]; + tensor var_5192_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5190, groups = var_4971, pad = var_5192_pad_0, pad_type = var_5192_pad_type_0, strides = var_5188, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = tensor("op_5192_cast_fp16")]; + tensor inputs_149_cast_fp16 = add(x = var_5192_cast_fp16, y = inputs_147_cast_fp16)[name = tensor("inputs_149_cast_fp16")]; + tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([1])]; + tensor input_333_gamma_0_to_fp16 = const()[name = tensor("input_333_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659101184)))]; + tensor input_333_beta_0_to_fp16 = const()[name = tensor("input_333_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659103808)))]; + tensor var_5202_to_fp16 = const()[name = tensor("op_5202_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = input_333_beta_0_to_fp16, epsilon = var_5202_to_fp16, gamma = input_333_gamma_0_to_fp16, x = inputs_149_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor var_5218 = const()[name = tensor("op_5218"), val = tensor([1, 1])]; + tensor var_5220 = const()[name = tensor("op_5220"), val = tensor([1, 1])]; + tensor var_5222_pad_type_0 = const()[name = tensor("op_5222_pad_type_0"), val = tensor("custom")]; + tensor var_5222_pad_0 = const()[name = tensor("op_5222_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(659106432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668936896))), 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(668937088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668944832))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5222_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5220, groups = var_4971, pad = var_5222_pad_0, pad_type = var_5222_pad_type_0, strides = var_5218, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_333_cast_fp16)[name = tensor("op_5222_cast_fp16")]; + tensor var_5223_split_sizes_0 = const()[name = tensor("op_5223_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5223_axis_0 = const()[name = tensor("op_5223_axis_0"), val = tensor(1)]; + tensor var_5223_cast_fp16_0, tensor var_5223_cast_fp16_1 = split(axis = var_5223_axis_0, split_sizes = var_5223_split_sizes_0, x = var_5222_cast_fp16)[name = tensor("op_5223_cast_fp16")]; + tensor var_5225_mode_0 = const()[name = tensor("op_5225_mode_0"), val = tensor("EXACT")]; + tensor var_5225_cast_fp16 = gelu(mode = var_5225_mode_0, x = var_5223_cast_fp16_1)[name = tensor("op_5225_cast_fp16")]; + tensor input_335_cast_fp16 = mul(x = var_5223_cast_fp16_0, y = var_5225_cast_fp16)[name = tensor("input_335_cast_fp16")]; + tensor var_5229 = const()[name = tensor("op_5229"), val = tensor([1, 1])]; + tensor var_5231 = const()[name = tensor("op_5231"), val = tensor([1, 1])]; + tensor var_5233_pad_type_0 = const()[name = tensor("op_5233_pad_type_0"), val = tensor("custom")]; + tensor var_5233_pad_0 = const()[name = tensor("op_5233_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(668945024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673860288))), 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(673860480)))]; + tensor var_5233_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5231, groups = var_4971, pad = var_5233_pad_0, pad_type = var_5233_pad_type_0, strides = var_5229, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = tensor("op_5233_cast_fp16")]; + tensor inputs_151_cast_fp16 = add(x = var_5233_cast_fp16, y = inputs_149_cast_fp16)[name = tensor("inputs_151_cast_fp16")]; + tensor hidden_states_215_axes_0 = const()[name = tensor("hidden_states_215_axes_0"), val = tensor([1])]; + tensor hidden_states_215_gamma_0_to_fp16 = const()[name = tensor("hidden_states_215_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673863104)))]; + tensor hidden_states_215_beta_0_to_fp16 = const()[name = tensor("hidden_states_215_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673865728)))]; + tensor var_5249_to_fp16 = const()[name = tensor("op_5249_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_215_cast_fp16 = layer_norm(axes = hidden_states_215_axes_0, beta = hidden_states_215_beta_0_to_fp16, epsilon = var_5249_to_fp16, gamma = hidden_states_215_gamma_0_to_fp16, x = inputs_151_cast_fp16)[name = tensor("hidden_states_215_cast_fp16")]; + tensor var_5264 = const()[name = tensor("op_5264"), val = tensor([1, 1])]; + tensor var_5266 = const()[name = tensor("op_5266"), val = tensor([1, 1])]; + tensor q_101_pad_type_0 = const()[name = tensor("q_101_pad_type_0"), val = tensor("custom")]; + tensor q_101_pad_0 = const()[name = tensor("q_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673868352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(675097216))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_101_cast_fp16 = conv(dilations = var_5266, groups = var_4971, pad = q_101_pad_0, pad_type = q_101_pad_type_0, strides = var_5264, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_215_cast_fp16)[name = tensor("q_101_cast_fp16")]; + tensor var_5270 = const()[name = tensor("op_5270"), val = tensor([1, 1])]; + tensor var_5272 = const()[name = tensor("op_5272"), 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 mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(675097408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(676326272))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_101_cast_fp16 = conv(dilations = var_5272, groups = var_4971, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_5270, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_215_cast_fp16)[name = tensor("k_101_cast_fp16")]; + tensor var_5276 = const()[name = tensor("op_5276"), val = tensor([1, 1])]; + tensor var_5278 = const()[name = tensor("op_5278"), val = tensor([1, 1])]; + tensor v_101_pad_type_0 = const()[name = tensor("v_101_pad_type_0"), val = tensor("custom")]; + tensor v_101_pad_0 = const()[name = tensor("v_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(676326464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677555328))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_101_cast_fp16 = conv(dilations = var_5278, groups = var_4971, pad = v_101_pad_0, pad_type = v_101_pad_type_0, strides = var_5276, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_215_cast_fp16)[name = tensor("v_101_cast_fp16")]; + tensor var_5282 = const()[name = tensor("op_5282"), val = tensor([1, 20, 64, -1])]; + tensor var_5283_cast_fp16 = reshape(shape = var_5282, x = q_101_cast_fp16)[name = tensor("op_5283_cast_fp16")]; + tensor var_5284 = const()[name = tensor("op_5284"), val = tensor([1, 20, 64, -1])]; + tensor var_5285_cast_fp16 = reshape(shape = var_5284, x = k_101_cast_fp16)[name = tensor("op_5285_cast_fp16")]; + tensor var_5286 = const()[name = tensor("op_5286"), val = tensor([1, 20, 64, -1])]; + tensor var_5287_cast_fp16 = reshape(shape = var_5286, x = v_101_cast_fp16)[name = tensor("op_5287_cast_fp16")]; + tensor attn_weights_201_transpose_x_0 = const()[name = tensor("attn_weights_201_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_201_transpose_y_0 = const()[name = tensor("attn_weights_201_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_5283_cast_fp16, y = var_5285_cast_fp16)[name = tensor("attn_weights_201_cast_fp16")]; + tensor attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_203_cast_fp16")]; + tensor var_5291_cast_fp16 = softmax(axis = var_4955, x = attn_weights_203_cast_fp16)[name = tensor("op_5291_cast_fp16")]; + tensor attn_101_transpose_x_0 = const()[name = tensor("attn_101_transpose_x_0"), val = tensor(false)]; + tensor attn_101_transpose_y_0 = const()[name = tensor("attn_101_transpose_y_0"), val = tensor(true)]; + tensor attn_101_cast_fp16 = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5287_cast_fp16, y = var_5291_cast_fp16)[name = tensor("attn_101_cast_fp16")]; + tensor var_5295 = const()[name = tensor("op_5295"), val = tensor([1, 1280, 1, -1])]; + tensor input_337_cast_fp16 = reshape(shape = var_5295, x = attn_101_cast_fp16)[name = tensor("input_337_cast_fp16")]; + tensor var_5300 = const()[name = tensor("op_5300"), val = tensor([1, 1])]; + tensor var_5302 = const()[name = tensor("op_5302"), val = tensor([1, 1])]; + tensor var_5304_pad_type_0 = const()[name = tensor("op_5304_pad_type_0"), val = tensor("custom")]; + tensor var_5304_pad_0 = const()[name = tensor("op_5304_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677555520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678784384))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678784576)))]; + tensor var_5304_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_5302, groups = var_4971, pad = var_5304_pad_0, pad_type = var_5304_pad_type_0, strides = var_5300, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor("op_5304_cast_fp16")]; + tensor inputs_153_cast_fp16 = add(x = var_5304_cast_fp16, y = inputs_151_cast_fp16)[name = tensor("inputs_153_cast_fp16")]; + tensor hidden_states_217_axes_0 = const()[name = tensor("hidden_states_217_axes_0"), val = tensor([1])]; + tensor hidden_states_217_gamma_0_to_fp16 = const()[name = tensor("hidden_states_217_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678787200)))]; + tensor hidden_states_217_beta_0_to_fp16 = const()[name = tensor("hidden_states_217_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678789824)))]; + tensor var_5314_to_fp16 = const()[name = tensor("op_5314_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_217_cast_fp16 = layer_norm(axes = hidden_states_217_axes_0, beta = hidden_states_217_beta_0_to_fp16, epsilon = var_5314_to_fp16, gamma = hidden_states_217_gamma_0_to_fp16, x = inputs_153_cast_fp16)[name = tensor("hidden_states_217_cast_fp16")]; + tensor var_5329 = const()[name = tensor("op_5329"), val = tensor([1, 1])]; + tensor var_5331 = const()[name = tensor("op_5331"), val = tensor([1, 1])]; + tensor q_103_pad_type_0 = const()[name = tensor("q_103_pad_type_0"), val = tensor("custom")]; + tensor q_103_pad_0 = const()[name = tensor("q_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678792448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680021312))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_103_cast_fp16 = conv(dilations = var_5331, groups = var_4971, pad = q_103_pad_0, pad_type = q_103_pad_type_0, strides = var_5329, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_217_cast_fp16)[name = tensor("q_103_cast_fp16")]; + tensor var_5335 = const()[name = tensor("op_5335"), val = tensor([1, 1])]; + tensor var_5337 = const()[name = tensor("op_5337"), val = tensor([1, 1])]; + tensor k_103_pad_type_0 = const()[name = tensor("k_103_pad_type_0"), val = tensor("custom")]; + tensor k_103_pad_0 = const()[name = tensor("k_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680021504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(681987648))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_103_cast_fp16 = conv(dilations = var_5337, groups = var_4971, pad = k_103_pad_0, pad_type = k_103_pad_type_0, strides = var_5335, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_103_cast_fp16")]; + tensor var_5341 = const()[name = tensor("op_5341"), val = tensor([1, 1])]; + tensor var_5343 = const()[name = tensor("op_5343"), val = tensor([1, 1])]; + tensor v_103_pad_type_0 = const()[name = tensor("v_103_pad_type_0"), val = tensor("custom")]; + tensor v_103_pad_0 = const()[name = tensor("v_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(681987840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683953984))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_103_cast_fp16 = conv(dilations = var_5343, groups = var_4971, pad = v_103_pad_0, pad_type = v_103_pad_type_0, strides = var_5341, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_103_cast_fp16")]; + tensor var_5347 = const()[name = tensor("op_5347"), val = tensor([1, 20, 64, -1])]; + tensor var_5348_cast_fp16 = reshape(shape = var_5347, x = q_103_cast_fp16)[name = tensor("op_5348_cast_fp16")]; + tensor var_5349 = const()[name = tensor("op_5349"), val = tensor([1, 20, 64, -1])]; + tensor var_5350_cast_fp16 = reshape(shape = var_5349, x = k_103_cast_fp16)[name = tensor("op_5350_cast_fp16")]; + tensor var_5351 = const()[name = tensor("op_5351"), val = tensor([1, 20, 64, -1])]; + tensor var_5352_cast_fp16 = reshape(shape = var_5351, x = v_103_cast_fp16)[name = tensor("op_5352_cast_fp16")]; + tensor attn_weights_205_transpose_x_0 = const()[name = tensor("attn_weights_205_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_205_transpose_y_0 = const()[name = tensor("attn_weights_205_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_205_cast_fp16 = matmul(transpose_x = attn_weights_205_transpose_x_0, transpose_y = attn_weights_205_transpose_y_0, x = var_5348_cast_fp16, y = var_5350_cast_fp16)[name = tensor("attn_weights_205_cast_fp16")]; + tensor attn_weights_207_cast_fp16 = mul(x = attn_weights_205_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_207_cast_fp16")]; + tensor var_5356_cast_fp16 = softmax(axis = var_4955, x = attn_weights_207_cast_fp16)[name = tensor("op_5356_cast_fp16")]; + tensor attn_103_transpose_x_0 = const()[name = tensor("attn_103_transpose_x_0"), val = tensor(false)]; + tensor attn_103_transpose_y_0 = const()[name = tensor("attn_103_transpose_y_0"), val = tensor(true)]; + tensor attn_103_cast_fp16 = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5352_cast_fp16, y = var_5356_cast_fp16)[name = tensor("attn_103_cast_fp16")]; + tensor var_5360 = const()[name = tensor("op_5360"), val = tensor([1, 1280, 1, -1])]; + tensor input_339_cast_fp16 = reshape(shape = var_5360, x = attn_103_cast_fp16)[name = tensor("input_339_cast_fp16")]; + tensor var_5365 = const()[name = tensor("op_5365"), val = tensor([1, 1])]; + tensor var_5367 = const()[name = tensor("op_5367"), val = tensor([1, 1])]; + tensor var_5369_pad_type_0 = const()[name = tensor("op_5369_pad_type_0"), val = tensor("custom")]; + tensor var_5369_pad_0 = const()[name = tensor("op_5369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683954176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685183040))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685183232)))]; + tensor var_5369_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_5367, groups = var_4971, pad = var_5369_pad_0, pad_type = var_5369_pad_type_0, strides = var_5365, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_339_cast_fp16)[name = tensor("op_5369_cast_fp16")]; + tensor inputs_155_cast_fp16 = add(x = var_5369_cast_fp16, y = inputs_153_cast_fp16)[name = tensor("inputs_155_cast_fp16")]; + tensor input_341_axes_0 = const()[name = tensor("input_341_axes_0"), val = tensor([1])]; + tensor input_341_gamma_0_to_fp16 = const()[name = tensor("input_341_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685185856)))]; + tensor input_341_beta_0_to_fp16 = const()[name = tensor("input_341_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685188480)))]; + tensor var_5379_to_fp16 = const()[name = tensor("op_5379_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_341_cast_fp16 = layer_norm(axes = input_341_axes_0, beta = input_341_beta_0_to_fp16, epsilon = var_5379_to_fp16, gamma = input_341_gamma_0_to_fp16, x = inputs_155_cast_fp16)[name = tensor("input_341_cast_fp16")]; + 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 var_5399_pad_type_0 = const()[name = tensor("op_5399_pad_type_0"), val = tensor("custom")]; + tensor var_5399_pad_0 = const()[name = tensor("op_5399_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685191104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695021568))), name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695021760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695029504))), name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5399_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5397, groups = var_4971, pad = var_5399_pad_0, pad_type = var_5399_pad_type_0, strides = var_5395, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_341_cast_fp16)[name = tensor("op_5399_cast_fp16")]; + tensor var_5400_split_sizes_0 = const()[name = tensor("op_5400_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5400_axis_0 = const()[name = tensor("op_5400_axis_0"), val = tensor(1)]; + tensor var_5400_cast_fp16_0, tensor var_5400_cast_fp16_1 = split(axis = var_5400_axis_0, split_sizes = var_5400_split_sizes_0, x = var_5399_cast_fp16)[name = tensor("op_5400_cast_fp16")]; + tensor var_5402_mode_0 = const()[name = tensor("op_5402_mode_0"), val = tensor("EXACT")]; + tensor var_5402_cast_fp16 = gelu(mode = var_5402_mode_0, x = var_5400_cast_fp16_1)[name = tensor("op_5402_cast_fp16")]; + tensor input_343_cast_fp16 = mul(x = var_5400_cast_fp16_0, y = var_5402_cast_fp16)[name = tensor("input_343_cast_fp16")]; + tensor var_5406 = const()[name = tensor("op_5406"), val = tensor([1, 1])]; + tensor var_5408 = const()[name = tensor("op_5408"), val = tensor([1, 1])]; + tensor var_5410_pad_type_0 = const()[name = tensor("op_5410_pad_type_0"), val = tensor("custom")]; + tensor var_5410_pad_0 = const()[name = tensor("op_5410_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695029696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699944960))), name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699945152)))]; + tensor var_5410_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_5408, groups = var_4971, pad = var_5410_pad_0, pad_type = var_5410_pad_type_0, strides = var_5406, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_343_cast_fp16)[name = tensor("op_5410_cast_fp16")]; + tensor inputs_157_cast_fp16 = add(x = var_5410_cast_fp16, y = inputs_155_cast_fp16)[name = tensor("inputs_157_cast_fp16")]; + tensor hidden_states_221_axes_0 = const()[name = tensor("hidden_states_221_axes_0"), val = tensor([1])]; + tensor hidden_states_221_gamma_0_to_fp16 = const()[name = tensor("hidden_states_221_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699947776)))]; + tensor hidden_states_221_beta_0_to_fp16 = const()[name = tensor("hidden_states_221_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699950400)))]; + tensor var_5426_to_fp16 = const()[name = tensor("op_5426_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_221_cast_fp16 = layer_norm(axes = hidden_states_221_axes_0, beta = hidden_states_221_beta_0_to_fp16, epsilon = var_5426_to_fp16, gamma = hidden_states_221_gamma_0_to_fp16, x = inputs_157_cast_fp16)[name = tensor("hidden_states_221_cast_fp16")]; + tensor var_5441 = const()[name = tensor("op_5441"), val = tensor([1, 1])]; + tensor var_5443 = const()[name = tensor("op_5443"), val = tensor([1, 1])]; + tensor q_105_pad_type_0 = const()[name = tensor("q_105_pad_type_0"), val = tensor("custom")]; + tensor q_105_pad_0 = const()[name = tensor("q_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699953024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(701181888))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_105_cast_fp16 = conv(dilations = var_5443, groups = var_4971, pad = q_105_pad_0, pad_type = q_105_pad_type_0, strides = var_5441, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_221_cast_fp16)[name = tensor("q_105_cast_fp16")]; + tensor var_5447 = const()[name = tensor("op_5447"), val = tensor([1, 1])]; + tensor var_5449 = const()[name = tensor("op_5449"), 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 mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(701182080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(702410944))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_105_cast_fp16 = conv(dilations = var_5449, groups = var_4971, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_5447, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_221_cast_fp16)[name = tensor("k_105_cast_fp16")]; + tensor var_5453 = const()[name = tensor("op_5453"), val = tensor([1, 1])]; + tensor var_5455 = const()[name = tensor("op_5455"), val = tensor([1, 1])]; + tensor v_105_pad_type_0 = const()[name = tensor("v_105_pad_type_0"), val = tensor("custom")]; + tensor v_105_pad_0 = const()[name = tensor("v_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(702411136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703640000))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_105_cast_fp16 = conv(dilations = var_5455, groups = var_4971, pad = v_105_pad_0, pad_type = v_105_pad_type_0, strides = var_5453, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_221_cast_fp16)[name = tensor("v_105_cast_fp16")]; + tensor var_5459 = const()[name = tensor("op_5459"), val = tensor([1, 20, 64, -1])]; + tensor var_5460_cast_fp16 = reshape(shape = var_5459, x = q_105_cast_fp16)[name = tensor("op_5460_cast_fp16")]; + tensor var_5461 = const()[name = tensor("op_5461"), val = tensor([1, 20, 64, -1])]; + tensor var_5462_cast_fp16 = reshape(shape = var_5461, x = k_105_cast_fp16)[name = tensor("op_5462_cast_fp16")]; + tensor var_5463 = const()[name = tensor("op_5463"), val = tensor([1, 20, 64, -1])]; + tensor var_5464_cast_fp16 = reshape(shape = var_5463, x = v_105_cast_fp16)[name = tensor("op_5464_cast_fp16")]; + tensor attn_weights_209_transpose_x_0 = const()[name = tensor("attn_weights_209_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_209_transpose_y_0 = const()[name = tensor("attn_weights_209_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5460_cast_fp16, y = var_5462_cast_fp16)[name = tensor("attn_weights_209_cast_fp16")]; + tensor attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_211_cast_fp16")]; + tensor var_5468_cast_fp16 = softmax(axis = var_4955, x = attn_weights_211_cast_fp16)[name = tensor("op_5468_cast_fp16")]; + tensor attn_105_transpose_x_0 = const()[name = tensor("attn_105_transpose_x_0"), val = tensor(false)]; + tensor attn_105_transpose_y_0 = const()[name = tensor("attn_105_transpose_y_0"), val = tensor(true)]; + tensor attn_105_cast_fp16 = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5464_cast_fp16, y = var_5468_cast_fp16)[name = tensor("attn_105_cast_fp16")]; + tensor var_5472 = const()[name = tensor("op_5472"), val = tensor([1, 1280, 1, -1])]; + tensor input_345_cast_fp16 = reshape(shape = var_5472, x = attn_105_cast_fp16)[name = tensor("input_345_cast_fp16")]; + tensor var_5477 = const()[name = tensor("op_5477"), val = tensor([1, 1])]; + tensor var_5479 = const()[name = tensor("op_5479"), val = tensor([1, 1])]; + tensor var_5481_pad_type_0 = const()[name = tensor("op_5481_pad_type_0"), val = tensor("custom")]; + tensor var_5481_pad_0 = const()[name = tensor("op_5481_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703640192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704869056))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704869248)))]; + tensor var_5481_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_5479, groups = var_4971, pad = var_5481_pad_0, pad_type = var_5481_pad_type_0, strides = var_5477, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = tensor("op_5481_cast_fp16")]; + tensor inputs_159_cast_fp16 = add(x = var_5481_cast_fp16, y = inputs_157_cast_fp16)[name = tensor("inputs_159_cast_fp16")]; + tensor hidden_states_223_axes_0 = const()[name = tensor("hidden_states_223_axes_0"), val = tensor([1])]; + tensor hidden_states_223_gamma_0_to_fp16 = const()[name = tensor("hidden_states_223_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704871872)))]; + tensor hidden_states_223_beta_0_to_fp16 = const()[name = tensor("hidden_states_223_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704874496)))]; + tensor var_5491_to_fp16 = const()[name = tensor("op_5491_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_223_cast_fp16 = layer_norm(axes = hidden_states_223_axes_0, beta = hidden_states_223_beta_0_to_fp16, epsilon = var_5491_to_fp16, gamma = hidden_states_223_gamma_0_to_fp16, x = inputs_159_cast_fp16)[name = tensor("hidden_states_223_cast_fp16")]; + tensor var_5506 = const()[name = tensor("op_5506"), val = tensor([1, 1])]; + tensor var_5508 = const()[name = tensor("op_5508"), val = tensor([1, 1])]; + tensor q_107_pad_type_0 = const()[name = tensor("q_107_pad_type_0"), val = tensor("custom")]; + tensor q_107_pad_0 = const()[name = tensor("q_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704877120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706105984))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_107_cast_fp16 = conv(dilations = var_5508, groups = var_4971, pad = q_107_pad_0, pad_type = q_107_pad_type_0, strides = var_5506, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_223_cast_fp16)[name = tensor("q_107_cast_fp16")]; + tensor var_5512 = const()[name = tensor("op_5512"), val = tensor([1, 1])]; + tensor var_5514 = const()[name = tensor("op_5514"), val = tensor([1, 1])]; + tensor k_107_pad_type_0 = const()[name = tensor("k_107_pad_type_0"), val = tensor("custom")]; + tensor k_107_pad_0 = const()[name = tensor("k_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706106176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(708072320))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_107_cast_fp16 = conv(dilations = var_5514, groups = var_4971, pad = k_107_pad_0, pad_type = k_107_pad_type_0, strides = var_5512, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_107_cast_fp16")]; + tensor var_5518 = const()[name = tensor("op_5518"), val = tensor([1, 1])]; + tensor var_5520 = const()[name = tensor("op_5520"), val = tensor([1, 1])]; + tensor v_107_pad_type_0 = const()[name = tensor("v_107_pad_type_0"), val = tensor("custom")]; + tensor v_107_pad_0 = const()[name = tensor("v_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(708072512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(710038656))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_107_cast_fp16 = conv(dilations = var_5520, groups = var_4971, pad = v_107_pad_0, pad_type = v_107_pad_type_0, strides = var_5518, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_107_cast_fp16")]; + tensor var_5524 = const()[name = tensor("op_5524"), val = tensor([1, 20, 64, -1])]; + tensor var_5525_cast_fp16 = reshape(shape = var_5524, x = q_107_cast_fp16)[name = tensor("op_5525_cast_fp16")]; + tensor var_5526 = const()[name = tensor("op_5526"), val = tensor([1, 20, 64, -1])]; + tensor var_5527_cast_fp16 = reshape(shape = var_5526, x = k_107_cast_fp16)[name = tensor("op_5527_cast_fp16")]; + tensor var_5528 = const()[name = tensor("op_5528"), val = tensor([1, 20, 64, -1])]; + tensor var_5529_cast_fp16 = reshape(shape = var_5528, x = v_107_cast_fp16)[name = tensor("op_5529_cast_fp16")]; + tensor attn_weights_213_transpose_x_0 = const()[name = tensor("attn_weights_213_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_213_transpose_y_0 = const()[name = tensor("attn_weights_213_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_213_cast_fp16 = matmul(transpose_x = attn_weights_213_transpose_x_0, transpose_y = attn_weights_213_transpose_y_0, x = var_5525_cast_fp16, y = var_5527_cast_fp16)[name = tensor("attn_weights_213_cast_fp16")]; + tensor attn_weights_215_cast_fp16 = mul(x = attn_weights_213_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_215_cast_fp16")]; + tensor var_5533_cast_fp16 = softmax(axis = var_4955, x = attn_weights_215_cast_fp16)[name = tensor("op_5533_cast_fp16")]; + tensor attn_107_transpose_x_0 = const()[name = tensor("attn_107_transpose_x_0"), val = tensor(false)]; + tensor attn_107_transpose_y_0 = const()[name = tensor("attn_107_transpose_y_0"), val = tensor(true)]; + tensor attn_107_cast_fp16 = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5529_cast_fp16, y = var_5533_cast_fp16)[name = tensor("attn_107_cast_fp16")]; + tensor var_5537 = const()[name = tensor("op_5537"), val = tensor([1, 1280, 1, -1])]; + tensor input_347_cast_fp16 = reshape(shape = var_5537, x = attn_107_cast_fp16)[name = tensor("input_347_cast_fp16")]; + tensor var_5542 = const()[name = tensor("op_5542"), val = tensor([1, 1])]; + tensor var_5544 = const()[name = tensor("op_5544"), val = tensor([1, 1])]; + tensor var_5546_pad_type_0 = const()[name = tensor("op_5546_pad_type_0"), val = tensor("custom")]; + tensor var_5546_pad_0 = const()[name = tensor("op_5546_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(710038848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711267712))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711267904)))]; + tensor var_5546_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_5544, groups = var_4971, pad = var_5546_pad_0, pad_type = var_5546_pad_type_0, strides = var_5542, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_347_cast_fp16)[name = tensor("op_5546_cast_fp16")]; + tensor inputs_161_cast_fp16 = add(x = var_5546_cast_fp16, y = inputs_159_cast_fp16)[name = tensor("inputs_161_cast_fp16")]; + tensor input_349_axes_0 = const()[name = tensor("input_349_axes_0"), val = tensor([1])]; + tensor input_349_gamma_0_to_fp16 = const()[name = tensor("input_349_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711270528)))]; + tensor input_349_beta_0_to_fp16 = const()[name = tensor("input_349_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711273152)))]; + tensor var_5556_to_fp16 = const()[name = tensor("op_5556_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_349_cast_fp16 = layer_norm(axes = input_349_axes_0, beta = input_349_beta_0_to_fp16, epsilon = var_5556_to_fp16, gamma = input_349_gamma_0_to_fp16, x = inputs_161_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor var_5572 = const()[name = tensor("op_5572"), val = tensor([1, 1])]; + tensor var_5574 = const()[name = tensor("op_5574"), val = tensor([1, 1])]; + tensor var_5576_pad_type_0 = const()[name = tensor("op_5576_pad_type_0"), val = tensor("custom")]; + tensor var_5576_pad_0 = const()[name = tensor("op_5576_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711275776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721106240))), name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721106432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721114176))), name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5576_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5574, groups = var_4971, pad = var_5576_pad_0, pad_type = var_5576_pad_type_0, strides = var_5572, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = tensor("op_5576_cast_fp16")]; + tensor var_5577_split_sizes_0 = const()[name = tensor("op_5577_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5577_axis_0 = const()[name = tensor("op_5577_axis_0"), val = tensor(1)]; + tensor var_5577_cast_fp16_0, tensor var_5577_cast_fp16_1 = split(axis = var_5577_axis_0, split_sizes = var_5577_split_sizes_0, x = var_5576_cast_fp16)[name = tensor("op_5577_cast_fp16")]; + tensor var_5579_mode_0 = const()[name = tensor("op_5579_mode_0"), val = tensor("EXACT")]; + tensor var_5579_cast_fp16 = gelu(mode = var_5579_mode_0, x = var_5577_cast_fp16_1)[name = tensor("op_5579_cast_fp16")]; + tensor input_351_cast_fp16 = mul(x = var_5577_cast_fp16_0, y = var_5579_cast_fp16)[name = tensor("input_351_cast_fp16")]; + tensor var_5583 = const()[name = tensor("op_5583"), val = tensor([1, 1])]; + tensor var_5585 = const()[name = tensor("op_5585"), val = tensor([1, 1])]; + tensor var_5587_pad_type_0 = const()[name = tensor("op_5587_pad_type_0"), val = tensor("custom")]; + tensor var_5587_pad_0 = const()[name = tensor("op_5587_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721114368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726029632))), name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726029824)))]; + tensor var_5587_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_5585, groups = var_4971, pad = var_5587_pad_0, pad_type = var_5587_pad_type_0, strides = var_5583, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = tensor("op_5587_cast_fp16")]; + tensor inputs_163_cast_fp16 = add(x = var_5587_cast_fp16, y = inputs_161_cast_fp16)[name = tensor("inputs_163_cast_fp16")]; + tensor hidden_states_227_axes_0 = const()[name = tensor("hidden_states_227_axes_0"), val = tensor([1])]; + tensor hidden_states_227_gamma_0_to_fp16 = const()[name = tensor("hidden_states_227_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726032448)))]; + tensor hidden_states_227_beta_0_to_fp16 = const()[name = tensor("hidden_states_227_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726035072)))]; + tensor var_5603_to_fp16 = const()[name = tensor("op_5603_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_227_cast_fp16 = layer_norm(axes = hidden_states_227_axes_0, beta = hidden_states_227_beta_0_to_fp16, epsilon = var_5603_to_fp16, gamma = hidden_states_227_gamma_0_to_fp16, x = inputs_163_cast_fp16)[name = tensor("hidden_states_227_cast_fp16")]; + tensor var_5618 = const()[name = tensor("op_5618"), val = tensor([1, 1])]; + tensor var_5620 = const()[name = tensor("op_5620"), val = tensor([1, 1])]; + tensor q_109_pad_type_0 = const()[name = tensor("q_109_pad_type_0"), val = tensor("custom")]; + tensor q_109_pad_0 = const()[name = tensor("q_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726037696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(727266560))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_109_cast_fp16 = conv(dilations = var_5620, groups = var_4971, pad = q_109_pad_0, pad_type = q_109_pad_type_0, strides = var_5618, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("q_109_cast_fp16")]; + tensor var_5624 = const()[name = tensor("op_5624"), val = tensor([1, 1])]; + tensor var_5626 = const()[name = tensor("op_5626"), 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 mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(727266752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728495616))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_109_cast_fp16 = conv(dilations = var_5626, groups = var_4971, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_5624, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("k_109_cast_fp16")]; + tensor var_5630 = const()[name = tensor("op_5630"), val = tensor([1, 1])]; + tensor var_5632 = const()[name = tensor("op_5632"), val = tensor([1, 1])]; + tensor v_109_pad_type_0 = const()[name = tensor("v_109_pad_type_0"), val = tensor("custom")]; + tensor v_109_pad_0 = const()[name = tensor("v_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728495808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(729724672))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_109_cast_fp16 = conv(dilations = var_5632, groups = var_4971, pad = v_109_pad_0, pad_type = v_109_pad_type_0, strides = var_5630, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("v_109_cast_fp16")]; + tensor var_5636 = const()[name = tensor("op_5636"), val = tensor([1, 20, 64, -1])]; + tensor var_5637_cast_fp16 = reshape(shape = var_5636, x = q_109_cast_fp16)[name = tensor("op_5637_cast_fp16")]; + tensor var_5638 = const()[name = tensor("op_5638"), val = tensor([1, 20, 64, -1])]; + tensor var_5639_cast_fp16 = reshape(shape = var_5638, x = k_109_cast_fp16)[name = tensor("op_5639_cast_fp16")]; + tensor var_5640 = const()[name = tensor("op_5640"), val = tensor([1, 20, 64, -1])]; + tensor var_5641_cast_fp16 = reshape(shape = var_5640, x = v_109_cast_fp16)[name = tensor("op_5641_cast_fp16")]; + tensor attn_weights_217_transpose_x_0 = const()[name = tensor("attn_weights_217_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_217_transpose_y_0 = const()[name = tensor("attn_weights_217_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5637_cast_fp16, y = var_5639_cast_fp16)[name = tensor("attn_weights_217_cast_fp16")]; + tensor attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_219_cast_fp16")]; + tensor var_5645_cast_fp16 = softmax(axis = var_4955, x = attn_weights_219_cast_fp16)[name = tensor("op_5645_cast_fp16")]; + tensor attn_109_transpose_x_0 = const()[name = tensor("attn_109_transpose_x_0"), val = tensor(false)]; + tensor attn_109_transpose_y_0 = const()[name = tensor("attn_109_transpose_y_0"), val = tensor(true)]; + tensor attn_109_cast_fp16 = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_5641_cast_fp16, y = var_5645_cast_fp16)[name = tensor("attn_109_cast_fp16")]; + tensor var_5649 = const()[name = tensor("op_5649"), val = tensor([1, 1280, 1, -1])]; + tensor input_353_cast_fp16 = reshape(shape = var_5649, x = attn_109_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor var_5654 = const()[name = tensor("op_5654"), val = tensor([1, 1])]; + tensor var_5656 = const()[name = tensor("op_5656"), val = tensor([1, 1])]; + tensor var_5658_pad_type_0 = const()[name = tensor("op_5658_pad_type_0"), val = tensor("custom")]; + tensor var_5658_pad_0 = const()[name = tensor("op_5658_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(729724864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730953728))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730953920)))]; + tensor var_5658_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_5656, groups = var_4971, pad = var_5658_pad_0, pad_type = var_5658_pad_type_0, strides = var_5654, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = tensor("op_5658_cast_fp16")]; + tensor inputs_165_cast_fp16 = add(x = var_5658_cast_fp16, y = inputs_163_cast_fp16)[name = tensor("inputs_165_cast_fp16")]; + tensor hidden_states_229_axes_0 = const()[name = tensor("hidden_states_229_axes_0"), val = tensor([1])]; + tensor hidden_states_229_gamma_0_to_fp16 = const()[name = tensor("hidden_states_229_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730956544)))]; + tensor hidden_states_229_beta_0_to_fp16 = const()[name = tensor("hidden_states_229_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730959168)))]; + tensor var_5668_to_fp16 = const()[name = tensor("op_5668_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_229_cast_fp16 = layer_norm(axes = hidden_states_229_axes_0, beta = hidden_states_229_beta_0_to_fp16, epsilon = var_5668_to_fp16, gamma = hidden_states_229_gamma_0_to_fp16, x = inputs_165_cast_fp16)[name = tensor("hidden_states_229_cast_fp16")]; + tensor var_5683 = const()[name = tensor("op_5683"), val = tensor([1, 1])]; + tensor var_5685 = const()[name = tensor("op_5685"), val = tensor([1, 1])]; + tensor q_111_pad_type_0 = const()[name = tensor("q_111_pad_type_0"), val = tensor("custom")]; + tensor q_111_pad_0 = const()[name = tensor("q_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730961792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(732190656))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_111_cast_fp16 = conv(dilations = var_5685, groups = var_4971, pad = q_111_pad_0, pad_type = q_111_pad_type_0, strides = var_5683, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_229_cast_fp16)[name = tensor("q_111_cast_fp16")]; + tensor var_5689 = const()[name = tensor("op_5689"), val = tensor([1, 1])]; + tensor var_5691 = const()[name = tensor("op_5691"), val = tensor([1, 1])]; + tensor k_111_pad_type_0 = const()[name = tensor("k_111_pad_type_0"), val = tensor("custom")]; + tensor k_111_pad_0 = const()[name = tensor("k_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(732190848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(734156992))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_111_cast_fp16 = conv(dilations = var_5691, groups = var_4971, pad = k_111_pad_0, pad_type = k_111_pad_type_0, strides = var_5689, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_111_cast_fp16")]; + tensor var_5695 = const()[name = tensor("op_5695"), val = tensor([1, 1])]; + tensor var_5697 = const()[name = tensor("op_5697"), val = tensor([1, 1])]; + tensor v_111_pad_type_0 = const()[name = tensor("v_111_pad_type_0"), val = tensor("custom")]; + tensor v_111_pad_0 = const()[name = tensor("v_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(734157184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736123328))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_111_cast_fp16 = conv(dilations = var_5697, groups = var_4971, pad = v_111_pad_0, pad_type = v_111_pad_type_0, strides = var_5695, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_111_cast_fp16")]; + tensor var_5701 = const()[name = tensor("op_5701"), val = tensor([1, 20, 64, -1])]; + tensor var_5702_cast_fp16 = reshape(shape = var_5701, x = q_111_cast_fp16)[name = tensor("op_5702_cast_fp16")]; + tensor var_5703 = const()[name = tensor("op_5703"), val = tensor([1, 20, 64, -1])]; + tensor var_5704_cast_fp16 = reshape(shape = var_5703, x = k_111_cast_fp16)[name = tensor("op_5704_cast_fp16")]; + tensor var_5705 = const()[name = tensor("op_5705"), val = tensor([1, 20, 64, -1])]; + tensor var_5706_cast_fp16 = reshape(shape = var_5705, x = v_111_cast_fp16)[name = tensor("op_5706_cast_fp16")]; + tensor attn_weights_221_transpose_x_0 = const()[name = tensor("attn_weights_221_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_221_transpose_y_0 = const()[name = tensor("attn_weights_221_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_221_cast_fp16 = matmul(transpose_x = attn_weights_221_transpose_x_0, transpose_y = attn_weights_221_transpose_y_0, x = var_5702_cast_fp16, y = var_5704_cast_fp16)[name = tensor("attn_weights_221_cast_fp16")]; + tensor attn_weights_223_cast_fp16 = mul(x = attn_weights_221_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_223_cast_fp16")]; + tensor var_5710_cast_fp16 = softmax(axis = var_4955, x = attn_weights_223_cast_fp16)[name = tensor("op_5710_cast_fp16")]; + tensor attn_111_transpose_x_0 = const()[name = tensor("attn_111_transpose_x_0"), val = tensor(false)]; + tensor attn_111_transpose_y_0 = const()[name = tensor("attn_111_transpose_y_0"), val = tensor(true)]; + tensor attn_111_cast_fp16 = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_5706_cast_fp16, y = var_5710_cast_fp16)[name = tensor("attn_111_cast_fp16")]; + tensor var_5714 = const()[name = tensor("op_5714"), val = tensor([1, 1280, 1, -1])]; + tensor input_355_cast_fp16 = reshape(shape = var_5714, x = attn_111_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor var_5719 = const()[name = tensor("op_5719"), val = tensor([1, 1])]; + tensor var_5721 = const()[name = tensor("op_5721"), val = tensor([1, 1])]; + tensor var_5723_pad_type_0 = const()[name = tensor("op_5723_pad_type_0"), val = tensor("custom")]; + tensor var_5723_pad_0 = const()[name = tensor("op_5723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736123520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737352384))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737352576)))]; + tensor var_5723_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_5721, groups = var_4971, pad = var_5723_pad_0, pad_type = var_5723_pad_type_0, strides = var_5719, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = tensor("op_5723_cast_fp16")]; + tensor inputs_167_cast_fp16 = add(x = var_5723_cast_fp16, y = inputs_165_cast_fp16)[name = tensor("inputs_167_cast_fp16")]; + tensor input_357_axes_0 = const()[name = tensor("input_357_axes_0"), val = tensor([1])]; + tensor input_357_gamma_0_to_fp16 = const()[name = tensor("input_357_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737355200)))]; + tensor input_357_beta_0_to_fp16 = const()[name = tensor("input_357_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737357824)))]; + tensor var_5733_to_fp16 = const()[name = tensor("op_5733_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_357_cast_fp16 = layer_norm(axes = input_357_axes_0, beta = input_357_beta_0_to_fp16, epsilon = var_5733_to_fp16, gamma = input_357_gamma_0_to_fp16, x = inputs_167_cast_fp16)[name = tensor("input_357_cast_fp16")]; + tensor var_5749 = const()[name = tensor("op_5749"), val = tensor([1, 1])]; + tensor var_5751 = const()[name = tensor("op_5751"), val = tensor([1, 1])]; + tensor var_5753_pad_type_0 = const()[name = tensor("op_5753_pad_type_0"), val = tensor("custom")]; + tensor var_5753_pad_0 = const()[name = tensor("op_5753_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737360448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747190912))), name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747191104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747198848))), name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5753_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5751, groups = var_4971, pad = var_5753_pad_0, pad_type = var_5753_pad_type_0, strides = var_5749, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_357_cast_fp16)[name = tensor("op_5753_cast_fp16")]; + tensor var_5754_split_sizes_0 = const()[name = tensor("op_5754_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5754_axis_0 = const()[name = tensor("op_5754_axis_0"), val = tensor(1)]; + tensor var_5754_cast_fp16_0, tensor var_5754_cast_fp16_1 = split(axis = var_5754_axis_0, split_sizes = var_5754_split_sizes_0, x = var_5753_cast_fp16)[name = tensor("op_5754_cast_fp16")]; + tensor var_5756_mode_0 = const()[name = tensor("op_5756_mode_0"), val = tensor("EXACT")]; + tensor var_5756_cast_fp16 = gelu(mode = var_5756_mode_0, x = var_5754_cast_fp16_1)[name = tensor("op_5756_cast_fp16")]; + tensor input_359_cast_fp16 = mul(x = var_5754_cast_fp16_0, y = var_5756_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor var_5760 = const()[name = tensor("op_5760"), val = tensor([1, 1])]; + tensor var_5762 = const()[name = tensor("op_5762"), val = tensor([1, 1])]; + tensor var_5764_pad_type_0 = const()[name = tensor("op_5764_pad_type_0"), val = tensor("custom")]; + tensor var_5764_pad_0 = const()[name = tensor("op_5764_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747199040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752114304))), name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752114496)))]; + tensor var_5764_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_5762, groups = var_4971, pad = var_5764_pad_0, pad_type = var_5764_pad_type_0, strides = var_5760, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_359_cast_fp16)[name = tensor("op_5764_cast_fp16")]; + tensor inputs_169_cast_fp16 = add(x = var_5764_cast_fp16, y = inputs_167_cast_fp16)[name = tensor("inputs_169_cast_fp16")]; + tensor hidden_states_233_axes_0 = const()[name = tensor("hidden_states_233_axes_0"), val = tensor([1])]; + tensor hidden_states_233_gamma_0_to_fp16 = const()[name = tensor("hidden_states_233_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752117120)))]; + tensor hidden_states_233_beta_0_to_fp16 = const()[name = tensor("hidden_states_233_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752119744)))]; + tensor var_5780_to_fp16 = const()[name = tensor("op_5780_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_233_cast_fp16 = layer_norm(axes = hidden_states_233_axes_0, beta = hidden_states_233_beta_0_to_fp16, epsilon = var_5780_to_fp16, gamma = hidden_states_233_gamma_0_to_fp16, x = inputs_169_cast_fp16)[name = tensor("hidden_states_233_cast_fp16")]; + tensor var_5795 = const()[name = tensor("op_5795"), val = tensor([1, 1])]; + tensor var_5797 = const()[name = tensor("op_5797"), val = tensor([1, 1])]; + tensor q_113_pad_type_0 = const()[name = tensor("q_113_pad_type_0"), val = tensor("custom")]; + tensor q_113_pad_0 = const()[name = tensor("q_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752122368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(753351232))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_113_cast_fp16 = conv(dilations = var_5797, groups = var_4971, pad = q_113_pad_0, pad_type = q_113_pad_type_0, strides = var_5795, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_233_cast_fp16)[name = tensor("q_113_cast_fp16")]; + tensor var_5801 = const()[name = tensor("op_5801"), val = tensor([1, 1])]; + tensor var_5803 = const()[name = tensor("op_5803"), 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 mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(753351424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754580288))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_113_cast_fp16 = conv(dilations = var_5803, groups = var_4971, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_5801, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_233_cast_fp16)[name = tensor("k_113_cast_fp16")]; + tensor var_5807 = const()[name = tensor("op_5807"), val = tensor([1, 1])]; + tensor var_5809 = const()[name = tensor("op_5809"), val = tensor([1, 1])]; + tensor v_113_pad_type_0 = const()[name = tensor("v_113_pad_type_0"), val = tensor("custom")]; + tensor v_113_pad_0 = const()[name = tensor("v_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754580480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755809344))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_113_cast_fp16 = conv(dilations = var_5809, groups = var_4971, pad = v_113_pad_0, pad_type = v_113_pad_type_0, strides = var_5807, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_233_cast_fp16)[name = tensor("v_113_cast_fp16")]; + tensor var_5813 = const()[name = tensor("op_5813"), val = tensor([1, 20, 64, -1])]; + tensor var_5814_cast_fp16 = reshape(shape = var_5813, x = q_113_cast_fp16)[name = tensor("op_5814_cast_fp16")]; + tensor var_5815 = const()[name = tensor("op_5815"), val = tensor([1, 20, 64, -1])]; + tensor var_5816_cast_fp16 = reshape(shape = var_5815, x = k_113_cast_fp16)[name = tensor("op_5816_cast_fp16")]; + tensor var_5817 = const()[name = tensor("op_5817"), val = tensor([1, 20, 64, -1])]; + tensor var_5818_cast_fp16 = reshape(shape = var_5817, x = v_113_cast_fp16)[name = tensor("op_5818_cast_fp16")]; + tensor attn_weights_225_transpose_x_0 = const()[name = tensor("attn_weights_225_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_225_transpose_y_0 = const()[name = tensor("attn_weights_225_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5814_cast_fp16, y = var_5816_cast_fp16)[name = tensor("attn_weights_225_cast_fp16")]; + tensor attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_227_cast_fp16")]; + tensor var_5822_cast_fp16 = softmax(axis = var_4955, x = attn_weights_227_cast_fp16)[name = tensor("op_5822_cast_fp16")]; + tensor attn_113_transpose_x_0 = const()[name = tensor("attn_113_transpose_x_0"), val = tensor(false)]; + tensor attn_113_transpose_y_0 = const()[name = tensor("attn_113_transpose_y_0"), val = tensor(true)]; + tensor attn_113_cast_fp16 = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_5818_cast_fp16, y = var_5822_cast_fp16)[name = tensor("attn_113_cast_fp16")]; + tensor var_5826 = const()[name = tensor("op_5826"), val = tensor([1, 1280, 1, -1])]; + tensor input_361_cast_fp16 = reshape(shape = var_5826, x = attn_113_cast_fp16)[name = tensor("input_361_cast_fp16")]; + tensor var_5831 = const()[name = tensor("op_5831"), val = tensor([1, 1])]; + tensor var_5833 = const()[name = tensor("op_5833"), val = tensor([1, 1])]; + tensor var_5835_pad_type_0 = const()[name = tensor("op_5835_pad_type_0"), val = tensor("custom")]; + tensor var_5835_pad_0 = const()[name = tensor("op_5835_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755809536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757038400))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757038592)))]; + tensor var_5835_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_5833, groups = var_4971, pad = var_5835_pad_0, pad_type = var_5835_pad_type_0, strides = var_5831, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = tensor("op_5835_cast_fp16")]; + tensor inputs_171_cast_fp16 = add(x = var_5835_cast_fp16, y = inputs_169_cast_fp16)[name = tensor("inputs_171_cast_fp16")]; + tensor hidden_states_235_axes_0 = const()[name = tensor("hidden_states_235_axes_0"), val = tensor([1])]; + tensor hidden_states_235_gamma_0_to_fp16 = const()[name = tensor("hidden_states_235_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757041216)))]; + tensor hidden_states_235_beta_0_to_fp16 = const()[name = tensor("hidden_states_235_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757043840)))]; + tensor var_5845_to_fp16 = const()[name = tensor("op_5845_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_235_cast_fp16 = layer_norm(axes = hidden_states_235_axes_0, beta = hidden_states_235_beta_0_to_fp16, epsilon = var_5845_to_fp16, gamma = hidden_states_235_gamma_0_to_fp16, x = inputs_171_cast_fp16)[name = tensor("hidden_states_235_cast_fp16")]; + tensor var_5860 = const()[name = tensor("op_5860"), val = tensor([1, 1])]; + tensor var_5862 = const()[name = tensor("op_5862"), val = tensor([1, 1])]; + tensor q_115_pad_type_0 = const()[name = tensor("q_115_pad_type_0"), val = tensor("custom")]; + tensor q_115_pad_0 = const()[name = tensor("q_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757046464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(758275328))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_115_cast_fp16 = conv(dilations = var_5862, groups = var_4971, pad = q_115_pad_0, pad_type = q_115_pad_type_0, strides = var_5860, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_235_cast_fp16)[name = tensor("q_115_cast_fp16")]; + tensor var_5866 = const()[name = tensor("op_5866"), val = tensor([1, 1])]; + tensor var_5868 = const()[name = tensor("op_5868"), val = tensor([1, 1])]; + tensor k_115_pad_type_0 = const()[name = tensor("k_115_pad_type_0"), val = tensor("custom")]; + tensor k_115_pad_0 = const()[name = tensor("k_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(758275520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760241664))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_115_cast_fp16 = conv(dilations = var_5868, groups = var_4971, pad = k_115_pad_0, pad_type = k_115_pad_type_0, strides = var_5866, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_115_cast_fp16")]; + tensor var_5872 = const()[name = tensor("op_5872"), val = tensor([1, 1])]; + tensor var_5874 = const()[name = tensor("op_5874"), val = tensor([1, 1])]; + tensor v_115_pad_type_0 = const()[name = tensor("v_115_pad_type_0"), val = tensor("custom")]; + tensor v_115_pad_0 = const()[name = tensor("v_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760241856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762208000))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_115_cast_fp16 = conv(dilations = var_5874, groups = var_4971, pad = v_115_pad_0, pad_type = v_115_pad_type_0, strides = var_5872, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_115_cast_fp16")]; + tensor var_5878 = const()[name = tensor("op_5878"), val = tensor([1, 20, 64, -1])]; + tensor var_5879_cast_fp16 = reshape(shape = var_5878, x = q_115_cast_fp16)[name = tensor("op_5879_cast_fp16")]; + tensor var_5880 = const()[name = tensor("op_5880"), val = tensor([1, 20, 64, -1])]; + tensor var_5881_cast_fp16 = reshape(shape = var_5880, x = k_115_cast_fp16)[name = tensor("op_5881_cast_fp16")]; + tensor var_5882 = const()[name = tensor("op_5882"), val = tensor([1, 20, 64, -1])]; + tensor var_5883_cast_fp16 = reshape(shape = var_5882, x = v_115_cast_fp16)[name = tensor("op_5883_cast_fp16")]; + tensor attn_weights_229_transpose_x_0 = const()[name = tensor("attn_weights_229_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_229_transpose_y_0 = const()[name = tensor("attn_weights_229_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_229_cast_fp16 = matmul(transpose_x = attn_weights_229_transpose_x_0, transpose_y = attn_weights_229_transpose_y_0, x = var_5879_cast_fp16, y = var_5881_cast_fp16)[name = tensor("attn_weights_229_cast_fp16")]; + tensor attn_weights_231_cast_fp16 = mul(x = attn_weights_229_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_231_cast_fp16")]; + tensor var_5887_cast_fp16 = softmax(axis = var_4955, x = attn_weights_231_cast_fp16)[name = tensor("op_5887_cast_fp16")]; + tensor attn_115_transpose_x_0 = const()[name = tensor("attn_115_transpose_x_0"), val = tensor(false)]; + tensor attn_115_transpose_y_0 = const()[name = tensor("attn_115_transpose_y_0"), val = tensor(true)]; + tensor attn_115_cast_fp16 = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_5883_cast_fp16, y = var_5887_cast_fp16)[name = tensor("attn_115_cast_fp16")]; + tensor var_5891 = const()[name = tensor("op_5891"), val = tensor([1, 1280, 1, -1])]; + tensor input_363_cast_fp16 = reshape(shape = var_5891, x = attn_115_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor var_5896 = const()[name = tensor("op_5896"), val = tensor([1, 1])]; + tensor var_5898 = const()[name = tensor("op_5898"), val = tensor([1, 1])]; + tensor var_5900_pad_type_0 = const()[name = tensor("op_5900_pad_type_0"), val = tensor("custom")]; + tensor var_5900_pad_0 = const()[name = tensor("op_5900_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762208192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763437056))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763437248)))]; + tensor var_5900_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_5898, groups = var_4971, pad = var_5900_pad_0, pad_type = var_5900_pad_type_0, strides = var_5896, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_363_cast_fp16)[name = tensor("op_5900_cast_fp16")]; + tensor inputs_173_cast_fp16 = add(x = var_5900_cast_fp16, y = inputs_171_cast_fp16)[name = tensor("inputs_173_cast_fp16")]; + tensor input_365_axes_0 = const()[name = tensor("input_365_axes_0"), val = tensor([1])]; + tensor input_365_gamma_0_to_fp16 = const()[name = tensor("input_365_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763439872)))]; + tensor input_365_beta_0_to_fp16 = const()[name = tensor("input_365_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763442496)))]; + tensor var_5910_to_fp16 = const()[name = tensor("op_5910_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_365_cast_fp16 = layer_norm(axes = input_365_axes_0, beta = input_365_beta_0_to_fp16, epsilon = var_5910_to_fp16, gamma = input_365_gamma_0_to_fp16, x = inputs_173_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor var_5926 = const()[name = tensor("op_5926"), val = tensor([1, 1])]; + tensor var_5928 = const()[name = tensor("op_5928"), val = tensor([1, 1])]; + tensor var_5930_pad_type_0 = const()[name = tensor("op_5930_pad_type_0"), val = tensor("custom")]; + tensor var_5930_pad_0 = const()[name = tensor("op_5930_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763445120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773275584))), name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773275776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773283520))), name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5930_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5928, groups = var_4971, pad = var_5930_pad_0, pad_type = var_5930_pad_type_0, strides = var_5926, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_365_cast_fp16)[name = tensor("op_5930_cast_fp16")]; + tensor var_5931_split_sizes_0 = const()[name = tensor("op_5931_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5931_axis_0 = const()[name = tensor("op_5931_axis_0"), val = tensor(1)]; + tensor var_5931_cast_fp16_0, tensor var_5931_cast_fp16_1 = split(axis = var_5931_axis_0, split_sizes = var_5931_split_sizes_0, x = var_5930_cast_fp16)[name = tensor("op_5931_cast_fp16")]; + tensor var_5933_mode_0 = const()[name = tensor("op_5933_mode_0"), val = tensor("EXACT")]; + tensor var_5933_cast_fp16 = gelu(mode = var_5933_mode_0, x = var_5931_cast_fp16_1)[name = tensor("op_5933_cast_fp16")]; + tensor input_367_cast_fp16 = mul(x = var_5931_cast_fp16_0, y = var_5933_cast_fp16)[name = tensor("input_367_cast_fp16")]; + tensor var_5937 = const()[name = tensor("op_5937"), val = tensor([1, 1])]; + tensor var_5939 = const()[name = tensor("op_5939"), val = tensor([1, 1])]; + tensor var_5941_pad_type_0 = const()[name = tensor("op_5941_pad_type_0"), val = tensor("custom")]; + tensor var_5941_pad_0 = const()[name = tensor("op_5941_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773283712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778198976))), name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778199168)))]; + tensor var_5941_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_5939, groups = var_4971, pad = var_5941_pad_0, pad_type = var_5941_pad_type_0, strides = var_5937, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_367_cast_fp16)[name = tensor("op_5941_cast_fp16")]; + tensor inputs_175_cast_fp16 = add(x = var_5941_cast_fp16, y = inputs_173_cast_fp16)[name = tensor("inputs_175_cast_fp16")]; + tensor hidden_states_239_axes_0 = const()[name = tensor("hidden_states_239_axes_0"), val = tensor([1])]; + tensor hidden_states_239_gamma_0_to_fp16 = const()[name = tensor("hidden_states_239_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778201792)))]; + tensor hidden_states_239_beta_0_to_fp16 = const()[name = tensor("hidden_states_239_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778204416)))]; + tensor var_5957_to_fp16 = const()[name = tensor("op_5957_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_239_cast_fp16 = layer_norm(axes = hidden_states_239_axes_0, beta = hidden_states_239_beta_0_to_fp16, epsilon = var_5957_to_fp16, gamma = hidden_states_239_gamma_0_to_fp16, x = inputs_175_cast_fp16)[name = tensor("hidden_states_239_cast_fp16")]; + tensor var_5972 = const()[name = tensor("op_5972"), val = tensor([1, 1])]; + tensor var_5974 = const()[name = tensor("op_5974"), val = tensor([1, 1])]; + tensor q_117_pad_type_0 = const()[name = tensor("q_117_pad_type_0"), val = tensor("custom")]; + tensor q_117_pad_0 = const()[name = tensor("q_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778207040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(779435904))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_117_cast_fp16 = conv(dilations = var_5974, groups = var_4971, pad = q_117_pad_0, pad_type = q_117_pad_type_0, strides = var_5972, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_239_cast_fp16)[name = tensor("q_117_cast_fp16")]; + tensor var_5978 = const()[name = tensor("op_5978"), val = tensor([1, 1])]; + tensor var_5980 = const()[name = tensor("op_5980"), 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 mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(779436096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(780664960))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_117_cast_fp16 = conv(dilations = var_5980, groups = var_4971, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_5978, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_239_cast_fp16)[name = tensor("k_117_cast_fp16")]; + tensor var_5984 = const()[name = tensor("op_5984"), val = tensor([1, 1])]; + tensor var_5986 = const()[name = tensor("op_5986"), val = tensor([1, 1])]; + tensor v_117_pad_type_0 = const()[name = tensor("v_117_pad_type_0"), val = tensor("custom")]; + tensor v_117_pad_0 = const()[name = tensor("v_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(780665152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781894016))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_117_cast_fp16 = conv(dilations = var_5986, groups = var_4971, pad = v_117_pad_0, pad_type = v_117_pad_type_0, strides = var_5984, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_239_cast_fp16)[name = tensor("v_117_cast_fp16")]; + tensor var_5990 = const()[name = tensor("op_5990"), val = tensor([1, 20, 64, -1])]; + tensor var_5991_cast_fp16 = reshape(shape = var_5990, x = q_117_cast_fp16)[name = tensor("op_5991_cast_fp16")]; + tensor var_5992 = const()[name = tensor("op_5992"), val = tensor([1, 20, 64, -1])]; + tensor var_5993_cast_fp16 = reshape(shape = var_5992, x = k_117_cast_fp16)[name = tensor("op_5993_cast_fp16")]; + tensor var_5994 = const()[name = tensor("op_5994"), val = tensor([1, 20, 64, -1])]; + tensor var_5995_cast_fp16 = reshape(shape = var_5994, x = v_117_cast_fp16)[name = tensor("op_5995_cast_fp16")]; + tensor attn_weights_233_transpose_x_0 = const()[name = tensor("attn_weights_233_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_233_transpose_y_0 = const()[name = tensor("attn_weights_233_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5991_cast_fp16, y = var_5993_cast_fp16)[name = tensor("attn_weights_233_cast_fp16")]; + tensor attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_235_cast_fp16")]; + tensor var_5999_cast_fp16 = softmax(axis = var_4955, x = attn_weights_235_cast_fp16)[name = tensor("op_5999_cast_fp16")]; + tensor attn_117_transpose_x_0 = const()[name = tensor("attn_117_transpose_x_0"), val = tensor(false)]; + tensor attn_117_transpose_y_0 = const()[name = tensor("attn_117_transpose_y_0"), val = tensor(true)]; + tensor attn_117_cast_fp16 = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_5995_cast_fp16, y = var_5999_cast_fp16)[name = tensor("attn_117_cast_fp16")]; + tensor var_6003 = const()[name = tensor("op_6003"), val = tensor([1, 1280, 1, -1])]; + tensor input_369_cast_fp16 = reshape(shape = var_6003, x = attn_117_cast_fp16)[name = tensor("input_369_cast_fp16")]; + 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 var_6012_pad_type_0 = const()[name = tensor("op_6012_pad_type_0"), val = tensor("custom")]; + tensor var_6012_pad_0 = const()[name = tensor("op_6012_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781894208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783123072))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783123264)))]; + tensor var_6012_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_6010, groups = var_4971, pad = var_6012_pad_0, pad_type = var_6012_pad_type_0, strides = var_6008, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = tensor("op_6012_cast_fp16")]; + tensor inputs_177_cast_fp16 = add(x = var_6012_cast_fp16, y = inputs_175_cast_fp16)[name = tensor("inputs_177_cast_fp16")]; + tensor hidden_states_241_axes_0 = const()[name = tensor("hidden_states_241_axes_0"), val = tensor([1])]; + tensor hidden_states_241_gamma_0_to_fp16 = const()[name = tensor("hidden_states_241_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783125888)))]; + tensor hidden_states_241_beta_0_to_fp16 = const()[name = tensor("hidden_states_241_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783128512)))]; + tensor var_6022_to_fp16 = const()[name = tensor("op_6022_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_241_cast_fp16 = layer_norm(axes = hidden_states_241_axes_0, beta = hidden_states_241_beta_0_to_fp16, epsilon = var_6022_to_fp16, gamma = hidden_states_241_gamma_0_to_fp16, x = inputs_177_cast_fp16)[name = tensor("hidden_states_241_cast_fp16")]; + tensor var_6037 = const()[name = tensor("op_6037"), val = tensor([1, 1])]; + tensor var_6039 = const()[name = tensor("op_6039"), val = tensor([1, 1])]; + tensor q_119_pad_type_0 = const()[name = tensor("q_119_pad_type_0"), val = tensor("custom")]; + tensor q_119_pad_0 = const()[name = tensor("q_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783131136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784360000))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_119_cast_fp16 = conv(dilations = var_6039, groups = var_4971, pad = q_119_pad_0, pad_type = q_119_pad_type_0, strides = var_6037, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_241_cast_fp16)[name = tensor("q_119_cast_fp16")]; + tensor var_6043 = const()[name = tensor("op_6043"), val = tensor([1, 1])]; + tensor var_6045 = const()[name = tensor("op_6045"), val = tensor([1, 1])]; + tensor k_119_pad_type_0 = const()[name = tensor("k_119_pad_type_0"), val = tensor("custom")]; + tensor k_119_pad_0 = const()[name = tensor("k_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784360192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(786326336))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_119_cast_fp16 = conv(dilations = var_6045, groups = var_4971, pad = k_119_pad_0, pad_type = k_119_pad_type_0, strides = var_6043, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_119_cast_fp16")]; + tensor var_6049 = const()[name = tensor("op_6049"), val = tensor([1, 1])]; + tensor var_6051 = const()[name = tensor("op_6051"), val = tensor([1, 1])]; + tensor v_119_pad_type_0 = const()[name = tensor("v_119_pad_type_0"), val = tensor("custom")]; + tensor v_119_pad_0 = const()[name = tensor("v_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(786326528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788292672))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_119_cast_fp16 = conv(dilations = var_6051, groups = var_4971, pad = v_119_pad_0, pad_type = v_119_pad_type_0, strides = var_6049, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_119_cast_fp16")]; + tensor var_6055 = const()[name = tensor("op_6055"), val = tensor([1, 20, 64, -1])]; + tensor var_6056_cast_fp16 = reshape(shape = var_6055, x = q_119_cast_fp16)[name = tensor("op_6056_cast_fp16")]; + tensor var_6057 = const()[name = tensor("op_6057"), val = tensor([1, 20, 64, -1])]; + tensor var_6058_cast_fp16 = reshape(shape = var_6057, x = k_119_cast_fp16)[name = tensor("op_6058_cast_fp16")]; + tensor var_6059 = const()[name = tensor("op_6059"), val = tensor([1, 20, 64, -1])]; + tensor var_6060_cast_fp16 = reshape(shape = var_6059, x = v_119_cast_fp16)[name = tensor("op_6060_cast_fp16")]; + tensor attn_weights_237_transpose_x_0 = const()[name = tensor("attn_weights_237_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_237_transpose_y_0 = const()[name = tensor("attn_weights_237_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_237_cast_fp16 = matmul(transpose_x = attn_weights_237_transpose_x_0, transpose_y = attn_weights_237_transpose_y_0, x = var_6056_cast_fp16, y = var_6058_cast_fp16)[name = tensor("attn_weights_237_cast_fp16")]; + tensor attn_weights_239_cast_fp16 = mul(x = attn_weights_237_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_239_cast_fp16")]; + tensor var_6064_cast_fp16 = softmax(axis = var_4955, x = attn_weights_239_cast_fp16)[name = tensor("op_6064_cast_fp16")]; + tensor attn_119_transpose_x_0 = const()[name = tensor("attn_119_transpose_x_0"), val = tensor(false)]; + tensor attn_119_transpose_y_0 = const()[name = tensor("attn_119_transpose_y_0"), val = tensor(true)]; + tensor attn_119_cast_fp16 = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_6060_cast_fp16, y = var_6064_cast_fp16)[name = tensor("attn_119_cast_fp16")]; + tensor var_6068 = const()[name = tensor("op_6068"), val = tensor([1, 1280, 1, -1])]; + tensor input_371_cast_fp16 = reshape(shape = var_6068, x = attn_119_cast_fp16)[name = tensor("input_371_cast_fp16")]; + tensor var_6073 = const()[name = tensor("op_6073"), val = tensor([1, 1])]; + tensor var_6075 = const()[name = tensor("op_6075"), val = tensor([1, 1])]; + tensor var_6077_pad_type_0 = const()[name = tensor("op_6077_pad_type_0"), val = tensor("custom")]; + tensor var_6077_pad_0 = const()[name = tensor("op_6077_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788292864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789521728))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789521920)))]; + tensor var_6077_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_6075, groups = var_4971, pad = var_6077_pad_0, pad_type = var_6077_pad_type_0, strides = var_6073, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_371_cast_fp16)[name = tensor("op_6077_cast_fp16")]; + tensor inputs_179_cast_fp16 = add(x = var_6077_cast_fp16, y = inputs_177_cast_fp16)[name = tensor("inputs_179_cast_fp16")]; + tensor input_373_axes_0 = const()[name = tensor("input_373_axes_0"), val = tensor([1])]; + tensor input_373_gamma_0_to_fp16 = const()[name = tensor("input_373_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789524544)))]; + tensor input_373_beta_0_to_fp16 = const()[name = tensor("input_373_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789527168)))]; + tensor var_6087_to_fp16 = const()[name = tensor("op_6087_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_373_cast_fp16 = layer_norm(axes = input_373_axes_0, beta = input_373_beta_0_to_fp16, epsilon = var_6087_to_fp16, gamma = input_373_gamma_0_to_fp16, x = inputs_179_cast_fp16)[name = tensor("input_373_cast_fp16")]; + tensor var_6103 = const()[name = tensor("op_6103"), val = tensor([1, 1])]; + tensor var_6105 = const()[name = tensor("op_6105"), val = tensor([1, 1])]; + tensor var_6107_pad_type_0 = const()[name = tensor("op_6107_pad_type_0"), val = tensor("custom")]; + tensor var_6107_pad_0 = const()[name = tensor("op_6107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789529792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799360256))), name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799360448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799368192))), name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6107_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6105, groups = var_4971, pad = var_6107_pad_0, pad_type = var_6107_pad_type_0, strides = var_6103, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = tensor("op_6107_cast_fp16")]; + tensor var_6108_split_sizes_0 = const()[name = tensor("op_6108_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6108_axis_0 = const()[name = tensor("op_6108_axis_0"), val = tensor(1)]; + tensor var_6108_cast_fp16_0, tensor var_6108_cast_fp16_1 = split(axis = var_6108_axis_0, split_sizes = var_6108_split_sizes_0, x = var_6107_cast_fp16)[name = tensor("op_6108_cast_fp16")]; + tensor var_6110_mode_0 = const()[name = tensor("op_6110_mode_0"), val = tensor("EXACT")]; + tensor var_6110_cast_fp16 = gelu(mode = var_6110_mode_0, x = var_6108_cast_fp16_1)[name = tensor("op_6110_cast_fp16")]; + tensor input_375_cast_fp16 = mul(x = var_6108_cast_fp16_0, y = var_6110_cast_fp16)[name = tensor("input_375_cast_fp16")]; + tensor var_6114 = const()[name = tensor("op_6114"), val = tensor([1, 1])]; + tensor var_6116 = const()[name = tensor("op_6116"), val = tensor([1, 1])]; + tensor var_6118_pad_type_0 = const()[name = tensor("op_6118_pad_type_0"), val = tensor("custom")]; + tensor var_6118_pad_0 = const()[name = tensor("op_6118_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799368384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804283648))), name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804283840)))]; + tensor var_6118_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_6116, groups = var_4971, pad = var_6118_pad_0, pad_type = var_6118_pad_type_0, strides = var_6114, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_375_cast_fp16)[name = tensor("op_6118_cast_fp16")]; + tensor inputs_181_cast_fp16 = add(x = var_6118_cast_fp16, y = inputs_179_cast_fp16)[name = tensor("inputs_181_cast_fp16")]; + tensor hidden_states_245_axes_0 = const()[name = tensor("hidden_states_245_axes_0"), val = tensor([1])]; + tensor hidden_states_245_gamma_0_to_fp16 = const()[name = tensor("hidden_states_245_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804286464)))]; + tensor hidden_states_245_beta_0_to_fp16 = const()[name = tensor("hidden_states_245_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804289088)))]; + tensor var_6134_to_fp16 = const()[name = tensor("op_6134_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_245_cast_fp16 = layer_norm(axes = hidden_states_245_axes_0, beta = hidden_states_245_beta_0_to_fp16, epsilon = var_6134_to_fp16, gamma = hidden_states_245_gamma_0_to_fp16, x = inputs_181_cast_fp16)[name = tensor("hidden_states_245_cast_fp16")]; + tensor var_6149 = const()[name = tensor("op_6149"), val = tensor([1, 1])]; + tensor var_6151 = const()[name = tensor("op_6151"), val = tensor([1, 1])]; + tensor q_121_pad_type_0 = const()[name = tensor("q_121_pad_type_0"), val = tensor("custom")]; + tensor q_121_pad_0 = const()[name = tensor("q_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804291712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805520576))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_121_cast_fp16 = conv(dilations = var_6151, groups = var_4971, pad = q_121_pad_0, pad_type = q_121_pad_type_0, strides = var_6149, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_245_cast_fp16)[name = tensor("q_121_cast_fp16")]; + tensor var_6155 = const()[name = tensor("op_6155"), val = tensor([1, 1])]; + tensor var_6157 = const()[name = tensor("op_6157"), 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 mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805520768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806749632))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_121_cast_fp16 = conv(dilations = var_6157, groups = var_4971, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_6155, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_245_cast_fp16)[name = tensor("k_121_cast_fp16")]; + tensor var_6161 = const()[name = tensor("op_6161"), val = tensor([1, 1])]; + tensor var_6163 = const()[name = tensor("op_6163"), val = tensor([1, 1])]; + tensor v_121_pad_type_0 = const()[name = tensor("v_121_pad_type_0"), val = tensor("custom")]; + tensor v_121_pad_0 = const()[name = tensor("v_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806749824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(807978688))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_121_cast_fp16 = conv(dilations = var_6163, groups = var_4971, pad = v_121_pad_0, pad_type = v_121_pad_type_0, strides = var_6161, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_245_cast_fp16)[name = tensor("v_121_cast_fp16")]; + tensor var_6167 = const()[name = tensor("op_6167"), val = tensor([1, 20, 64, -1])]; + tensor var_6168_cast_fp16 = reshape(shape = var_6167, x = q_121_cast_fp16)[name = tensor("op_6168_cast_fp16")]; + tensor var_6169 = const()[name = tensor("op_6169"), val = tensor([1, 20, 64, -1])]; + tensor var_6170_cast_fp16 = reshape(shape = var_6169, x = k_121_cast_fp16)[name = tensor("op_6170_cast_fp16")]; + tensor var_6171 = const()[name = tensor("op_6171"), val = tensor([1, 20, 64, -1])]; + tensor var_6172_cast_fp16 = reshape(shape = var_6171, x = v_121_cast_fp16)[name = tensor("op_6172_cast_fp16")]; + tensor attn_weights_241_transpose_x_0 = const()[name = tensor("attn_weights_241_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_241_transpose_y_0 = const()[name = tensor("attn_weights_241_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_6168_cast_fp16, y = var_6170_cast_fp16)[name = tensor("attn_weights_241_cast_fp16")]; + tensor attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_243_cast_fp16")]; + tensor var_6176_cast_fp16 = softmax(axis = var_4955, x = attn_weights_243_cast_fp16)[name = tensor("op_6176_cast_fp16")]; + tensor attn_121_transpose_x_0 = const()[name = tensor("attn_121_transpose_x_0"), val = tensor(false)]; + tensor attn_121_transpose_y_0 = const()[name = tensor("attn_121_transpose_y_0"), val = tensor(true)]; + tensor attn_121_cast_fp16 = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6172_cast_fp16, y = var_6176_cast_fp16)[name = tensor("attn_121_cast_fp16")]; + tensor var_6180 = const()[name = tensor("op_6180"), val = tensor([1, 1280, 1, -1])]; + tensor input_377_cast_fp16 = reshape(shape = var_6180, x = attn_121_cast_fp16)[name = tensor("input_377_cast_fp16")]; + tensor var_6185 = const()[name = tensor("op_6185"), val = tensor([1, 1])]; + tensor var_6187 = const()[name = tensor("op_6187"), val = tensor([1, 1])]; + tensor var_6189_pad_type_0 = const()[name = tensor("op_6189_pad_type_0"), val = tensor("custom")]; + tensor var_6189_pad_0 = const()[name = tensor("op_6189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(807978880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809207744))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809207936)))]; + tensor var_6189_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_6187, groups = var_4971, pad = var_6189_pad_0, pad_type = var_6189_pad_type_0, strides = var_6185, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = tensor("op_6189_cast_fp16")]; + tensor inputs_183_cast_fp16 = add(x = var_6189_cast_fp16, y = inputs_181_cast_fp16)[name = tensor("inputs_183_cast_fp16")]; + tensor hidden_states_247_axes_0 = const()[name = tensor("hidden_states_247_axes_0"), val = tensor([1])]; + tensor hidden_states_247_gamma_0_to_fp16 = const()[name = tensor("hidden_states_247_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809210560)))]; + tensor hidden_states_247_beta_0_to_fp16 = const()[name = tensor("hidden_states_247_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809213184)))]; + tensor var_6199_to_fp16 = const()[name = tensor("op_6199_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_247_cast_fp16 = layer_norm(axes = hidden_states_247_axes_0, beta = hidden_states_247_beta_0_to_fp16, epsilon = var_6199_to_fp16, gamma = hidden_states_247_gamma_0_to_fp16, x = inputs_183_cast_fp16)[name = tensor("hidden_states_247_cast_fp16")]; + tensor var_6214 = const()[name = tensor("op_6214"), val = tensor([1, 1])]; + tensor var_6216 = const()[name = tensor("op_6216"), val = tensor([1, 1])]; + tensor q_123_pad_type_0 = const()[name = tensor("q_123_pad_type_0"), val = tensor("custom")]; + tensor q_123_pad_0 = const()[name = tensor("q_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809215808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810444672))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_123_cast_fp16 = conv(dilations = var_6216, groups = var_4971, pad = q_123_pad_0, pad_type = q_123_pad_type_0, strides = var_6214, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_247_cast_fp16)[name = tensor("q_123_cast_fp16")]; + tensor var_6220 = const()[name = tensor("op_6220"), val = tensor([1, 1])]; + tensor var_6222 = const()[name = tensor("op_6222"), val = tensor([1, 1])]; + tensor k_123_pad_type_0 = const()[name = tensor("k_123_pad_type_0"), val = tensor("custom")]; + tensor k_123_pad_0 = const()[name = tensor("k_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810444864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(812411008))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_123_cast_fp16 = conv(dilations = var_6222, groups = var_4971, pad = k_123_pad_0, pad_type = k_123_pad_type_0, strides = var_6220, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_123_cast_fp16")]; + tensor var_6226 = const()[name = tensor("op_6226"), val = tensor([1, 1])]; + tensor var_6228 = const()[name = tensor("op_6228"), val = tensor([1, 1])]; + tensor v_123_pad_type_0 = const()[name = tensor("v_123_pad_type_0"), val = tensor("custom")]; + tensor v_123_pad_0 = const()[name = tensor("v_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(812411200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814377344))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_123_cast_fp16 = conv(dilations = var_6228, groups = var_4971, pad = v_123_pad_0, pad_type = v_123_pad_type_0, strides = var_6226, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_123_cast_fp16")]; + tensor var_6232 = const()[name = tensor("op_6232"), val = tensor([1, 20, 64, -1])]; + tensor var_6233_cast_fp16 = reshape(shape = var_6232, x = q_123_cast_fp16)[name = tensor("op_6233_cast_fp16")]; + tensor var_6234 = const()[name = tensor("op_6234"), val = tensor([1, 20, 64, -1])]; + tensor var_6235_cast_fp16 = reshape(shape = var_6234, x = k_123_cast_fp16)[name = tensor("op_6235_cast_fp16")]; + tensor var_6236 = const()[name = tensor("op_6236"), val = tensor([1, 20, 64, -1])]; + tensor var_6237_cast_fp16 = reshape(shape = var_6236, x = v_123_cast_fp16)[name = tensor("op_6237_cast_fp16")]; + tensor attn_weights_245_transpose_x_0 = const()[name = tensor("attn_weights_245_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_245_transpose_y_0 = const()[name = tensor("attn_weights_245_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_245_cast_fp16 = matmul(transpose_x = attn_weights_245_transpose_x_0, transpose_y = attn_weights_245_transpose_y_0, x = var_6233_cast_fp16, y = var_6235_cast_fp16)[name = tensor("attn_weights_245_cast_fp16")]; + tensor attn_weights_247_cast_fp16 = mul(x = attn_weights_245_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_247_cast_fp16")]; + tensor var_6241_cast_fp16 = softmax(axis = var_4955, x = attn_weights_247_cast_fp16)[name = tensor("op_6241_cast_fp16")]; + tensor attn_123_transpose_x_0 = const()[name = tensor("attn_123_transpose_x_0"), val = tensor(false)]; + tensor attn_123_transpose_y_0 = const()[name = tensor("attn_123_transpose_y_0"), val = tensor(true)]; + tensor attn_123_cast_fp16 = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6237_cast_fp16, y = var_6241_cast_fp16)[name = tensor("attn_123_cast_fp16")]; + tensor var_6245 = const()[name = tensor("op_6245"), val = tensor([1, 1280, 1, -1])]; + tensor input_379_cast_fp16 = reshape(shape = var_6245, x = attn_123_cast_fp16)[name = tensor("input_379_cast_fp16")]; + 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 var_6254_pad_type_0 = const()[name = tensor("op_6254_pad_type_0"), val = tensor("custom")]; + tensor var_6254_pad_0 = const()[name = tensor("op_6254_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814377536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815606400))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815606592)))]; + tensor var_6254_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_6252, groups = var_4971, pad = var_6254_pad_0, pad_type = var_6254_pad_type_0, strides = var_6250, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_379_cast_fp16)[name = tensor("op_6254_cast_fp16")]; + tensor inputs_185_cast_fp16 = add(x = var_6254_cast_fp16, y = inputs_183_cast_fp16)[name = tensor("inputs_185_cast_fp16")]; + tensor input_381_axes_0 = const()[name = tensor("input_381_axes_0"), val = tensor([1])]; + tensor input_381_gamma_0_to_fp16 = const()[name = tensor("input_381_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815609216)))]; + tensor input_381_beta_0_to_fp16 = const()[name = tensor("input_381_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815611840)))]; + tensor var_6264_to_fp16 = const()[name = tensor("op_6264_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_381_cast_fp16 = layer_norm(axes = input_381_axes_0, beta = input_381_beta_0_to_fp16, epsilon = var_6264_to_fp16, gamma = input_381_gamma_0_to_fp16, x = inputs_185_cast_fp16)[name = tensor("input_381_cast_fp16")]; + tensor var_6280 = const()[name = tensor("op_6280"), val = tensor([1, 1])]; + tensor var_6282 = const()[name = tensor("op_6282"), val = tensor([1, 1])]; + tensor var_6284_pad_type_0 = const()[name = tensor("op_6284_pad_type_0"), val = tensor("custom")]; + tensor var_6284_pad_0 = const()[name = tensor("op_6284_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815614464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825444928))), name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825445120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825452864))), name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6284_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6282, groups = var_4971, pad = var_6284_pad_0, pad_type = var_6284_pad_type_0, strides = var_6280, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = tensor("op_6284_cast_fp16")]; + tensor var_6285_split_sizes_0 = const()[name = tensor("op_6285_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6285_axis_0 = const()[name = tensor("op_6285_axis_0"), val = tensor(1)]; + tensor var_6285_cast_fp16_0, tensor var_6285_cast_fp16_1 = split(axis = var_6285_axis_0, split_sizes = var_6285_split_sizes_0, x = var_6284_cast_fp16)[name = tensor("op_6285_cast_fp16")]; + tensor var_6287_mode_0 = const()[name = tensor("op_6287_mode_0"), val = tensor("EXACT")]; + tensor var_6287_cast_fp16 = gelu(mode = var_6287_mode_0, x = var_6285_cast_fp16_1)[name = tensor("op_6287_cast_fp16")]; + tensor input_383_cast_fp16 = mul(x = var_6285_cast_fp16_0, y = var_6287_cast_fp16)[name = tensor("input_383_cast_fp16")]; + tensor var_6291 = const()[name = tensor("op_6291"), val = tensor([1, 1])]; + tensor var_6293 = const()[name = tensor("op_6293"), val = tensor([1, 1])]; + tensor var_6295_pad_type_0 = const()[name = tensor("op_6295_pad_type_0"), val = tensor("custom")]; + tensor var_6295_pad_0 = const()[name = tensor("op_6295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825453056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830368320))), name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830368512)))]; + tensor var_6295_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_6293, groups = var_4971, pad = var_6295_pad_0, pad_type = var_6295_pad_type_0, strides = var_6291, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_383_cast_fp16)[name = tensor("op_6295_cast_fp16")]; + tensor inputs_187_cast_fp16 = add(x = var_6295_cast_fp16, y = inputs_185_cast_fp16)[name = tensor("inputs_187_cast_fp16")]; + tensor hidden_states_251_axes_0 = const()[name = tensor("hidden_states_251_axes_0"), val = tensor([1])]; + tensor hidden_states_251_gamma_0_to_fp16 = const()[name = tensor("hidden_states_251_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830371136)))]; + tensor hidden_states_251_beta_0_to_fp16 = const()[name = tensor("hidden_states_251_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830373760)))]; + tensor var_6311_to_fp16 = const()[name = tensor("op_6311_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_251_cast_fp16 = layer_norm(axes = hidden_states_251_axes_0, beta = hidden_states_251_beta_0_to_fp16, epsilon = var_6311_to_fp16, gamma = hidden_states_251_gamma_0_to_fp16, x = inputs_187_cast_fp16)[name = tensor("hidden_states_251_cast_fp16")]; + tensor var_6326 = const()[name = tensor("op_6326"), val = tensor([1, 1])]; + tensor var_6328 = const()[name = tensor("op_6328"), val = tensor([1, 1])]; + tensor q_125_pad_type_0 = const()[name = tensor("q_125_pad_type_0"), val = tensor("custom")]; + tensor q_125_pad_0 = const()[name = tensor("q_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830376384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(831605248))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_125_cast_fp16 = conv(dilations = var_6328, groups = var_4971, pad = q_125_pad_0, pad_type = q_125_pad_type_0, strides = var_6326, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_251_cast_fp16)[name = tensor("q_125_cast_fp16")]; + tensor var_6332 = const()[name = tensor("op_6332"), val = tensor([1, 1])]; + tensor var_6334 = const()[name = tensor("op_6334"), 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 mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(831605440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832834304))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_125_cast_fp16 = conv(dilations = var_6334, groups = var_4971, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_6332, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_251_cast_fp16)[name = tensor("k_125_cast_fp16")]; + tensor var_6338 = const()[name = tensor("op_6338"), val = tensor([1, 1])]; + tensor var_6340 = const()[name = tensor("op_6340"), val = tensor([1, 1])]; + tensor v_125_pad_type_0 = const()[name = tensor("v_125_pad_type_0"), val = tensor("custom")]; + tensor v_125_pad_0 = const()[name = tensor("v_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832834496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834063360))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_125_cast_fp16 = conv(dilations = var_6340, groups = var_4971, pad = v_125_pad_0, pad_type = v_125_pad_type_0, strides = var_6338, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_251_cast_fp16)[name = tensor("v_125_cast_fp16")]; + tensor var_6344 = const()[name = tensor("op_6344"), val = tensor([1, 20, 64, -1])]; + tensor var_6345_cast_fp16 = reshape(shape = var_6344, x = q_125_cast_fp16)[name = tensor("op_6345_cast_fp16")]; + tensor var_6346 = const()[name = tensor("op_6346"), val = tensor([1, 20, 64, -1])]; + tensor var_6347_cast_fp16 = reshape(shape = var_6346, x = k_125_cast_fp16)[name = tensor("op_6347_cast_fp16")]; + tensor var_6348 = const()[name = tensor("op_6348"), val = tensor([1, 20, 64, -1])]; + tensor var_6349_cast_fp16 = reshape(shape = var_6348, x = v_125_cast_fp16)[name = tensor("op_6349_cast_fp16")]; + tensor attn_weights_249_transpose_x_0 = const()[name = tensor("attn_weights_249_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_249_transpose_y_0 = const()[name = tensor("attn_weights_249_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_6345_cast_fp16, y = var_6347_cast_fp16)[name = tensor("attn_weights_249_cast_fp16")]; + tensor attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_251_cast_fp16")]; + tensor var_6353_cast_fp16 = softmax(axis = var_4955, x = attn_weights_251_cast_fp16)[name = tensor("op_6353_cast_fp16")]; + tensor attn_125_transpose_x_0 = const()[name = tensor("attn_125_transpose_x_0"), val = tensor(false)]; + tensor attn_125_transpose_y_0 = const()[name = tensor("attn_125_transpose_y_0"), val = tensor(true)]; + tensor attn_125_cast_fp16 = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6349_cast_fp16, y = var_6353_cast_fp16)[name = tensor("attn_125_cast_fp16")]; + tensor var_6357 = const()[name = tensor("op_6357"), val = tensor([1, 1280, 1, -1])]; + tensor input_385_cast_fp16 = reshape(shape = var_6357, x = attn_125_cast_fp16)[name = tensor("input_385_cast_fp16")]; + tensor var_6362 = const()[name = tensor("op_6362"), val = tensor([1, 1])]; + tensor var_6364 = const()[name = tensor("op_6364"), val = tensor([1, 1])]; + tensor var_6366_pad_type_0 = const()[name = tensor("op_6366_pad_type_0"), val = tensor("custom")]; + tensor var_6366_pad_0 = const()[name = tensor("op_6366_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834063552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835292416))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835292608)))]; + tensor var_6366_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_6364, groups = var_4971, pad = var_6366_pad_0, pad_type = var_6366_pad_type_0, strides = var_6362, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_385_cast_fp16)[name = tensor("op_6366_cast_fp16")]; + tensor inputs_189_cast_fp16 = add(x = var_6366_cast_fp16, y = inputs_187_cast_fp16)[name = tensor("inputs_189_cast_fp16")]; + tensor hidden_states_253_axes_0 = const()[name = tensor("hidden_states_253_axes_0"), val = tensor([1])]; + tensor hidden_states_253_gamma_0_to_fp16 = const()[name = tensor("hidden_states_253_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835295232)))]; + tensor hidden_states_253_beta_0_to_fp16 = const()[name = tensor("hidden_states_253_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835297856)))]; + tensor var_6376_to_fp16 = const()[name = tensor("op_6376_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_253_cast_fp16 = layer_norm(axes = hidden_states_253_axes_0, beta = hidden_states_253_beta_0_to_fp16, epsilon = var_6376_to_fp16, gamma = hidden_states_253_gamma_0_to_fp16, x = inputs_189_cast_fp16)[name = tensor("hidden_states_253_cast_fp16")]; + tensor var_6391 = const()[name = tensor("op_6391"), val = tensor([1, 1])]; + tensor var_6393 = const()[name = tensor("op_6393"), val = tensor([1, 1])]; + tensor q_127_pad_type_0 = const()[name = tensor("q_127_pad_type_0"), val = tensor("custom")]; + tensor q_127_pad_0 = const()[name = tensor("q_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835300480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836529344))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_127_cast_fp16 = conv(dilations = var_6393, groups = var_4971, pad = q_127_pad_0, pad_type = q_127_pad_type_0, strides = var_6391, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_253_cast_fp16)[name = tensor("q_127_cast_fp16")]; + tensor var_6397 = const()[name = tensor("op_6397"), val = tensor([1, 1])]; + tensor var_6399 = const()[name = tensor("op_6399"), val = tensor([1, 1])]; + tensor k_127_pad_type_0 = const()[name = tensor("k_127_pad_type_0"), val = tensor("custom")]; + tensor k_127_pad_0 = const()[name = tensor("k_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836529536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838495680))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_127_cast_fp16 = conv(dilations = var_6399, groups = var_4971, pad = k_127_pad_0, pad_type = k_127_pad_type_0, strides = var_6397, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_127_cast_fp16")]; + tensor var_6403 = const()[name = tensor("op_6403"), val = tensor([1, 1])]; + tensor var_6405 = const()[name = tensor("op_6405"), val = tensor([1, 1])]; + tensor v_127_pad_type_0 = const()[name = tensor("v_127_pad_type_0"), val = tensor("custom")]; + tensor v_127_pad_0 = const()[name = tensor("v_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838495872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(840462016))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_127_cast_fp16 = conv(dilations = var_6405, groups = var_4971, pad = v_127_pad_0, pad_type = v_127_pad_type_0, strides = var_6403, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_127_cast_fp16")]; + tensor var_6409 = const()[name = tensor("op_6409"), val = tensor([1, 20, 64, -1])]; + tensor var_6410_cast_fp16 = reshape(shape = var_6409, x = q_127_cast_fp16)[name = tensor("op_6410_cast_fp16")]; + tensor var_6411 = const()[name = tensor("op_6411"), val = tensor([1, 20, 64, -1])]; + tensor var_6412_cast_fp16 = reshape(shape = var_6411, x = k_127_cast_fp16)[name = tensor("op_6412_cast_fp16")]; + tensor var_6413 = const()[name = tensor("op_6413"), val = tensor([1, 20, 64, -1])]; + tensor var_6414_cast_fp16 = reshape(shape = var_6413, x = v_127_cast_fp16)[name = tensor("op_6414_cast_fp16")]; + tensor attn_weights_253_transpose_x_0 = const()[name = tensor("attn_weights_253_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_253_transpose_y_0 = const()[name = tensor("attn_weights_253_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_253_cast_fp16 = matmul(transpose_x = attn_weights_253_transpose_x_0, transpose_y = attn_weights_253_transpose_y_0, x = var_6410_cast_fp16, y = var_6412_cast_fp16)[name = tensor("attn_weights_253_cast_fp16")]; + tensor attn_weights_255_cast_fp16 = mul(x = attn_weights_253_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_255_cast_fp16")]; + tensor var_6418_cast_fp16 = softmax(axis = var_4955, x = attn_weights_255_cast_fp16)[name = tensor("op_6418_cast_fp16")]; + tensor attn_127_transpose_x_0 = const()[name = tensor("attn_127_transpose_x_0"), val = tensor(false)]; + tensor attn_127_transpose_y_0 = const()[name = tensor("attn_127_transpose_y_0"), val = tensor(true)]; + tensor attn_127_cast_fp16 = matmul(transpose_x = attn_127_transpose_x_0, transpose_y = attn_127_transpose_y_0, x = var_6414_cast_fp16, y = var_6418_cast_fp16)[name = tensor("attn_127_cast_fp16")]; + tensor var_6422 = const()[name = tensor("op_6422"), val = tensor([1, 1280, 1, -1])]; + tensor input_387_cast_fp16 = reshape(shape = var_6422, x = attn_127_cast_fp16)[name = tensor("input_387_cast_fp16")]; + tensor var_6427 = const()[name = tensor("op_6427"), val = tensor([1, 1])]; + tensor var_6429 = const()[name = tensor("op_6429"), val = tensor([1, 1])]; + tensor var_6431_pad_type_0 = const()[name = tensor("op_6431_pad_type_0"), val = tensor("custom")]; + tensor var_6431_pad_0 = const()[name = tensor("op_6431_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(840462208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841691072))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841691264)))]; + tensor var_6431_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_6429, groups = var_4971, pad = var_6431_pad_0, pad_type = var_6431_pad_type_0, strides = var_6427, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_387_cast_fp16)[name = tensor("op_6431_cast_fp16")]; + tensor inputs_191_cast_fp16 = add(x = var_6431_cast_fp16, y = inputs_189_cast_fp16)[name = tensor("inputs_191_cast_fp16")]; + tensor input_389_axes_0 = const()[name = tensor("input_389_axes_0"), val = tensor([1])]; + tensor input_389_gamma_0_to_fp16 = const()[name = tensor("input_389_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841693888)))]; + tensor input_389_beta_0_to_fp16 = const()[name = tensor("input_389_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841696512)))]; + tensor var_6441_to_fp16 = const()[name = tensor("op_6441_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_389_cast_fp16 = layer_norm(axes = input_389_axes_0, beta = input_389_beta_0_to_fp16, epsilon = var_6441_to_fp16, gamma = input_389_gamma_0_to_fp16, x = inputs_191_cast_fp16)[name = tensor("input_389_cast_fp16")]; + tensor var_6457 = const()[name = tensor("op_6457"), val = tensor([1, 1])]; + tensor var_6459 = const()[name = tensor("op_6459"), val = tensor([1, 1])]; + tensor var_6461_pad_type_0 = const()[name = tensor("op_6461_pad_type_0"), val = tensor("custom")]; + tensor var_6461_pad_0 = const()[name = tensor("op_6461_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841699136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851529600))), name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851529792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851537536))), name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6461_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6459, groups = var_4971, pad = var_6461_pad_0, pad_type = var_6461_pad_type_0, strides = var_6457, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = tensor("op_6461_cast_fp16")]; + tensor var_6462_split_sizes_0 = const()[name = tensor("op_6462_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6462_axis_0 = const()[name = tensor("op_6462_axis_0"), val = tensor(1)]; + tensor var_6462_cast_fp16_0, tensor var_6462_cast_fp16_1 = split(axis = var_6462_axis_0, split_sizes = var_6462_split_sizes_0, x = var_6461_cast_fp16)[name = tensor("op_6462_cast_fp16")]; + tensor var_6464_mode_0 = const()[name = tensor("op_6464_mode_0"), val = tensor("EXACT")]; + tensor var_6464_cast_fp16 = gelu(mode = var_6464_mode_0, x = var_6462_cast_fp16_1)[name = tensor("op_6464_cast_fp16")]; + tensor input_391_cast_fp16 = mul(x = var_6462_cast_fp16_0, y = var_6464_cast_fp16)[name = tensor("input_391_cast_fp16")]; + tensor var_6468 = const()[name = tensor("op_6468"), val = tensor([1, 1])]; + tensor var_6470 = const()[name = tensor("op_6470"), val = tensor([1, 1])]; + tensor var_6472_pad_type_0 = const()[name = tensor("op_6472_pad_type_0"), val = tensor("custom")]; + tensor var_6472_pad_0 = const()[name = tensor("op_6472_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851537728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856452992))), name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856453184)))]; + tensor var_6472_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_6470, groups = var_4971, pad = var_6472_pad_0, pad_type = var_6472_pad_type_0, strides = var_6468, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = tensor("op_6472_cast_fp16")]; + tensor inputs_193_cast_fp16 = add(x = var_6472_cast_fp16, y = inputs_191_cast_fp16)[name = tensor("inputs_193_cast_fp16")]; + tensor hidden_states_257_axes_0 = const()[name = tensor("hidden_states_257_axes_0"), val = tensor([1])]; + tensor hidden_states_257_gamma_0_to_fp16 = const()[name = tensor("hidden_states_257_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856455808)))]; + tensor hidden_states_257_beta_0_to_fp16 = const()[name = tensor("hidden_states_257_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856458432)))]; + tensor var_6488_to_fp16 = const()[name = tensor("op_6488_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_257_cast_fp16 = layer_norm(axes = hidden_states_257_axes_0, beta = hidden_states_257_beta_0_to_fp16, epsilon = var_6488_to_fp16, gamma = hidden_states_257_gamma_0_to_fp16, x = inputs_193_cast_fp16)[name = tensor("hidden_states_257_cast_fp16")]; + tensor var_6503 = const()[name = tensor("op_6503"), val = tensor([1, 1])]; + tensor var_6505 = const()[name = tensor("op_6505"), val = tensor([1, 1])]; + tensor q_129_pad_type_0 = const()[name = tensor("q_129_pad_type_0"), val = tensor("custom")]; + tensor q_129_pad_0 = const()[name = tensor("q_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856461056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(857689920))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_129_cast_fp16 = conv(dilations = var_6505, groups = var_4971, pad = q_129_pad_0, pad_type = q_129_pad_type_0, strides = var_6503, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_257_cast_fp16)[name = tensor("q_129_cast_fp16")]; + tensor var_6509 = const()[name = tensor("op_6509"), val = tensor([1, 1])]; + tensor var_6511 = const()[name = tensor("op_6511"), val = tensor([1, 1])]; + tensor k_129_pad_type_0 = const()[name = tensor("k_129_pad_type_0"), val = tensor("custom")]; + tensor k_129_pad_0 = const()[name = tensor("k_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(857690112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858918976))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_129_cast_fp16 = conv(dilations = var_6511, groups = var_4971, pad = k_129_pad_0, pad_type = k_129_pad_type_0, strides = var_6509, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_257_cast_fp16)[name = tensor("k_129_cast_fp16")]; + tensor var_6515 = const()[name = tensor("op_6515"), val = tensor([1, 1])]; + tensor var_6517 = const()[name = tensor("op_6517"), val = tensor([1, 1])]; + tensor v_129_pad_type_0 = const()[name = tensor("v_129_pad_type_0"), val = tensor("custom")]; + tensor v_129_pad_0 = const()[name = tensor("v_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858919168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860148032))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_129_cast_fp16 = conv(dilations = var_6517, groups = var_4971, pad = v_129_pad_0, pad_type = v_129_pad_type_0, strides = var_6515, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_257_cast_fp16)[name = tensor("v_129_cast_fp16")]; + tensor var_6521 = const()[name = tensor("op_6521"), val = tensor([1, 20, 64, -1])]; + tensor var_6522_cast_fp16 = reshape(shape = var_6521, x = q_129_cast_fp16)[name = tensor("op_6522_cast_fp16")]; + tensor var_6523 = const()[name = tensor("op_6523"), val = tensor([1, 20, 64, -1])]; + tensor var_6524_cast_fp16 = reshape(shape = var_6523, x = k_129_cast_fp16)[name = tensor("op_6524_cast_fp16")]; + tensor var_6525 = const()[name = tensor("op_6525"), val = tensor([1, 20, 64, -1])]; + tensor var_6526_cast_fp16 = reshape(shape = var_6525, x = v_129_cast_fp16)[name = tensor("op_6526_cast_fp16")]; + tensor attn_weights_257_transpose_x_0 = const()[name = tensor("attn_weights_257_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_257_transpose_y_0 = const()[name = tensor("attn_weights_257_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6522_cast_fp16, y = var_6524_cast_fp16)[name = tensor("attn_weights_257_cast_fp16")]; + tensor attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_259_cast_fp16")]; + tensor var_6530_cast_fp16 = softmax(axis = var_4955, x = attn_weights_259_cast_fp16)[name = tensor("op_6530_cast_fp16")]; + tensor attn_129_transpose_x_0 = const()[name = tensor("attn_129_transpose_x_0"), val = tensor(false)]; + tensor attn_129_transpose_y_0 = const()[name = tensor("attn_129_transpose_y_0"), val = tensor(true)]; + tensor attn_129_cast_fp16 = matmul(transpose_x = attn_129_transpose_x_0, transpose_y = attn_129_transpose_y_0, x = var_6526_cast_fp16, y = var_6530_cast_fp16)[name = tensor("attn_129_cast_fp16")]; + tensor var_6534 = const()[name = tensor("op_6534"), val = tensor([1, 1280, 1, -1])]; + tensor input_393_cast_fp16 = reshape(shape = var_6534, x = attn_129_cast_fp16)[name = tensor("input_393_cast_fp16")]; + tensor var_6539 = const()[name = tensor("op_6539"), val = tensor([1, 1])]; + tensor var_6541 = const()[name = tensor("op_6541"), val = tensor([1, 1])]; + tensor var_6543_pad_type_0 = const()[name = tensor("op_6543_pad_type_0"), val = tensor("custom")]; + tensor var_6543_pad_0 = const()[name = tensor("op_6543_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860148224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861377088))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861377280)))]; + tensor var_6543_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_6541, groups = var_4971, pad = var_6543_pad_0, pad_type = var_6543_pad_type_0, strides = var_6539, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = tensor("op_6543_cast_fp16")]; + tensor inputs_195_cast_fp16 = add(x = var_6543_cast_fp16, y = inputs_193_cast_fp16)[name = tensor("inputs_195_cast_fp16")]; + tensor hidden_states_259_axes_0 = const()[name = tensor("hidden_states_259_axes_0"), val = tensor([1])]; + tensor hidden_states_259_gamma_0_to_fp16 = const()[name = tensor("hidden_states_259_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861379904)))]; + tensor hidden_states_259_beta_0_to_fp16 = const()[name = tensor("hidden_states_259_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861382528)))]; + tensor var_6553_to_fp16 = const()[name = tensor("op_6553_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_259_cast_fp16 = layer_norm(axes = hidden_states_259_axes_0, beta = hidden_states_259_beta_0_to_fp16, epsilon = var_6553_to_fp16, gamma = hidden_states_259_gamma_0_to_fp16, x = inputs_195_cast_fp16)[name = tensor("hidden_states_259_cast_fp16")]; + tensor var_6568 = const()[name = tensor("op_6568"), val = tensor([1, 1])]; + tensor var_6570 = const()[name = tensor("op_6570"), val = tensor([1, 1])]; + tensor q_131_pad_type_0 = const()[name = tensor("q_131_pad_type_0"), val = tensor("custom")]; + tensor q_131_pad_0 = const()[name = tensor("q_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861385152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(862614016))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_131_cast_fp16 = conv(dilations = var_6570, groups = var_4971, pad = q_131_pad_0, pad_type = q_131_pad_type_0, strides = var_6568, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_259_cast_fp16)[name = tensor("q_131_cast_fp16")]; + tensor var_6574 = const()[name = tensor("op_6574"), val = tensor([1, 1])]; + tensor var_6576 = const()[name = tensor("op_6576"), val = tensor([1, 1])]; + tensor k_131_pad_type_0 = const()[name = tensor("k_131_pad_type_0"), val = tensor("custom")]; + tensor k_131_pad_0 = const()[name = tensor("k_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(862614208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(864580352))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_131_cast_fp16 = conv(dilations = var_6576, groups = var_4971, pad = k_131_pad_0, pad_type = k_131_pad_type_0, strides = var_6574, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_131_cast_fp16")]; + tensor var_6580 = const()[name = tensor("op_6580"), val = tensor([1, 1])]; + tensor var_6582 = const()[name = tensor("op_6582"), val = tensor([1, 1])]; + tensor v_131_pad_type_0 = const()[name = tensor("v_131_pad_type_0"), val = tensor("custom")]; + tensor v_131_pad_0 = const()[name = tensor("v_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(864580544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(866546688))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_131_cast_fp16 = conv(dilations = var_6582, groups = var_4971, pad = v_131_pad_0, pad_type = v_131_pad_type_0, strides = var_6580, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_131_cast_fp16")]; + tensor var_6586 = const()[name = tensor("op_6586"), val = tensor([1, 20, 64, -1])]; + tensor var_6587_cast_fp16 = reshape(shape = var_6586, x = q_131_cast_fp16)[name = tensor("op_6587_cast_fp16")]; + tensor var_6588 = const()[name = tensor("op_6588"), val = tensor([1, 20, 64, -1])]; + tensor var_6589_cast_fp16 = reshape(shape = var_6588, x = k_131_cast_fp16)[name = tensor("op_6589_cast_fp16")]; + tensor var_6590 = const()[name = tensor("op_6590"), val = tensor([1, 20, 64, -1])]; + tensor var_6591_cast_fp16 = reshape(shape = var_6590, x = v_131_cast_fp16)[name = tensor("op_6591_cast_fp16")]; + tensor attn_weights_261_transpose_x_0 = const()[name = tensor("attn_weights_261_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_261_transpose_y_0 = const()[name = tensor("attn_weights_261_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_261_cast_fp16 = matmul(transpose_x = attn_weights_261_transpose_x_0, transpose_y = attn_weights_261_transpose_y_0, x = var_6587_cast_fp16, y = var_6589_cast_fp16)[name = tensor("attn_weights_261_cast_fp16")]; + tensor attn_weights_263_cast_fp16 = mul(x = attn_weights_261_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_263_cast_fp16")]; + tensor var_6595_cast_fp16 = softmax(axis = var_4955, x = attn_weights_263_cast_fp16)[name = tensor("op_6595_cast_fp16")]; + tensor attn_131_transpose_x_0 = const()[name = tensor("attn_131_transpose_x_0"), val = tensor(false)]; + tensor attn_131_transpose_y_0 = const()[name = tensor("attn_131_transpose_y_0"), val = tensor(true)]; + tensor attn_131_cast_fp16 = matmul(transpose_x = attn_131_transpose_x_0, transpose_y = attn_131_transpose_y_0, x = var_6591_cast_fp16, y = var_6595_cast_fp16)[name = tensor("attn_131_cast_fp16")]; + tensor var_6599 = const()[name = tensor("op_6599"), val = tensor([1, 1280, 1, -1])]; + tensor input_395_cast_fp16 = reshape(shape = var_6599, x = attn_131_cast_fp16)[name = tensor("input_395_cast_fp16")]; + tensor var_6604 = const()[name = tensor("op_6604"), val = tensor([1, 1])]; + tensor var_6606 = const()[name = tensor("op_6606"), val = tensor([1, 1])]; + tensor var_6608_pad_type_0 = const()[name = tensor("op_6608_pad_type_0"), val = tensor("custom")]; + tensor var_6608_pad_0 = const()[name = tensor("op_6608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(866546880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867775744))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867775936)))]; + tensor var_6608_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_6606, groups = var_4971, pad = var_6608_pad_0, pad_type = var_6608_pad_type_0, strides = var_6604, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_395_cast_fp16)[name = tensor("op_6608_cast_fp16")]; + tensor inputs_197_cast_fp16 = add(x = var_6608_cast_fp16, y = inputs_195_cast_fp16)[name = tensor("inputs_197_cast_fp16")]; + tensor input_397_axes_0 = const()[name = tensor("input_397_axes_0"), val = tensor([1])]; + tensor input_397_gamma_0_to_fp16 = const()[name = tensor("input_397_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867778560)))]; + tensor input_397_beta_0_to_fp16 = const()[name = tensor("input_397_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867781184)))]; + tensor var_6618_to_fp16 = const()[name = tensor("op_6618_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_397_cast_fp16 = layer_norm(axes = input_397_axes_0, beta = input_397_beta_0_to_fp16, epsilon = var_6618_to_fp16, gamma = input_397_gamma_0_to_fp16, x = inputs_197_cast_fp16)[name = tensor("input_397_cast_fp16")]; + tensor var_6634 = const()[name = tensor("op_6634"), val = tensor([1, 1])]; + tensor var_6636 = const()[name = tensor("op_6636"), val = tensor([1, 1])]; + tensor var_6638_pad_type_0 = const()[name = tensor("op_6638_pad_type_0"), val = tensor("custom")]; + tensor var_6638_pad_0 = const()[name = tensor("op_6638_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867783808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877614272))), name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877614464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877622208))), name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6638_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6636, groups = var_4971, pad = var_6638_pad_0, pad_type = var_6638_pad_type_0, strides = var_6634, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_397_cast_fp16)[name = tensor("op_6638_cast_fp16")]; + tensor var_6639_split_sizes_0 = const()[name = tensor("op_6639_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6639_axis_0 = const()[name = tensor("op_6639_axis_0"), val = tensor(1)]; + tensor var_6639_cast_fp16_0, tensor var_6639_cast_fp16_1 = split(axis = var_6639_axis_0, split_sizes = var_6639_split_sizes_0, x = var_6638_cast_fp16)[name = tensor("op_6639_cast_fp16")]; + tensor var_6641_mode_0 = const()[name = tensor("op_6641_mode_0"), val = tensor("EXACT")]; + tensor var_6641_cast_fp16 = gelu(mode = var_6641_mode_0, x = var_6639_cast_fp16_1)[name = tensor("op_6641_cast_fp16")]; + tensor input_399_cast_fp16 = mul(x = var_6639_cast_fp16_0, y = var_6641_cast_fp16)[name = tensor("input_399_cast_fp16")]; + tensor var_6645 = const()[name = tensor("op_6645"), val = tensor([1, 1])]; + tensor var_6647 = const()[name = tensor("op_6647"), val = tensor([1, 1])]; + tensor var_6649_pad_type_0 = const()[name = tensor("op_6649_pad_type_0"), val = tensor("custom")]; + tensor var_6649_pad_0 = const()[name = tensor("op_6649_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877622400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882537664))), name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882537856)))]; + tensor var_6649_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_6647, groups = var_4971, pad = var_6649_pad_0, pad_type = var_6649_pad_type_0, strides = var_6645, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_399_cast_fp16)[name = tensor("op_6649_cast_fp16")]; + tensor inputs_199_cast_fp16 = add(x = var_6649_cast_fp16, y = inputs_197_cast_fp16)[name = tensor("inputs_199_cast_fp16")]; + tensor hidden_states_263_axes_0 = const()[name = tensor("hidden_states_263_axes_0"), val = tensor([1])]; + tensor hidden_states_263_gamma_0_to_fp16 = const()[name = tensor("hidden_states_263_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882540480)))]; + tensor hidden_states_263_beta_0_to_fp16 = const()[name = tensor("hidden_states_263_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882543104)))]; + tensor var_6665_to_fp16 = const()[name = tensor("op_6665_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_263_cast_fp16 = layer_norm(axes = hidden_states_263_axes_0, beta = hidden_states_263_beta_0_to_fp16, epsilon = var_6665_to_fp16, gamma = hidden_states_263_gamma_0_to_fp16, x = inputs_199_cast_fp16)[name = tensor("hidden_states_263_cast_fp16")]; + tensor var_6680 = const()[name = tensor("op_6680"), val = tensor([1, 1])]; + tensor var_6682 = const()[name = tensor("op_6682"), val = tensor([1, 1])]; + tensor q_133_pad_type_0 = const()[name = tensor("q_133_pad_type_0"), val = tensor("custom")]; + tensor q_133_pad_0 = const()[name = tensor("q_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882545728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(883774592))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_133_cast_fp16 = conv(dilations = var_6682, groups = var_4971, pad = q_133_pad_0, pad_type = q_133_pad_type_0, strides = var_6680, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_263_cast_fp16)[name = tensor("q_133_cast_fp16")]; + tensor var_6686 = const()[name = tensor("op_6686"), val = tensor([1, 1])]; + tensor var_6688 = const()[name = tensor("op_6688"), val = tensor([1, 1])]; + tensor k_133_pad_type_0 = const()[name = tensor("k_133_pad_type_0"), val = tensor("custom")]; + tensor k_133_pad_0 = const()[name = tensor("k_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(883774784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(885003648))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_133_cast_fp16 = conv(dilations = var_6688, groups = var_4971, pad = k_133_pad_0, pad_type = k_133_pad_type_0, strides = var_6686, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_263_cast_fp16)[name = tensor("k_133_cast_fp16")]; + tensor var_6692 = const()[name = tensor("op_6692"), val = tensor([1, 1])]; + tensor var_6694 = const()[name = tensor("op_6694"), val = tensor([1, 1])]; + tensor v_133_pad_type_0 = const()[name = tensor("v_133_pad_type_0"), val = tensor("custom")]; + tensor v_133_pad_0 = const()[name = tensor("v_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(885003840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(886232704))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_133_cast_fp16 = conv(dilations = var_6694, groups = var_4971, pad = v_133_pad_0, pad_type = v_133_pad_type_0, strides = var_6692, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_263_cast_fp16)[name = tensor("v_133_cast_fp16")]; + tensor var_6698 = const()[name = tensor("op_6698"), val = tensor([1, 20, 64, -1])]; + tensor var_6699_cast_fp16 = reshape(shape = var_6698, x = q_133_cast_fp16)[name = tensor("op_6699_cast_fp16")]; + tensor var_6700 = const()[name = tensor("op_6700"), val = tensor([1, 20, 64, -1])]; + tensor var_6701_cast_fp16 = reshape(shape = var_6700, x = k_133_cast_fp16)[name = tensor("op_6701_cast_fp16")]; + tensor var_6702 = const()[name = tensor("op_6702"), val = tensor([1, 20, 64, -1])]; + tensor var_6703_cast_fp16 = reshape(shape = var_6702, x = v_133_cast_fp16)[name = tensor("op_6703_cast_fp16")]; + tensor attn_weights_265_transpose_x_0 = const()[name = tensor("attn_weights_265_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_265_transpose_y_0 = const()[name = tensor("attn_weights_265_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6699_cast_fp16, y = var_6701_cast_fp16)[name = tensor("attn_weights_265_cast_fp16")]; + tensor attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_267_cast_fp16")]; + tensor var_6707_cast_fp16 = softmax(axis = var_4955, x = attn_weights_267_cast_fp16)[name = tensor("op_6707_cast_fp16")]; + tensor attn_133_transpose_x_0 = const()[name = tensor("attn_133_transpose_x_0"), val = tensor(false)]; + tensor attn_133_transpose_y_0 = const()[name = tensor("attn_133_transpose_y_0"), val = tensor(true)]; + tensor attn_133_cast_fp16 = matmul(transpose_x = attn_133_transpose_x_0, transpose_y = attn_133_transpose_y_0, x = var_6703_cast_fp16, y = var_6707_cast_fp16)[name = tensor("attn_133_cast_fp16")]; + tensor var_6711 = const()[name = tensor("op_6711"), val = tensor([1, 1280, 1, -1])]; + tensor input_401_cast_fp16 = reshape(shape = var_6711, x = attn_133_cast_fp16)[name = tensor("input_401_cast_fp16")]; + tensor var_6716 = const()[name = tensor("op_6716"), val = tensor([1, 1])]; + tensor var_6718 = const()[name = tensor("op_6718"), val = tensor([1, 1])]; + tensor var_6720_pad_type_0 = const()[name = tensor("op_6720_pad_type_0"), val = tensor("custom")]; + tensor var_6720_pad_0 = const()[name = tensor("op_6720_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(886232896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887461760))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887461952)))]; + tensor var_6720_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_6718, groups = var_4971, pad = var_6720_pad_0, pad_type = var_6720_pad_type_0, strides = var_6716, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = tensor("op_6720_cast_fp16")]; + tensor inputs_201_cast_fp16 = add(x = var_6720_cast_fp16, y = inputs_199_cast_fp16)[name = tensor("inputs_201_cast_fp16")]; + tensor hidden_states_265_axes_0 = const()[name = tensor("hidden_states_265_axes_0"), val = tensor([1])]; + tensor hidden_states_265_gamma_0_to_fp16 = const()[name = tensor("hidden_states_265_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887464576)))]; + tensor hidden_states_265_beta_0_to_fp16 = const()[name = tensor("hidden_states_265_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887467200)))]; + tensor var_6730_to_fp16 = const()[name = tensor("op_6730_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_265_cast_fp16 = layer_norm(axes = hidden_states_265_axes_0, beta = hidden_states_265_beta_0_to_fp16, epsilon = var_6730_to_fp16, gamma = hidden_states_265_gamma_0_to_fp16, x = inputs_201_cast_fp16)[name = tensor("hidden_states_265_cast_fp16")]; + tensor var_6745 = const()[name = tensor("op_6745"), val = tensor([1, 1])]; + tensor var_6747 = const()[name = tensor("op_6747"), val = tensor([1, 1])]; + tensor q_135_pad_type_0 = const()[name = tensor("q_135_pad_type_0"), val = tensor("custom")]; + tensor q_135_pad_0 = const()[name = tensor("q_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887469824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(888698688))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_135_cast_fp16 = conv(dilations = var_6747, groups = var_4971, pad = q_135_pad_0, pad_type = q_135_pad_type_0, strides = var_6745, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_265_cast_fp16)[name = tensor("q_135_cast_fp16")]; + tensor var_6751 = const()[name = tensor("op_6751"), val = tensor([1, 1])]; + tensor var_6753 = const()[name = tensor("op_6753"), val = tensor([1, 1])]; + tensor k_135_pad_type_0 = const()[name = tensor("k_135_pad_type_0"), val = tensor("custom")]; + tensor k_135_pad_0 = const()[name = tensor("k_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(888698880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(890665024))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_135_cast_fp16 = conv(dilations = var_6753, groups = var_4971, pad = k_135_pad_0, pad_type = k_135_pad_type_0, strides = var_6751, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_135_cast_fp16")]; + tensor var_6757 = const()[name = tensor("op_6757"), val = tensor([1, 1])]; + tensor var_6759 = const()[name = tensor("op_6759"), val = tensor([1, 1])]; + tensor v_135_pad_type_0 = const()[name = tensor("v_135_pad_type_0"), val = tensor("custom")]; + tensor v_135_pad_0 = const()[name = tensor("v_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(890665216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(892631360))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_135_cast_fp16 = conv(dilations = var_6759, groups = var_4971, pad = v_135_pad_0, pad_type = v_135_pad_type_0, strides = var_6757, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_135_cast_fp16")]; + tensor var_6763 = const()[name = tensor("op_6763"), val = tensor([1, 20, 64, -1])]; + tensor var_6764_cast_fp16 = reshape(shape = var_6763, x = q_135_cast_fp16)[name = tensor("op_6764_cast_fp16")]; + tensor var_6765 = const()[name = tensor("op_6765"), val = tensor([1, 20, 64, -1])]; + tensor var_6766_cast_fp16 = reshape(shape = var_6765, x = k_135_cast_fp16)[name = tensor("op_6766_cast_fp16")]; + tensor var_6767 = const()[name = tensor("op_6767"), val = tensor([1, 20, 64, -1])]; + tensor var_6768_cast_fp16 = reshape(shape = var_6767, x = v_135_cast_fp16)[name = tensor("op_6768_cast_fp16")]; + tensor attn_weights_269_transpose_x_0 = const()[name = tensor("attn_weights_269_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_269_transpose_y_0 = const()[name = tensor("attn_weights_269_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_269_cast_fp16 = matmul(transpose_x = attn_weights_269_transpose_x_0, transpose_y = attn_weights_269_transpose_y_0, x = var_6764_cast_fp16, y = var_6766_cast_fp16)[name = tensor("attn_weights_269_cast_fp16")]; + tensor attn_weights_271_cast_fp16 = mul(x = attn_weights_269_cast_fp16, y = var_4962_to_fp16)[name = tensor("attn_weights_271_cast_fp16")]; + tensor var_6772_cast_fp16 = softmax(axis = var_4955, x = attn_weights_271_cast_fp16)[name = tensor("op_6772_cast_fp16")]; + tensor attn_135_transpose_x_0 = const()[name = tensor("attn_135_transpose_x_0"), val = tensor(false)]; + tensor attn_135_transpose_y_0 = const()[name = tensor("attn_135_transpose_y_0"), val = tensor(true)]; + tensor attn_135_cast_fp16 = matmul(transpose_x = attn_135_transpose_x_0, transpose_y = attn_135_transpose_y_0, x = var_6768_cast_fp16, y = var_6772_cast_fp16)[name = tensor("attn_135_cast_fp16")]; + tensor var_6776 = const()[name = tensor("op_6776"), val = tensor([1, 1280, 1, -1])]; + tensor input_403_cast_fp16 = reshape(shape = var_6776, x = attn_135_cast_fp16)[name = tensor("input_403_cast_fp16")]; + tensor var_6781 = const()[name = tensor("op_6781"), val = tensor([1, 1])]; + tensor var_6783 = const()[name = tensor("op_6783"), val = tensor([1, 1])]; + tensor var_6785_pad_type_0 = const()[name = tensor("op_6785_pad_type_0"), val = tensor("custom")]; + tensor var_6785_pad_0 = const()[name = tensor("op_6785_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(892631552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893860416))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893860608)))]; + tensor var_6785_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_6783, groups = var_4971, pad = var_6785_pad_0, pad_type = var_6785_pad_type_0, strides = var_6781, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = tensor("op_6785_cast_fp16")]; + tensor inputs_203_cast_fp16 = add(x = var_6785_cast_fp16, y = inputs_201_cast_fp16)[name = tensor("inputs_203_cast_fp16")]; + tensor input_405_axes_0 = const()[name = tensor("input_405_axes_0"), val = tensor([1])]; + tensor input_405_gamma_0_to_fp16 = const()[name = tensor("input_405_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893863232)))]; + tensor input_405_beta_0_to_fp16 = const()[name = tensor("input_405_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893865856)))]; + tensor var_6795_to_fp16 = const()[name = tensor("op_6795_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_405_cast_fp16 = layer_norm(axes = input_405_axes_0, beta = input_405_beta_0_to_fp16, epsilon = var_6795_to_fp16, gamma = input_405_gamma_0_to_fp16, x = inputs_203_cast_fp16)[name = tensor("input_405_cast_fp16")]; + tensor var_6811 = const()[name = tensor("op_6811"), val = tensor([1, 1])]; + tensor var_6813 = const()[name = tensor("op_6813"), val = tensor([1, 1])]; + tensor var_6815_pad_type_0 = const()[name = tensor("op_6815_pad_type_0"), val = tensor("custom")]; + tensor var_6815_pad_0 = const()[name = tensor("op_6815_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893868480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903698944))), name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903699136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903706880))), name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6815_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6813, groups = var_4971, pad = var_6815_pad_0, pad_type = var_6815_pad_type_0, strides = var_6811, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = tensor("op_6815_cast_fp16")]; + tensor var_6816_split_sizes_0 = const()[name = tensor("op_6816_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6816_axis_0 = const()[name = tensor("op_6816_axis_0"), val = tensor(1)]; + tensor var_6816_cast_fp16_0, tensor var_6816_cast_fp16_1 = split(axis = var_6816_axis_0, split_sizes = var_6816_split_sizes_0, x = var_6815_cast_fp16)[name = tensor("op_6816_cast_fp16")]; + tensor var_6818_mode_0 = const()[name = tensor("op_6818_mode_0"), val = tensor("EXACT")]; + tensor var_6818_cast_fp16 = gelu(mode = var_6818_mode_0, x = var_6816_cast_fp16_1)[name = tensor("op_6818_cast_fp16")]; + tensor input_407_cast_fp16 = mul(x = var_6816_cast_fp16_0, y = var_6818_cast_fp16)[name = tensor("input_407_cast_fp16")]; + tensor var_6822 = const()[name = tensor("op_6822"), val = tensor([1, 1])]; + tensor var_6824 = const()[name = tensor("op_6824"), val = tensor([1, 1])]; + tensor var_6826_pad_type_0 = const()[name = tensor("op_6826_pad_type_0"), val = tensor("custom")]; + tensor var_6826_pad_0 = const()[name = tensor("op_6826_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903707072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(908622336))), name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(908622528)))]; + tensor var_6826_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_6824, groups = var_4971, pad = var_6826_pad_0, pad_type = var_6826_pad_type_0, strides = var_6822, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = tensor("op_6826_cast_fp16")]; + tensor hidden_states_269_cast_fp16 = add(x = var_6826_cast_fp16, y = inputs_203_cast_fp16)[name = tensor("hidden_states_269_cast_fp16")]; + tensor var_6828 = const()[name = tensor("op_6828"), val = tensor([1, 1280, 32, 32])]; + tensor input_409_cast_fp16 = reshape(shape = var_6828, x = hidden_states_269_cast_fp16)[name = tensor("input_409_cast_fp16")]; + tensor var_6832 = const()[name = tensor("op_6832"), val = tensor([1, 1])]; + tensor var_6834 = const()[name = tensor("op_6834"), val = tensor([1, 1])]; + tensor hidden_states_271_pad_type_0 = const()[name = tensor("hidden_states_271_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_271_pad_0 = const()[name = tensor("hidden_states_271_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(908625152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(909854016))), 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(909854208)))]; + tensor hidden_states_271_cast_fp16 = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_6834, groups = var_4971, pad = hidden_states_271_pad_0, pad_type = hidden_states_271_pad_type_0, strides = var_6832, weight = mid_block_attentions_0_proj_out_weight_to_fp16_palettized, x = input_409_cast_fp16)[name = tensor("hidden_states_271_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = hidden_states_271_cast_fp16, y = hidden_states_205_cast_fp16)[name = tensor("input_411_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = input_411_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + 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(909856832)))]; + 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(909859456)))]; + 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_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor input_415_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("input_415_cast_fp16")]; + tensor var_6849 = const()[name = tensor("op_6849"), val = tensor([1, 1])]; + tensor var_6851 = const()[name = tensor("op_6851"), val = tensor([1, 1])]; + tensor hidden_states_273_pad_type_0 = const()[name = tensor("hidden_states_273_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_273_pad_0 = const()[name = tensor("hidden_states_273_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(909862080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(920921344))), 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(920921536)))]; + tensor hidden_states_273_cast_fp16 = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_6851, groups = var_4971, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = var_6849, weight = mid_block_resnets_1_conv1_weight_to_fp16_palettized, x = input_415_cast_fp16)[name = tensor("hidden_states_273_cast_fp16")]; + tensor var_6857 = const()[name = tensor("op_6857"), val = tensor([1, 1])]; + tensor var_6859 = const()[name = tensor("op_6859"), 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 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(920924160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(922153024))), 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(922153216)))]; + tensor temb_15_cast_fp16 = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_6859, groups = var_4971, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_6857, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_15_cast_fp16")]; + tensor input_419_cast_fp16 = add(x = hidden_states_273_cast_fp16, y = temb_15_cast_fp16)[name = tensor("input_419_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_419_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + 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(922155840)))]; + 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(922158464)))]; + 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_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_423_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_423_cast_fp16")]; + tensor var_6869 = const()[name = tensor("op_6869"), val = tensor([1, 1])]; + tensor var_6871 = const()[name = tensor("op_6871"), 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([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(922161088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933220352))), 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(933220544)))]; + tensor hidden_states_275_cast_fp16 = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_6871, groups = var_4971, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_6869, weight = mid_block_resnets_1_conv2_weight_to_fp16_palettized, x = input_423_cast_fp16)[name = tensor("hidden_states_275_cast_fp16")]; + tensor sample_cast_fp16 = add(x = input_411_cast_fp16, y = hidden_states_275_cast_fp16)[name = tensor("sample_cast_fp16")]; + tensor hidden_states_277_cast_fp16 = add(x = sample_cast_fp16, y = additional_residual_9)[name = tensor("hidden_states_277_cast_fp16")]; + tensor var_6879 = const()[name = tensor("op_6879"), val = tensor(3)]; + tensor var_6895 = const()[name = tensor("op_6895"), val = tensor(1)]; + tensor input_425_interleave_0 = const()[name = tensor("input_425_interleave_0"), val = tensor(false)]; + tensor input_425_cast_fp16 = concat(axis = var_6895, interleave = input_425_interleave_0, values = (hidden_states_277_cast_fp16, res_hidden_states_1_cast_fp16))[name = tensor("input_425_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 80, 32, 32])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = input_425_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 2560, 32, 32])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_mean_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933223168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933225152))), name = tensor("add_43_mean_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_43_variance_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933225344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933227328))), name = tensor("add_43_variance_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_43_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933227520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933229504))), name = tensor("add_43_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_43_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933229696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933231680))), name = tensor("add_43_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + 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_fp16 = batch_norm(beta = add_43_beta_0_to_fp16_palettized, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16_palettized, mean = add_43_mean_0_to_fp16_palettized, variance = add_43_variance_0_to_fp16_palettized, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor input_429_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("input_429_cast_fp16")]; + tensor var_6924 = const()[name = tensor("op_6924"), val = tensor([1, 1])]; + tensor var_6926 = const()[name = tensor("op_6926"), 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_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933231872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(955350336))), 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(955350528)))]; + tensor hidden_states_279_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_6926, groups = var_6895, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_6924, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = tensor("hidden_states_279_cast_fp16")]; + tensor var_6932 = const()[name = tensor("op_6932"), val = tensor([1, 1])]; + tensor var_6934 = const()[name = tensor("op_6934"), 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 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(955353152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956582016))), 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(956582208)))]; + tensor temb_17_cast_fp16 = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_6934, groups = var_6895, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_6932, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_17_cast_fp16")]; + tensor input_433_cast_fp16 = add(x = hidden_states_279_cast_fp16, y = temb_17_cast_fp16)[name = tensor("input_433_cast_fp16")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_433_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; + tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; + tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; + tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; + tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; + 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(956584832)))]; + 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(956587456)))]; + 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_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; + tensor input_437_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_437_cast_fp16")]; + tensor var_6944 = const()[name = tensor("op_6944"), val = tensor([1, 1])]; + tensor var_6946 = const()[name = tensor("op_6946"), 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_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956590080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(967649344))), 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(967649536)))]; + tensor hidden_states_281_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_6946, groups = var_6895, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_6944, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_437_cast_fp16)[name = tensor("hidden_states_281_cast_fp16")]; + tensor var_6951 = const()[name = tensor("op_6951"), val = tensor([1, 1])]; + tensor var_6953 = const()[name = tensor("op_6953"), 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(967652160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(970109824))), 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(970110016)))]; + tensor x_5_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_6953, groups = var_6895, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_6951, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = tensor("x_5_cast_fp16")]; + tensor hidden_states_283_cast_fp16 = add(x = x_5_cast_fp16, y = hidden_states_281_cast_fp16)[name = tensor("hidden_states_283_cast_fp16")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = hidden_states_283_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; + tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; + tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; + tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; + tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; + 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(970112640)))]; + 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(970115264)))]; + 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_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; + tensor var_6991 = const()[name = tensor("op_6991"), val = tensor([1, 1])]; + tensor var_6993 = const()[name = tensor("op_6993"), 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_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(970117888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971346752))), name = tensor("up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971346944)))]; + tensor hidden_states_285_cast_fp16 = conv(bias = up_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_6993, groups = var_6895, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_6991, weight = up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized, x = add_47_cast_fp16)[name = tensor("hidden_states_285_cast_fp16")]; + tensor var_6998 = const()[name = tensor("op_6998"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_205_cast_fp16 = reshape(shape = var_6998, x = hidden_states_285_cast_fp16)[name = tensor("inputs_205_cast_fp16")]; + tensor hidden_states_287_axes_0 = const()[name = tensor("hidden_states_287_axes_0"), val = tensor([1])]; + tensor hidden_states_287_gamma_0_to_fp16 = const()[name = tensor("hidden_states_287_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971349568)))]; + tensor hidden_states_287_beta_0_to_fp16 = const()[name = tensor("hidden_states_287_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971352192)))]; + tensor var_7014_to_fp16 = const()[name = tensor("op_7014_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_287_cast_fp16 = layer_norm(axes = hidden_states_287_axes_0, beta = hidden_states_287_beta_0_to_fp16, epsilon = var_7014_to_fp16, gamma = hidden_states_287_gamma_0_to_fp16, x = inputs_205_cast_fp16)[name = tensor("hidden_states_287_cast_fp16")]; + tensor var_7029 = const()[name = tensor("op_7029"), val = tensor([1, 1])]; + tensor var_7031 = const()[name = tensor("op_7031"), val = tensor([1, 1])]; + tensor q_137_pad_type_0 = const()[name = tensor("q_137_pad_type_0"), val = tensor("custom")]; + tensor q_137_pad_0 = const()[name = tensor("q_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(971354816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(972583680))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_137_cast_fp16 = conv(dilations = var_7031, groups = var_6895, pad = q_137_pad_0, pad_type = q_137_pad_type_0, strides = var_7029, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("q_137_cast_fp16")]; + tensor var_7035 = const()[name = tensor("op_7035"), val = tensor([1, 1])]; + tensor var_7037 = const()[name = tensor("op_7037"), val = tensor([1, 1])]; + tensor k_137_pad_type_0 = const()[name = tensor("k_137_pad_type_0"), val = tensor("custom")]; + tensor k_137_pad_0 = const()[name = tensor("k_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(972583872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973812736))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_137_cast_fp16 = conv(dilations = var_7037, groups = var_6895, pad = k_137_pad_0, pad_type = k_137_pad_type_0, strides = var_7035, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("k_137_cast_fp16")]; + 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 v_137_pad_type_0 = const()[name = tensor("v_137_pad_type_0"), val = tensor("custom")]; + tensor v_137_pad_0 = const()[name = tensor("v_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(973812928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975041792))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_137_cast_fp16 = conv(dilations = var_7043, groups = var_6895, pad = v_137_pad_0, pad_type = v_137_pad_type_0, strides = var_7041, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("v_137_cast_fp16")]; + tensor var_7047 = const()[name = tensor("op_7047"), val = tensor([1, 20, 64, -1])]; + tensor var_7048_cast_fp16 = reshape(shape = var_7047, x = q_137_cast_fp16)[name = tensor("op_7048_cast_fp16")]; + tensor var_7049 = const()[name = tensor("op_7049"), val = tensor([1, 20, 64, -1])]; + tensor var_7050_cast_fp16 = reshape(shape = var_7049, x = k_137_cast_fp16)[name = tensor("op_7050_cast_fp16")]; + tensor var_7051 = const()[name = tensor("op_7051"), val = tensor([1, 20, 64, -1])]; + tensor var_7052_cast_fp16 = reshape(shape = var_7051, x = v_137_cast_fp16)[name = tensor("op_7052_cast_fp16")]; + tensor attn_weights_273_transpose_x_0 = const()[name = tensor("attn_weights_273_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_273_transpose_y_0 = const()[name = tensor("attn_weights_273_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_7048_cast_fp16, y = var_7050_cast_fp16)[name = tensor("attn_weights_273_cast_fp16")]; + tensor var_6886_to_fp16 = const()[name = tensor("op_6886_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_275_cast_fp16")]; + tensor var_7056_cast_fp16 = softmax(axis = var_6879, x = attn_weights_275_cast_fp16)[name = tensor("op_7056_cast_fp16")]; + tensor attn_137_transpose_x_0 = const()[name = tensor("attn_137_transpose_x_0"), val = tensor(false)]; + tensor attn_137_transpose_y_0 = const()[name = tensor("attn_137_transpose_y_0"), val = tensor(true)]; + tensor attn_137_cast_fp16 = matmul(transpose_x = attn_137_transpose_x_0, transpose_y = attn_137_transpose_y_0, x = var_7052_cast_fp16, y = var_7056_cast_fp16)[name = tensor("attn_137_cast_fp16")]; + tensor var_7060 = const()[name = tensor("op_7060"), val = tensor([1, 1280, 1, -1])]; + tensor input_441_cast_fp16 = reshape(shape = var_7060, x = attn_137_cast_fp16)[name = tensor("input_441_cast_fp16")]; + tensor var_7065 = const()[name = tensor("op_7065"), val = tensor([1, 1])]; + tensor var_7067 = const()[name = tensor("op_7067"), val = tensor([1, 1])]; + tensor var_7069_pad_type_0 = const()[name = tensor("op_7069_pad_type_0"), val = tensor("custom")]; + tensor var_7069_pad_0 = const()[name = tensor("op_7069_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(975041984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976270848))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_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(976271040)))]; + tensor var_7069_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_7067, groups = var_6895, pad = var_7069_pad_0, pad_type = var_7069_pad_type_0, strides = var_7065, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_441_cast_fp16)[name = tensor("op_7069_cast_fp16")]; + tensor inputs_207_cast_fp16 = add(x = var_7069_cast_fp16, y = inputs_205_cast_fp16)[name = tensor("inputs_207_cast_fp16")]; + tensor hidden_states_289_axes_0 = const()[name = tensor("hidden_states_289_axes_0"), val = tensor([1])]; + tensor hidden_states_289_gamma_0_to_fp16 = const()[name = tensor("hidden_states_289_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976273664)))]; + tensor hidden_states_289_beta_0_to_fp16 = const()[name = tensor("hidden_states_289_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976276288)))]; + tensor var_7079_to_fp16 = const()[name = tensor("op_7079_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_289_cast_fp16 = layer_norm(axes = hidden_states_289_axes_0, beta = hidden_states_289_beta_0_to_fp16, epsilon = var_7079_to_fp16, gamma = hidden_states_289_gamma_0_to_fp16, x = inputs_207_cast_fp16)[name = tensor("hidden_states_289_cast_fp16")]; + tensor var_7094 = const()[name = tensor("op_7094"), val = tensor([1, 1])]; + tensor var_7096 = const()[name = tensor("op_7096"), val = tensor([1, 1])]; + tensor q_139_pad_type_0 = const()[name = tensor("q_139_pad_type_0"), val = tensor("custom")]; + tensor q_139_pad_0 = const()[name = tensor("q_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(976278912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(977507776))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_139_cast_fp16 = conv(dilations = var_7096, groups = var_6895, pad = q_139_pad_0, pad_type = q_139_pad_type_0, strides = var_7094, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_289_cast_fp16)[name = tensor("q_139_cast_fp16")]; + tensor var_7100 = const()[name = tensor("op_7100"), val = tensor([1, 1])]; + tensor var_7102 = const()[name = tensor("op_7102"), val = tensor([1, 1])]; + tensor k_139_pad_type_0 = const()[name = tensor("k_139_pad_type_0"), val = tensor("custom")]; + tensor k_139_pad_0 = const()[name = tensor("k_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(977507968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(979474112))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_139_cast_fp16 = conv(dilations = var_7102, groups = var_6895, pad = k_139_pad_0, pad_type = k_139_pad_type_0, strides = var_7100, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_139_cast_fp16")]; + tensor var_7106 = const()[name = tensor("op_7106"), val = tensor([1, 1])]; + tensor var_7108 = const()[name = tensor("op_7108"), val = tensor([1, 1])]; + tensor v_139_pad_type_0 = const()[name = tensor("v_139_pad_type_0"), val = tensor("custom")]; + tensor v_139_pad_0 = const()[name = tensor("v_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(979474304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(981440448))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_139_cast_fp16 = conv(dilations = var_7108, groups = var_6895, pad = v_139_pad_0, pad_type = v_139_pad_type_0, strides = var_7106, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_139_cast_fp16")]; + tensor var_7112 = const()[name = tensor("op_7112"), val = tensor([1, 20, 64, -1])]; + tensor var_7113_cast_fp16 = reshape(shape = var_7112, x = q_139_cast_fp16)[name = tensor("op_7113_cast_fp16")]; + tensor var_7114 = const()[name = tensor("op_7114"), val = tensor([1, 20, 64, -1])]; + tensor var_7115_cast_fp16 = reshape(shape = var_7114, x = k_139_cast_fp16)[name = tensor("op_7115_cast_fp16")]; + tensor var_7116 = const()[name = tensor("op_7116"), val = tensor([1, 20, 64, -1])]; + tensor var_7117_cast_fp16 = reshape(shape = var_7116, x = v_139_cast_fp16)[name = tensor("op_7117_cast_fp16")]; + tensor attn_weights_277_transpose_x_0 = const()[name = tensor("attn_weights_277_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_277_transpose_y_0 = const()[name = tensor("attn_weights_277_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_277_cast_fp16 = matmul(transpose_x = attn_weights_277_transpose_x_0, transpose_y = attn_weights_277_transpose_y_0, x = var_7113_cast_fp16, y = var_7115_cast_fp16)[name = tensor("attn_weights_277_cast_fp16")]; + tensor attn_weights_279_cast_fp16 = mul(x = attn_weights_277_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_279_cast_fp16")]; + tensor var_7121_cast_fp16 = softmax(axis = var_6879, x = attn_weights_279_cast_fp16)[name = tensor("op_7121_cast_fp16")]; + tensor attn_139_transpose_x_0 = const()[name = tensor("attn_139_transpose_x_0"), val = tensor(false)]; + tensor attn_139_transpose_y_0 = const()[name = tensor("attn_139_transpose_y_0"), val = tensor(true)]; + tensor attn_139_cast_fp16 = matmul(transpose_x = attn_139_transpose_x_0, transpose_y = attn_139_transpose_y_0, x = var_7117_cast_fp16, y = var_7121_cast_fp16)[name = tensor("attn_139_cast_fp16")]; + tensor var_7125 = const()[name = tensor("op_7125"), val = tensor([1, 1280, 1, -1])]; + tensor input_443_cast_fp16 = reshape(shape = var_7125, x = attn_139_cast_fp16)[name = tensor("input_443_cast_fp16")]; + tensor var_7130 = const()[name = tensor("op_7130"), val = tensor([1, 1])]; + tensor var_7132 = const()[name = tensor("op_7132"), val = tensor([1, 1])]; + tensor var_7134_pad_type_0 = const()[name = tensor("op_7134_pad_type_0"), val = tensor("custom")]; + tensor var_7134_pad_0 = const()[name = tensor("op_7134_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(981440640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982669504))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_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(982669696)))]; + tensor var_7134_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_7132, groups = var_6895, pad = var_7134_pad_0, pad_type = var_7134_pad_type_0, strides = var_7130, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_443_cast_fp16)[name = tensor("op_7134_cast_fp16")]; + tensor inputs_209_cast_fp16 = add(x = var_7134_cast_fp16, y = inputs_207_cast_fp16)[name = tensor("inputs_209_cast_fp16")]; + tensor input_445_axes_0 = const()[name = tensor("input_445_axes_0"), val = tensor([1])]; + tensor input_445_gamma_0_to_fp16 = const()[name = tensor("input_445_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982672320)))]; + tensor input_445_beta_0_to_fp16 = const()[name = tensor("input_445_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982674944)))]; + tensor var_7144_to_fp16 = const()[name = tensor("op_7144_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_445_cast_fp16 = layer_norm(axes = input_445_axes_0, beta = input_445_beta_0_to_fp16, epsilon = var_7144_to_fp16, gamma = input_445_gamma_0_to_fp16, x = inputs_209_cast_fp16)[name = tensor("input_445_cast_fp16")]; + tensor var_7160 = const()[name = tensor("op_7160"), val = tensor([1, 1])]; + tensor var_7162 = const()[name = tensor("op_7162"), val = tensor([1, 1])]; + tensor var_7164_pad_type_0 = const()[name = tensor("op_7164_pad_type_0"), val = tensor("custom")]; + tensor var_7164_pad_0 = const()[name = tensor("op_7164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(982677568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992508032))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_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(992508224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992515968))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7164_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7162, groups = var_6895, pad = var_7164_pad_0, pad_type = var_7164_pad_type_0, strides = var_7160, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_445_cast_fp16)[name = tensor("op_7164_cast_fp16")]; + tensor var_7165_split_sizes_0 = const()[name = tensor("op_7165_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7165_axis_0 = const()[name = tensor("op_7165_axis_0"), val = tensor(1)]; + tensor var_7165_cast_fp16_0, tensor var_7165_cast_fp16_1 = split(axis = var_7165_axis_0, split_sizes = var_7165_split_sizes_0, x = var_7164_cast_fp16)[name = tensor("op_7165_cast_fp16")]; + tensor var_7167_mode_0 = const()[name = tensor("op_7167_mode_0"), val = tensor("EXACT")]; + tensor var_7167_cast_fp16 = gelu(mode = var_7167_mode_0, x = var_7165_cast_fp16_1)[name = tensor("op_7167_cast_fp16")]; + tensor input_447_cast_fp16 = mul(x = var_7165_cast_fp16_0, y = var_7167_cast_fp16)[name = tensor("input_447_cast_fp16")]; + tensor var_7171 = const()[name = tensor("op_7171"), val = tensor([1, 1])]; + tensor var_7173 = const()[name = tensor("op_7173"), val = tensor([1, 1])]; + tensor var_7175_pad_type_0 = const()[name = tensor("op_7175_pad_type_0"), val = tensor("custom")]; + tensor var_7175_pad_0 = const()[name = tensor("op_7175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(992516160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997431424))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_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(997431616)))]; + tensor var_7175_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7173, groups = var_6895, pad = var_7175_pad_0, pad_type = var_7175_pad_type_0, strides = var_7171, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = tensor("op_7175_cast_fp16")]; + tensor inputs_211_cast_fp16 = add(x = var_7175_cast_fp16, y = inputs_209_cast_fp16)[name = tensor("inputs_211_cast_fp16")]; + tensor hidden_states_293_axes_0 = const()[name = tensor("hidden_states_293_axes_0"), val = tensor([1])]; + tensor hidden_states_293_gamma_0_to_fp16 = const()[name = tensor("hidden_states_293_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997434240)))]; + tensor hidden_states_293_beta_0_to_fp16 = const()[name = tensor("hidden_states_293_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997436864)))]; + tensor var_7191_to_fp16 = const()[name = tensor("op_7191_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_293_cast_fp16 = layer_norm(axes = hidden_states_293_axes_0, beta = hidden_states_293_beta_0_to_fp16, epsilon = var_7191_to_fp16, gamma = hidden_states_293_gamma_0_to_fp16, x = inputs_211_cast_fp16)[name = tensor("hidden_states_293_cast_fp16")]; + tensor var_7206 = const()[name = tensor("op_7206"), val = tensor([1, 1])]; + tensor var_7208 = const()[name = tensor("op_7208"), val = tensor([1, 1])]; + tensor q_141_pad_type_0 = const()[name = tensor("q_141_pad_type_0"), val = tensor("custom")]; + tensor q_141_pad_0 = const()[name = tensor("q_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997439488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(998668352))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_141_cast_fp16 = conv(dilations = var_7208, groups = var_6895, pad = q_141_pad_0, pad_type = q_141_pad_type_0, strides = var_7206, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_293_cast_fp16)[name = tensor("q_141_cast_fp16")]; + tensor var_7212 = const()[name = tensor("op_7212"), val = tensor([1, 1])]; + tensor var_7214 = const()[name = tensor("op_7214"), val = tensor([1, 1])]; + tensor k_141_pad_type_0 = const()[name = tensor("k_141_pad_type_0"), val = tensor("custom")]; + tensor k_141_pad_0 = const()[name = tensor("k_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(998668544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(999897408))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_141_cast_fp16 = conv(dilations = var_7214, groups = var_6895, pad = k_141_pad_0, pad_type = k_141_pad_type_0, strides = var_7212, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_293_cast_fp16)[name = tensor("k_141_cast_fp16")]; + tensor var_7218 = const()[name = tensor("op_7218"), val = tensor([1, 1])]; + tensor var_7220 = const()[name = tensor("op_7220"), val = tensor([1, 1])]; + tensor v_141_pad_type_0 = const()[name = tensor("v_141_pad_type_0"), val = tensor("custom")]; + tensor v_141_pad_0 = const()[name = tensor("v_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(999897600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1001126464))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_141_cast_fp16 = conv(dilations = var_7220, groups = var_6895, pad = v_141_pad_0, pad_type = v_141_pad_type_0, strides = var_7218, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_293_cast_fp16)[name = tensor("v_141_cast_fp16")]; + tensor var_7224 = const()[name = tensor("op_7224"), val = tensor([1, 20, 64, -1])]; + tensor var_7225_cast_fp16 = reshape(shape = var_7224, x = q_141_cast_fp16)[name = tensor("op_7225_cast_fp16")]; + tensor var_7226 = const()[name = tensor("op_7226"), val = tensor([1, 20, 64, -1])]; + tensor var_7227_cast_fp16 = reshape(shape = var_7226, x = k_141_cast_fp16)[name = tensor("op_7227_cast_fp16")]; + tensor var_7228 = const()[name = tensor("op_7228"), val = tensor([1, 20, 64, -1])]; + tensor var_7229_cast_fp16 = reshape(shape = var_7228, x = v_141_cast_fp16)[name = tensor("op_7229_cast_fp16")]; + tensor attn_weights_281_transpose_x_0 = const()[name = tensor("attn_weights_281_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_281_transpose_y_0 = const()[name = tensor("attn_weights_281_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_7225_cast_fp16, y = var_7227_cast_fp16)[name = tensor("attn_weights_281_cast_fp16")]; + tensor attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_283_cast_fp16")]; + tensor var_7233_cast_fp16 = softmax(axis = var_6879, x = attn_weights_283_cast_fp16)[name = tensor("op_7233_cast_fp16")]; + tensor attn_141_transpose_x_0 = const()[name = tensor("attn_141_transpose_x_0"), val = tensor(false)]; + tensor attn_141_transpose_y_0 = const()[name = tensor("attn_141_transpose_y_0"), val = tensor(true)]; + tensor attn_141_cast_fp16 = matmul(transpose_x = attn_141_transpose_x_0, transpose_y = attn_141_transpose_y_0, x = var_7229_cast_fp16, y = var_7233_cast_fp16)[name = tensor("attn_141_cast_fp16")]; + tensor var_7237 = const()[name = tensor("op_7237"), val = tensor([1, 1280, 1, -1])]; + tensor input_449_cast_fp16 = reshape(shape = var_7237, x = attn_141_cast_fp16)[name = tensor("input_449_cast_fp16")]; + tensor var_7242 = const()[name = tensor("op_7242"), val = tensor([1, 1])]; + tensor var_7244 = const()[name = tensor("op_7244"), val = tensor([1, 1])]; + tensor var_7246_pad_type_0 = const()[name = tensor("op_7246_pad_type_0"), val = tensor("custom")]; + tensor var_7246_pad_0 = const()[name = tensor("op_7246_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1001126656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002355520))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002355712)))]; + tensor var_7246_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_7244, groups = var_6895, pad = var_7246_pad_0, pad_type = var_7246_pad_type_0, strides = var_7242, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_449_cast_fp16)[name = tensor("op_7246_cast_fp16")]; + tensor inputs_213_cast_fp16 = add(x = var_7246_cast_fp16, y = inputs_211_cast_fp16)[name = tensor("inputs_213_cast_fp16")]; + tensor hidden_states_295_axes_0 = const()[name = tensor("hidden_states_295_axes_0"), val = tensor([1])]; + tensor hidden_states_295_gamma_0_to_fp16 = const()[name = tensor("hidden_states_295_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002358336)))]; + tensor hidden_states_295_beta_0_to_fp16 = const()[name = tensor("hidden_states_295_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002360960)))]; + tensor var_7256_to_fp16 = const()[name = tensor("op_7256_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_295_cast_fp16 = layer_norm(axes = hidden_states_295_axes_0, beta = hidden_states_295_beta_0_to_fp16, epsilon = var_7256_to_fp16, gamma = hidden_states_295_gamma_0_to_fp16, x = inputs_213_cast_fp16)[name = tensor("hidden_states_295_cast_fp16")]; + tensor var_7271 = const()[name = tensor("op_7271"), val = tensor([1, 1])]; + tensor var_7273 = const()[name = tensor("op_7273"), val = tensor([1, 1])]; + tensor q_143_pad_type_0 = const()[name = tensor("q_143_pad_type_0"), val = tensor("custom")]; + tensor q_143_pad_0 = const()[name = tensor("q_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002363584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1003592448))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_143_cast_fp16 = conv(dilations = var_7273, groups = var_6895, pad = q_143_pad_0, pad_type = q_143_pad_type_0, strides = var_7271, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_295_cast_fp16)[name = tensor("q_143_cast_fp16")]; + tensor var_7277 = const()[name = tensor("op_7277"), val = tensor([1, 1])]; + tensor var_7279 = const()[name = tensor("op_7279"), val = tensor([1, 1])]; + tensor k_143_pad_type_0 = const()[name = tensor("k_143_pad_type_0"), val = tensor("custom")]; + tensor k_143_pad_0 = const()[name = tensor("k_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1003592640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005558784))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_143_cast_fp16 = conv(dilations = var_7279, groups = var_6895, pad = k_143_pad_0, pad_type = k_143_pad_type_0, strides = var_7277, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_143_cast_fp16")]; + tensor var_7283 = const()[name = tensor("op_7283"), val = tensor([1, 1])]; + tensor var_7285 = const()[name = tensor("op_7285"), val = tensor([1, 1])]; + tensor v_143_pad_type_0 = const()[name = tensor("v_143_pad_type_0"), val = tensor("custom")]; + tensor v_143_pad_0 = const()[name = tensor("v_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005558976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1007525120))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_143_cast_fp16 = conv(dilations = var_7285, groups = var_6895, pad = v_143_pad_0, pad_type = v_143_pad_type_0, strides = var_7283, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_143_cast_fp16")]; + tensor var_7289 = const()[name = tensor("op_7289"), val = tensor([1, 20, 64, -1])]; + tensor var_7290_cast_fp16 = reshape(shape = var_7289, x = q_143_cast_fp16)[name = tensor("op_7290_cast_fp16")]; + tensor var_7291 = const()[name = tensor("op_7291"), val = tensor([1, 20, 64, -1])]; + tensor var_7292_cast_fp16 = reshape(shape = var_7291, x = k_143_cast_fp16)[name = tensor("op_7292_cast_fp16")]; + tensor var_7293 = const()[name = tensor("op_7293"), val = tensor([1, 20, 64, -1])]; + tensor var_7294_cast_fp16 = reshape(shape = var_7293, x = v_143_cast_fp16)[name = tensor("op_7294_cast_fp16")]; + tensor attn_weights_285_transpose_x_0 = const()[name = tensor("attn_weights_285_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_285_transpose_y_0 = const()[name = tensor("attn_weights_285_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_285_cast_fp16 = matmul(transpose_x = attn_weights_285_transpose_x_0, transpose_y = attn_weights_285_transpose_y_0, x = var_7290_cast_fp16, y = var_7292_cast_fp16)[name = tensor("attn_weights_285_cast_fp16")]; + tensor attn_weights_287_cast_fp16 = mul(x = attn_weights_285_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_287_cast_fp16")]; + tensor var_7298_cast_fp16 = softmax(axis = var_6879, x = attn_weights_287_cast_fp16)[name = tensor("op_7298_cast_fp16")]; + tensor attn_143_transpose_x_0 = const()[name = tensor("attn_143_transpose_x_0"), val = tensor(false)]; + tensor attn_143_transpose_y_0 = const()[name = tensor("attn_143_transpose_y_0"), val = tensor(true)]; + tensor attn_143_cast_fp16 = matmul(transpose_x = attn_143_transpose_x_0, transpose_y = attn_143_transpose_y_0, x = var_7294_cast_fp16, y = var_7298_cast_fp16)[name = tensor("attn_143_cast_fp16")]; + tensor var_7302 = const()[name = tensor("op_7302"), val = tensor([1, 1280, 1, -1])]; + tensor input_451_cast_fp16 = reshape(shape = var_7302, x = attn_143_cast_fp16)[name = tensor("input_451_cast_fp16")]; + tensor var_7307 = const()[name = tensor("op_7307"), val = tensor([1, 1])]; + tensor var_7309 = const()[name = tensor("op_7309"), val = tensor([1, 1])]; + tensor var_7311_pad_type_0 = const()[name = tensor("op_7311_pad_type_0"), val = tensor("custom")]; + tensor var_7311_pad_0 = const()[name = tensor("op_7311_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1007525312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008754176))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008754368)))]; + tensor var_7311_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_7309, groups = var_6895, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7307, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_451_cast_fp16)[name = tensor("op_7311_cast_fp16")]; + tensor inputs_215_cast_fp16 = add(x = var_7311_cast_fp16, y = inputs_213_cast_fp16)[name = tensor("inputs_215_cast_fp16")]; + tensor input_453_axes_0 = const()[name = tensor("input_453_axes_0"), val = tensor([1])]; + tensor input_453_gamma_0_to_fp16 = const()[name = tensor("input_453_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008756992)))]; + tensor input_453_beta_0_to_fp16 = const()[name = tensor("input_453_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008759616)))]; + tensor var_7321_to_fp16 = const()[name = tensor("op_7321_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_453_cast_fp16 = layer_norm(axes = input_453_axes_0, beta = input_453_beta_0_to_fp16, epsilon = var_7321_to_fp16, gamma = input_453_gamma_0_to_fp16, x = inputs_215_cast_fp16)[name = tensor("input_453_cast_fp16")]; + tensor var_7337 = const()[name = tensor("op_7337"), val = tensor([1, 1])]; + tensor var_7339 = const()[name = tensor("op_7339"), val = tensor([1, 1])]; + tensor var_7341_pad_type_0 = const()[name = tensor("op_7341_pad_type_0"), val = tensor("custom")]; + tensor var_7341_pad_0 = const()[name = tensor("op_7341_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008762240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018592704))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018592896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018600640))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7341_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7339, groups = var_6895, pad = var_7341_pad_0, pad_type = var_7341_pad_type_0, strides = var_7337, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_453_cast_fp16)[name = tensor("op_7341_cast_fp16")]; + tensor var_7342_split_sizes_0 = const()[name = tensor("op_7342_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7342_axis_0 = const()[name = tensor("op_7342_axis_0"), val = tensor(1)]; + tensor var_7342_cast_fp16_0, tensor var_7342_cast_fp16_1 = split(axis = var_7342_axis_0, split_sizes = var_7342_split_sizes_0, x = var_7341_cast_fp16)[name = tensor("op_7342_cast_fp16")]; + tensor var_7344_mode_0 = const()[name = tensor("op_7344_mode_0"), val = tensor("EXACT")]; + tensor var_7344_cast_fp16 = gelu(mode = var_7344_mode_0, x = var_7342_cast_fp16_1)[name = tensor("op_7344_cast_fp16")]; + tensor input_455_cast_fp16 = mul(x = var_7342_cast_fp16_0, y = var_7344_cast_fp16)[name = tensor("input_455_cast_fp16")]; + tensor var_7348 = const()[name = tensor("op_7348"), val = tensor([1, 1])]; + tensor var_7350 = const()[name = tensor("op_7350"), val = tensor([1, 1])]; + tensor var_7352_pad_type_0 = const()[name = tensor("op_7352_pad_type_0"), val = tensor("custom")]; + tensor var_7352_pad_0 = const()[name = tensor("op_7352_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018600832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023516096))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023516288)))]; + tensor var_7352_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_7350, groups = var_6895, pad = var_7352_pad_0, pad_type = var_7352_pad_type_0, strides = var_7348, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = tensor("op_7352_cast_fp16")]; + tensor inputs_217_cast_fp16 = add(x = var_7352_cast_fp16, y = inputs_215_cast_fp16)[name = tensor("inputs_217_cast_fp16")]; + tensor hidden_states_299_axes_0 = const()[name = tensor("hidden_states_299_axes_0"), val = tensor([1])]; + tensor hidden_states_299_gamma_0_to_fp16 = const()[name = tensor("hidden_states_299_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023518912)))]; + tensor hidden_states_299_beta_0_to_fp16 = const()[name = tensor("hidden_states_299_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023521536)))]; + tensor var_7368_to_fp16 = const()[name = tensor("op_7368_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_299_cast_fp16 = layer_norm(axes = hidden_states_299_axes_0, beta = hidden_states_299_beta_0_to_fp16, epsilon = var_7368_to_fp16, gamma = hidden_states_299_gamma_0_to_fp16, x = inputs_217_cast_fp16)[name = tensor("hidden_states_299_cast_fp16")]; + tensor var_7383 = const()[name = tensor("op_7383"), val = tensor([1, 1])]; + tensor var_7385 = const()[name = tensor("op_7385"), val = tensor([1, 1])]; + tensor q_145_pad_type_0 = const()[name = tensor("q_145_pad_type_0"), val = tensor("custom")]; + tensor q_145_pad_0 = const()[name = tensor("q_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023524160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1024753024))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_145_cast_fp16 = conv(dilations = var_7385, groups = var_6895, pad = q_145_pad_0, pad_type = q_145_pad_type_0, strides = var_7383, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_299_cast_fp16)[name = tensor("q_145_cast_fp16")]; + tensor var_7389 = const()[name = tensor("op_7389"), val = tensor([1, 1])]; + tensor var_7391 = const()[name = tensor("op_7391"), val = tensor([1, 1])]; + tensor k_145_pad_type_0 = const()[name = tensor("k_145_pad_type_0"), val = tensor("custom")]; + tensor k_145_pad_0 = const()[name = tensor("k_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1024753216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025982080))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_145_cast_fp16 = conv(dilations = var_7391, groups = var_6895, pad = k_145_pad_0, pad_type = k_145_pad_type_0, strides = var_7389, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_299_cast_fp16)[name = tensor("k_145_cast_fp16")]; + tensor var_7395 = const()[name = tensor("op_7395"), val = tensor([1, 1])]; + tensor var_7397 = const()[name = tensor("op_7397"), val = tensor([1, 1])]; + tensor v_145_pad_type_0 = const()[name = tensor("v_145_pad_type_0"), val = tensor("custom")]; + tensor v_145_pad_0 = const()[name = tensor("v_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025982272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027211136))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_145_cast_fp16 = conv(dilations = var_7397, groups = var_6895, pad = v_145_pad_0, pad_type = v_145_pad_type_0, strides = var_7395, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_299_cast_fp16)[name = tensor("v_145_cast_fp16")]; + tensor var_7401 = const()[name = tensor("op_7401"), val = tensor([1, 20, 64, -1])]; + tensor var_7402_cast_fp16 = reshape(shape = var_7401, x = q_145_cast_fp16)[name = tensor("op_7402_cast_fp16")]; + tensor var_7403 = const()[name = tensor("op_7403"), val = tensor([1, 20, 64, -1])]; + tensor var_7404_cast_fp16 = reshape(shape = var_7403, x = k_145_cast_fp16)[name = tensor("op_7404_cast_fp16")]; + tensor var_7405 = const()[name = tensor("op_7405"), val = tensor([1, 20, 64, -1])]; + tensor var_7406_cast_fp16 = reshape(shape = var_7405, x = v_145_cast_fp16)[name = tensor("op_7406_cast_fp16")]; + tensor attn_weights_289_transpose_x_0 = const()[name = tensor("attn_weights_289_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_289_transpose_y_0 = const()[name = tensor("attn_weights_289_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_7402_cast_fp16, y = var_7404_cast_fp16)[name = tensor("attn_weights_289_cast_fp16")]; + tensor attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_291_cast_fp16")]; + tensor var_7410_cast_fp16 = softmax(axis = var_6879, x = attn_weights_291_cast_fp16)[name = tensor("op_7410_cast_fp16")]; + tensor attn_145_transpose_x_0 = const()[name = tensor("attn_145_transpose_x_0"), val = tensor(false)]; + tensor attn_145_transpose_y_0 = const()[name = tensor("attn_145_transpose_y_0"), val = tensor(true)]; + tensor attn_145_cast_fp16 = matmul(transpose_x = attn_145_transpose_x_0, transpose_y = attn_145_transpose_y_0, x = var_7406_cast_fp16, y = var_7410_cast_fp16)[name = tensor("attn_145_cast_fp16")]; + tensor var_7414 = const()[name = tensor("op_7414"), val = tensor([1, 1280, 1, -1])]; + tensor input_457_cast_fp16 = reshape(shape = var_7414, x = attn_145_cast_fp16)[name = tensor("input_457_cast_fp16")]; + tensor var_7419 = const()[name = tensor("op_7419"), val = tensor([1, 1])]; + tensor var_7421 = const()[name = tensor("op_7421"), val = tensor([1, 1])]; + tensor var_7423_pad_type_0 = const()[name = tensor("op_7423_pad_type_0"), val = tensor("custom")]; + tensor var_7423_pad_0 = const()[name = tensor("op_7423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027211328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028440192))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028440384)))]; + tensor var_7423_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_7421, groups = var_6895, pad = var_7423_pad_0, pad_type = var_7423_pad_type_0, strides = var_7419, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_457_cast_fp16)[name = tensor("op_7423_cast_fp16")]; + tensor inputs_219_cast_fp16 = add(x = var_7423_cast_fp16, y = inputs_217_cast_fp16)[name = tensor("inputs_219_cast_fp16")]; + tensor hidden_states_301_axes_0 = const()[name = tensor("hidden_states_301_axes_0"), val = tensor([1])]; + tensor hidden_states_301_gamma_0_to_fp16 = const()[name = tensor("hidden_states_301_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028443008)))]; + tensor hidden_states_301_beta_0_to_fp16 = const()[name = tensor("hidden_states_301_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028445632)))]; + tensor var_7433_to_fp16 = const()[name = tensor("op_7433_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_301_cast_fp16 = layer_norm(axes = hidden_states_301_axes_0, beta = hidden_states_301_beta_0_to_fp16, epsilon = var_7433_to_fp16, gamma = hidden_states_301_gamma_0_to_fp16, x = inputs_219_cast_fp16)[name = tensor("hidden_states_301_cast_fp16")]; + tensor var_7448 = const()[name = tensor("op_7448"), val = tensor([1, 1])]; + tensor var_7450 = const()[name = tensor("op_7450"), val = tensor([1, 1])]; + tensor q_147_pad_type_0 = const()[name = tensor("q_147_pad_type_0"), val = tensor("custom")]; + tensor q_147_pad_0 = const()[name = tensor("q_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028448256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1029677120))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_147_cast_fp16 = conv(dilations = var_7450, groups = var_6895, pad = q_147_pad_0, pad_type = q_147_pad_type_0, strides = var_7448, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_301_cast_fp16)[name = tensor("q_147_cast_fp16")]; + tensor var_7454 = const()[name = tensor("op_7454"), val = tensor([1, 1])]; + tensor var_7456 = const()[name = tensor("op_7456"), val = tensor([1, 1])]; + tensor k_147_pad_type_0 = const()[name = tensor("k_147_pad_type_0"), val = tensor("custom")]; + tensor k_147_pad_0 = const()[name = tensor("k_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1029677312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031643456))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_147_cast_fp16 = conv(dilations = var_7456, groups = var_6895, pad = k_147_pad_0, pad_type = k_147_pad_type_0, strides = var_7454, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_147_cast_fp16")]; + tensor var_7460 = const()[name = tensor("op_7460"), val = tensor([1, 1])]; + tensor var_7462 = const()[name = tensor("op_7462"), val = tensor([1, 1])]; + tensor v_147_pad_type_0 = const()[name = tensor("v_147_pad_type_0"), val = tensor("custom")]; + tensor v_147_pad_0 = const()[name = tensor("v_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031643648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1033609792))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_147_cast_fp16 = conv(dilations = var_7462, groups = var_6895, pad = v_147_pad_0, pad_type = v_147_pad_type_0, strides = var_7460, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_147_cast_fp16")]; + tensor var_7466 = const()[name = tensor("op_7466"), val = tensor([1, 20, 64, -1])]; + tensor var_7467_cast_fp16 = reshape(shape = var_7466, x = q_147_cast_fp16)[name = tensor("op_7467_cast_fp16")]; + tensor var_7468 = const()[name = tensor("op_7468"), val = tensor([1, 20, 64, -1])]; + tensor var_7469_cast_fp16 = reshape(shape = var_7468, x = k_147_cast_fp16)[name = tensor("op_7469_cast_fp16")]; + tensor var_7470 = const()[name = tensor("op_7470"), val = tensor([1, 20, 64, -1])]; + tensor var_7471_cast_fp16 = reshape(shape = var_7470, x = v_147_cast_fp16)[name = tensor("op_7471_cast_fp16")]; + tensor attn_weights_293_transpose_x_0 = const()[name = tensor("attn_weights_293_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_293_transpose_y_0 = const()[name = tensor("attn_weights_293_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_293_cast_fp16 = matmul(transpose_x = attn_weights_293_transpose_x_0, transpose_y = attn_weights_293_transpose_y_0, x = var_7467_cast_fp16, y = var_7469_cast_fp16)[name = tensor("attn_weights_293_cast_fp16")]; + tensor attn_weights_295_cast_fp16 = mul(x = attn_weights_293_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_295_cast_fp16")]; + tensor var_7475_cast_fp16 = softmax(axis = var_6879, x = attn_weights_295_cast_fp16)[name = tensor("op_7475_cast_fp16")]; + tensor attn_147_transpose_x_0 = const()[name = tensor("attn_147_transpose_x_0"), val = tensor(false)]; + tensor attn_147_transpose_y_0 = const()[name = tensor("attn_147_transpose_y_0"), val = tensor(true)]; + tensor attn_147_cast_fp16 = matmul(transpose_x = attn_147_transpose_x_0, transpose_y = attn_147_transpose_y_0, x = var_7471_cast_fp16, y = var_7475_cast_fp16)[name = tensor("attn_147_cast_fp16")]; + tensor var_7479 = const()[name = tensor("op_7479"), val = tensor([1, 1280, 1, -1])]; + tensor input_459_cast_fp16 = reshape(shape = var_7479, x = attn_147_cast_fp16)[name = tensor("input_459_cast_fp16")]; + tensor var_7484 = const()[name = tensor("op_7484"), val = tensor([1, 1])]; + tensor var_7486 = const()[name = tensor("op_7486"), val = tensor([1, 1])]; + tensor var_7488_pad_type_0 = const()[name = tensor("op_7488_pad_type_0"), val = tensor("custom")]; + tensor var_7488_pad_0 = const()[name = tensor("op_7488_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1033609984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034838848))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034839040)))]; + tensor var_7488_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_7486, groups = var_6895, pad = var_7488_pad_0, pad_type = var_7488_pad_type_0, strides = var_7484, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = tensor("op_7488_cast_fp16")]; + tensor inputs_221_cast_fp16 = add(x = var_7488_cast_fp16, y = inputs_219_cast_fp16)[name = tensor("inputs_221_cast_fp16")]; + tensor input_461_axes_0 = const()[name = tensor("input_461_axes_0"), val = tensor([1])]; + tensor input_461_gamma_0_to_fp16 = const()[name = tensor("input_461_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034841664)))]; + tensor input_461_beta_0_to_fp16 = const()[name = tensor("input_461_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034844288)))]; + tensor var_7498_to_fp16 = const()[name = tensor("op_7498_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_461_cast_fp16 = layer_norm(axes = input_461_axes_0, beta = input_461_beta_0_to_fp16, epsilon = var_7498_to_fp16, gamma = input_461_gamma_0_to_fp16, x = inputs_221_cast_fp16)[name = tensor("input_461_cast_fp16")]; + tensor var_7514 = const()[name = tensor("op_7514"), val = tensor([1, 1])]; + tensor var_7516 = const()[name = tensor("op_7516"), val = tensor([1, 1])]; + tensor var_7518_pad_type_0 = const()[name = tensor("op_7518_pad_type_0"), val = tensor("custom")]; + tensor var_7518_pad_0 = const()[name = tensor("op_7518_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034846912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044677376))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044677568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044685312))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7518_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7516, groups = var_6895, pad = var_7518_pad_0, pad_type = var_7518_pad_type_0, strides = var_7514, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_461_cast_fp16)[name = tensor("op_7518_cast_fp16")]; + tensor var_7519_split_sizes_0 = const()[name = tensor("op_7519_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7519_axis_0 = const()[name = tensor("op_7519_axis_0"), val = tensor(1)]; + tensor var_7519_cast_fp16_0, tensor var_7519_cast_fp16_1 = split(axis = var_7519_axis_0, split_sizes = var_7519_split_sizes_0, x = var_7518_cast_fp16)[name = tensor("op_7519_cast_fp16")]; + tensor var_7521_mode_0 = const()[name = tensor("op_7521_mode_0"), val = tensor("EXACT")]; + tensor var_7521_cast_fp16 = gelu(mode = var_7521_mode_0, x = var_7519_cast_fp16_1)[name = tensor("op_7521_cast_fp16")]; + tensor input_463_cast_fp16 = mul(x = var_7519_cast_fp16_0, y = var_7521_cast_fp16)[name = tensor("input_463_cast_fp16")]; + tensor var_7525 = const()[name = tensor("op_7525"), val = tensor([1, 1])]; + tensor var_7527 = const()[name = tensor("op_7527"), val = tensor([1, 1])]; + tensor var_7529_pad_type_0 = const()[name = tensor("op_7529_pad_type_0"), val = tensor("custom")]; + tensor var_7529_pad_0 = const()[name = tensor("op_7529_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044685504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049600768))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049600960)))]; + tensor var_7529_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_7527, groups = var_6895, pad = var_7529_pad_0, pad_type = var_7529_pad_type_0, strides = var_7525, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_463_cast_fp16)[name = tensor("op_7529_cast_fp16")]; + tensor inputs_223_cast_fp16 = add(x = var_7529_cast_fp16, y = inputs_221_cast_fp16)[name = tensor("inputs_223_cast_fp16")]; + tensor hidden_states_305_axes_0 = const()[name = tensor("hidden_states_305_axes_0"), val = tensor([1])]; + tensor hidden_states_305_gamma_0_to_fp16 = const()[name = tensor("hidden_states_305_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049603584)))]; + tensor hidden_states_305_beta_0_to_fp16 = const()[name = tensor("hidden_states_305_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049606208)))]; + tensor var_7545_to_fp16 = const()[name = tensor("op_7545_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_305_cast_fp16 = layer_norm(axes = hidden_states_305_axes_0, beta = hidden_states_305_beta_0_to_fp16, epsilon = var_7545_to_fp16, gamma = hidden_states_305_gamma_0_to_fp16, x = inputs_223_cast_fp16)[name = tensor("hidden_states_305_cast_fp16")]; + tensor var_7560 = const()[name = tensor("op_7560"), val = tensor([1, 1])]; + tensor var_7562 = const()[name = tensor("op_7562"), val = tensor([1, 1])]; + tensor q_149_pad_type_0 = const()[name = tensor("q_149_pad_type_0"), val = tensor("custom")]; + tensor q_149_pad_0 = const()[name = tensor("q_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049608832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1050837696))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_149_cast_fp16 = conv(dilations = var_7562, groups = var_6895, pad = q_149_pad_0, pad_type = q_149_pad_type_0, strides = var_7560, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_305_cast_fp16)[name = tensor("q_149_cast_fp16")]; + tensor var_7566 = const()[name = tensor("op_7566"), val = tensor([1, 1])]; + tensor var_7568 = const()[name = tensor("op_7568"), val = tensor([1, 1])]; + tensor k_149_pad_type_0 = const()[name = tensor("k_149_pad_type_0"), val = tensor("custom")]; + tensor k_149_pad_0 = const()[name = tensor("k_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1050837888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1052066752))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_149_cast_fp16 = conv(dilations = var_7568, groups = var_6895, pad = k_149_pad_0, pad_type = k_149_pad_type_0, strides = var_7566, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_305_cast_fp16)[name = tensor("k_149_cast_fp16")]; + tensor var_7572 = const()[name = tensor("op_7572"), val = tensor([1, 1])]; + tensor var_7574 = const()[name = tensor("op_7574"), val = tensor([1, 1])]; + tensor v_149_pad_type_0 = const()[name = tensor("v_149_pad_type_0"), val = tensor("custom")]; + tensor v_149_pad_0 = const()[name = tensor("v_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1052066944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1053295808))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_149_cast_fp16 = conv(dilations = var_7574, groups = var_6895, pad = v_149_pad_0, pad_type = v_149_pad_type_0, strides = var_7572, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_305_cast_fp16)[name = tensor("v_149_cast_fp16")]; + tensor var_7578 = const()[name = tensor("op_7578"), val = tensor([1, 20, 64, -1])]; + tensor var_7579_cast_fp16 = reshape(shape = var_7578, x = q_149_cast_fp16)[name = tensor("op_7579_cast_fp16")]; + tensor var_7580 = const()[name = tensor("op_7580"), val = tensor([1, 20, 64, -1])]; + tensor var_7581_cast_fp16 = reshape(shape = var_7580, x = k_149_cast_fp16)[name = tensor("op_7581_cast_fp16")]; + tensor var_7582 = const()[name = tensor("op_7582"), val = tensor([1, 20, 64, -1])]; + tensor var_7583_cast_fp16 = reshape(shape = var_7582, x = v_149_cast_fp16)[name = tensor("op_7583_cast_fp16")]; + tensor attn_weights_297_transpose_x_0 = const()[name = tensor("attn_weights_297_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_297_transpose_y_0 = const()[name = tensor("attn_weights_297_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_7579_cast_fp16, y = var_7581_cast_fp16)[name = tensor("attn_weights_297_cast_fp16")]; + tensor attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_299_cast_fp16")]; + tensor var_7587_cast_fp16 = softmax(axis = var_6879, x = attn_weights_299_cast_fp16)[name = tensor("op_7587_cast_fp16")]; + tensor attn_149_transpose_x_0 = const()[name = tensor("attn_149_transpose_x_0"), val = tensor(false)]; + tensor attn_149_transpose_y_0 = const()[name = tensor("attn_149_transpose_y_0"), val = tensor(true)]; + tensor attn_149_cast_fp16 = matmul(transpose_x = attn_149_transpose_x_0, transpose_y = attn_149_transpose_y_0, x = var_7583_cast_fp16, y = var_7587_cast_fp16)[name = tensor("attn_149_cast_fp16")]; + tensor var_7591 = const()[name = tensor("op_7591"), val = tensor([1, 1280, 1, -1])]; + tensor input_465_cast_fp16 = reshape(shape = var_7591, x = attn_149_cast_fp16)[name = tensor("input_465_cast_fp16")]; + tensor var_7596 = const()[name = tensor("op_7596"), val = tensor([1, 1])]; + tensor var_7598 = const()[name = tensor("op_7598"), val = tensor([1, 1])]; + tensor var_7600_pad_type_0 = const()[name = tensor("op_7600_pad_type_0"), val = tensor("custom")]; + tensor var_7600_pad_0 = const()[name = tensor("op_7600_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1053296000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054524864))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054525056)))]; + tensor var_7600_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_7598, groups = var_6895, pad = var_7600_pad_0, pad_type = var_7600_pad_type_0, strides = var_7596, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_465_cast_fp16)[name = tensor("op_7600_cast_fp16")]; + tensor inputs_225_cast_fp16 = add(x = var_7600_cast_fp16, y = inputs_223_cast_fp16)[name = tensor("inputs_225_cast_fp16")]; + tensor hidden_states_307_axes_0 = const()[name = tensor("hidden_states_307_axes_0"), val = tensor([1])]; + tensor hidden_states_307_gamma_0_to_fp16 = const()[name = tensor("hidden_states_307_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054527680)))]; + tensor hidden_states_307_beta_0_to_fp16 = const()[name = tensor("hidden_states_307_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054530304)))]; + tensor var_7610_to_fp16 = const()[name = tensor("op_7610_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_307_cast_fp16 = layer_norm(axes = hidden_states_307_axes_0, beta = hidden_states_307_beta_0_to_fp16, epsilon = var_7610_to_fp16, gamma = hidden_states_307_gamma_0_to_fp16, x = inputs_225_cast_fp16)[name = tensor("hidden_states_307_cast_fp16")]; + tensor var_7625 = const()[name = tensor("op_7625"), val = tensor([1, 1])]; + tensor var_7627 = const()[name = tensor("op_7627"), val = tensor([1, 1])]; + tensor q_151_pad_type_0 = const()[name = tensor("q_151_pad_type_0"), val = tensor("custom")]; + tensor q_151_pad_0 = const()[name = tensor("q_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054532928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1055761792))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_151_cast_fp16 = conv(dilations = var_7627, groups = var_6895, pad = q_151_pad_0, pad_type = q_151_pad_type_0, strides = var_7625, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_307_cast_fp16)[name = tensor("q_151_cast_fp16")]; + tensor var_7631 = const()[name = tensor("op_7631"), val = tensor([1, 1])]; + tensor var_7633 = const()[name = tensor("op_7633"), val = tensor([1, 1])]; + tensor k_151_pad_type_0 = const()[name = tensor("k_151_pad_type_0"), val = tensor("custom")]; + tensor k_151_pad_0 = const()[name = tensor("k_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1055761984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057728128))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_151_cast_fp16 = conv(dilations = var_7633, groups = var_6895, pad = k_151_pad_0, pad_type = k_151_pad_type_0, strides = var_7631, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_151_cast_fp16")]; + tensor var_7637 = const()[name = tensor("op_7637"), val = tensor([1, 1])]; + tensor var_7639 = const()[name = tensor("op_7639"), val = tensor([1, 1])]; + tensor v_151_pad_type_0 = const()[name = tensor("v_151_pad_type_0"), val = tensor("custom")]; + tensor v_151_pad_0 = const()[name = tensor("v_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057728320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1059694464))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_151_cast_fp16 = conv(dilations = var_7639, groups = var_6895, pad = v_151_pad_0, pad_type = v_151_pad_type_0, strides = var_7637, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_151_cast_fp16")]; + tensor var_7643 = const()[name = tensor("op_7643"), val = tensor([1, 20, 64, -1])]; + tensor var_7644_cast_fp16 = reshape(shape = var_7643, x = q_151_cast_fp16)[name = tensor("op_7644_cast_fp16")]; + tensor var_7645 = const()[name = tensor("op_7645"), val = tensor([1, 20, 64, -1])]; + tensor var_7646_cast_fp16 = reshape(shape = var_7645, x = k_151_cast_fp16)[name = tensor("op_7646_cast_fp16")]; + tensor var_7647 = const()[name = tensor("op_7647"), val = tensor([1, 20, 64, -1])]; + tensor var_7648_cast_fp16 = reshape(shape = var_7647, x = v_151_cast_fp16)[name = tensor("op_7648_cast_fp16")]; + tensor attn_weights_301_transpose_x_0 = const()[name = tensor("attn_weights_301_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_301_transpose_y_0 = const()[name = tensor("attn_weights_301_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_301_cast_fp16 = matmul(transpose_x = attn_weights_301_transpose_x_0, transpose_y = attn_weights_301_transpose_y_0, x = var_7644_cast_fp16, y = var_7646_cast_fp16)[name = tensor("attn_weights_301_cast_fp16")]; + tensor attn_weights_303_cast_fp16 = mul(x = attn_weights_301_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_303_cast_fp16")]; + tensor var_7652_cast_fp16 = softmax(axis = var_6879, x = attn_weights_303_cast_fp16)[name = tensor("op_7652_cast_fp16")]; + tensor attn_151_transpose_x_0 = const()[name = tensor("attn_151_transpose_x_0"), val = tensor(false)]; + tensor attn_151_transpose_y_0 = const()[name = tensor("attn_151_transpose_y_0"), val = tensor(true)]; + tensor attn_151_cast_fp16 = matmul(transpose_x = attn_151_transpose_x_0, transpose_y = attn_151_transpose_y_0, x = var_7648_cast_fp16, y = var_7652_cast_fp16)[name = tensor("attn_151_cast_fp16")]; + tensor var_7656 = const()[name = tensor("op_7656"), val = tensor([1, 1280, 1, -1])]; + tensor input_467_cast_fp16 = reshape(shape = var_7656, x = attn_151_cast_fp16)[name = tensor("input_467_cast_fp16")]; + tensor var_7661 = const()[name = tensor("op_7661"), val = tensor([1, 1])]; + tensor var_7663 = const()[name = tensor("op_7663"), val = tensor([1, 1])]; + tensor var_7665_pad_type_0 = const()[name = tensor("op_7665_pad_type_0"), val = tensor("custom")]; + tensor var_7665_pad_0 = const()[name = tensor("op_7665_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1059694656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060923520))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060923712)))]; + tensor var_7665_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_7663, groups = var_6895, pad = var_7665_pad_0, pad_type = var_7665_pad_type_0, strides = var_7661, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_467_cast_fp16)[name = tensor("op_7665_cast_fp16")]; + tensor inputs_227_cast_fp16 = add(x = var_7665_cast_fp16, y = inputs_225_cast_fp16)[name = tensor("inputs_227_cast_fp16")]; + tensor input_469_axes_0 = const()[name = tensor("input_469_axes_0"), val = tensor([1])]; + tensor input_469_gamma_0_to_fp16 = const()[name = tensor("input_469_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060926336)))]; + tensor input_469_beta_0_to_fp16 = const()[name = tensor("input_469_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060928960)))]; + tensor var_7675_to_fp16 = const()[name = tensor("op_7675_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_469_cast_fp16 = layer_norm(axes = input_469_axes_0, beta = input_469_beta_0_to_fp16, epsilon = var_7675_to_fp16, gamma = input_469_gamma_0_to_fp16, x = inputs_227_cast_fp16)[name = tensor("input_469_cast_fp16")]; + tensor var_7691 = const()[name = tensor("op_7691"), val = tensor([1, 1])]; + tensor var_7693 = const()[name = tensor("op_7693"), val = tensor([1, 1])]; + tensor var_7695_pad_type_0 = const()[name = tensor("op_7695_pad_type_0"), val = tensor("custom")]; + tensor var_7695_pad_0 = const()[name = tensor("op_7695_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060931584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070762048))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070762240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070769984))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7695_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7693, groups = var_6895, pad = var_7695_pad_0, pad_type = var_7695_pad_type_0, strides = var_7691, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_469_cast_fp16)[name = tensor("op_7695_cast_fp16")]; + tensor var_7696_split_sizes_0 = const()[name = tensor("op_7696_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7696_axis_0 = const()[name = tensor("op_7696_axis_0"), val = tensor(1)]; + tensor var_7696_cast_fp16_0, tensor var_7696_cast_fp16_1 = split(axis = var_7696_axis_0, split_sizes = var_7696_split_sizes_0, x = var_7695_cast_fp16)[name = tensor("op_7696_cast_fp16")]; + tensor var_7698_mode_0 = const()[name = tensor("op_7698_mode_0"), val = tensor("EXACT")]; + tensor var_7698_cast_fp16 = gelu(mode = var_7698_mode_0, x = var_7696_cast_fp16_1)[name = tensor("op_7698_cast_fp16")]; + tensor input_471_cast_fp16 = mul(x = var_7696_cast_fp16_0, y = var_7698_cast_fp16)[name = tensor("input_471_cast_fp16")]; + tensor var_7702 = const()[name = tensor("op_7702"), val = tensor([1, 1])]; + tensor var_7704 = const()[name = tensor("op_7704"), val = tensor([1, 1])]; + tensor var_7706_pad_type_0 = const()[name = tensor("op_7706_pad_type_0"), val = tensor("custom")]; + tensor var_7706_pad_0 = const()[name = tensor("op_7706_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070770176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075685440))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075685632)))]; + tensor var_7706_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_7704, groups = var_6895, pad = var_7706_pad_0, pad_type = var_7706_pad_type_0, strides = var_7702, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_471_cast_fp16)[name = tensor("op_7706_cast_fp16")]; + tensor inputs_229_cast_fp16 = add(x = var_7706_cast_fp16, y = inputs_227_cast_fp16)[name = tensor("inputs_229_cast_fp16")]; + tensor hidden_states_311_axes_0 = const()[name = tensor("hidden_states_311_axes_0"), val = tensor([1])]; + tensor hidden_states_311_gamma_0_to_fp16 = const()[name = tensor("hidden_states_311_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075688256)))]; + tensor hidden_states_311_beta_0_to_fp16 = const()[name = tensor("hidden_states_311_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075690880)))]; + tensor var_7722_to_fp16 = const()[name = tensor("op_7722_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_311_cast_fp16 = layer_norm(axes = hidden_states_311_axes_0, beta = hidden_states_311_beta_0_to_fp16, epsilon = var_7722_to_fp16, gamma = hidden_states_311_gamma_0_to_fp16, x = inputs_229_cast_fp16)[name = tensor("hidden_states_311_cast_fp16")]; + tensor var_7737 = const()[name = tensor("op_7737"), val = tensor([1, 1])]; + tensor var_7739 = const()[name = tensor("op_7739"), val = tensor([1, 1])]; + tensor q_153_pad_type_0 = const()[name = tensor("q_153_pad_type_0"), val = tensor("custom")]; + tensor q_153_pad_0 = const()[name = tensor("q_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075693504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1076922368))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_153_cast_fp16 = conv(dilations = var_7739, groups = var_6895, pad = q_153_pad_0, pad_type = q_153_pad_type_0, strides = var_7737, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_311_cast_fp16)[name = tensor("q_153_cast_fp16")]; + tensor var_7743 = const()[name = tensor("op_7743"), val = tensor([1, 1])]; + tensor var_7745 = const()[name = tensor("op_7745"), val = tensor([1, 1])]; + tensor k_153_pad_type_0 = const()[name = tensor("k_153_pad_type_0"), val = tensor("custom")]; + tensor k_153_pad_0 = const()[name = tensor("k_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1076922560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1078151424))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_153_cast_fp16 = conv(dilations = var_7745, groups = var_6895, pad = k_153_pad_0, pad_type = k_153_pad_type_0, strides = var_7743, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_311_cast_fp16)[name = tensor("k_153_cast_fp16")]; + tensor var_7749 = const()[name = tensor("op_7749"), val = tensor([1, 1])]; + tensor var_7751 = const()[name = tensor("op_7751"), val = tensor([1, 1])]; + tensor v_153_pad_type_0 = const()[name = tensor("v_153_pad_type_0"), val = tensor("custom")]; + tensor v_153_pad_0 = const()[name = tensor("v_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1078151616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1079380480))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_153_cast_fp16 = conv(dilations = var_7751, groups = var_6895, pad = v_153_pad_0, pad_type = v_153_pad_type_0, strides = var_7749, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_311_cast_fp16)[name = tensor("v_153_cast_fp16")]; + tensor var_7755 = const()[name = tensor("op_7755"), val = tensor([1, 20, 64, -1])]; + tensor var_7756_cast_fp16 = reshape(shape = var_7755, x = q_153_cast_fp16)[name = tensor("op_7756_cast_fp16")]; + tensor var_7757 = const()[name = tensor("op_7757"), val = tensor([1, 20, 64, -1])]; + tensor var_7758_cast_fp16 = reshape(shape = var_7757, x = k_153_cast_fp16)[name = tensor("op_7758_cast_fp16")]; + tensor var_7759 = const()[name = tensor("op_7759"), val = tensor([1, 20, 64, -1])]; + tensor var_7760_cast_fp16 = reshape(shape = var_7759, x = v_153_cast_fp16)[name = tensor("op_7760_cast_fp16")]; + tensor attn_weights_305_transpose_x_0 = const()[name = tensor("attn_weights_305_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_305_transpose_y_0 = const()[name = tensor("attn_weights_305_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7756_cast_fp16, y = var_7758_cast_fp16)[name = tensor("attn_weights_305_cast_fp16")]; + tensor attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_307_cast_fp16")]; + tensor var_7764_cast_fp16 = softmax(axis = var_6879, x = attn_weights_307_cast_fp16)[name = tensor("op_7764_cast_fp16")]; + tensor attn_153_transpose_x_0 = const()[name = tensor("attn_153_transpose_x_0"), val = tensor(false)]; + tensor attn_153_transpose_y_0 = const()[name = tensor("attn_153_transpose_y_0"), val = tensor(true)]; + tensor attn_153_cast_fp16 = matmul(transpose_x = attn_153_transpose_x_0, transpose_y = attn_153_transpose_y_0, x = var_7760_cast_fp16, y = var_7764_cast_fp16)[name = tensor("attn_153_cast_fp16")]; + tensor var_7768 = const()[name = tensor("op_7768"), val = tensor([1, 1280, 1, -1])]; + tensor input_473_cast_fp16 = reshape(shape = var_7768, x = attn_153_cast_fp16)[name = tensor("input_473_cast_fp16")]; + tensor var_7773 = const()[name = tensor("op_7773"), val = tensor([1, 1])]; + tensor var_7775 = const()[name = tensor("op_7775"), val = tensor([1, 1])]; + tensor var_7777_pad_type_0 = const()[name = tensor("op_7777_pad_type_0"), val = tensor("custom")]; + tensor var_7777_pad_0 = const()[name = tensor("op_7777_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1079380672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080609536))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080609728)))]; + tensor var_7777_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_7775, groups = var_6895, pad = var_7777_pad_0, pad_type = var_7777_pad_type_0, strides = var_7773, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_473_cast_fp16)[name = tensor("op_7777_cast_fp16")]; + tensor inputs_231_cast_fp16 = add(x = var_7777_cast_fp16, y = inputs_229_cast_fp16)[name = tensor("inputs_231_cast_fp16")]; + tensor hidden_states_313_axes_0 = const()[name = tensor("hidden_states_313_axes_0"), val = tensor([1])]; + tensor hidden_states_313_gamma_0_to_fp16 = const()[name = tensor("hidden_states_313_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080612352)))]; + tensor hidden_states_313_beta_0_to_fp16 = const()[name = tensor("hidden_states_313_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080614976)))]; + tensor var_7787_to_fp16 = const()[name = tensor("op_7787_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_313_cast_fp16 = layer_norm(axes = hidden_states_313_axes_0, beta = hidden_states_313_beta_0_to_fp16, epsilon = var_7787_to_fp16, gamma = hidden_states_313_gamma_0_to_fp16, x = inputs_231_cast_fp16)[name = tensor("hidden_states_313_cast_fp16")]; + tensor var_7802 = const()[name = tensor("op_7802"), val = tensor([1, 1])]; + tensor var_7804 = const()[name = tensor("op_7804"), val = tensor([1, 1])]; + tensor q_155_pad_type_0 = const()[name = tensor("q_155_pad_type_0"), val = tensor("custom")]; + tensor q_155_pad_0 = const()[name = tensor("q_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080617600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1081846464))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_155_cast_fp16 = conv(dilations = var_7804, groups = var_6895, pad = q_155_pad_0, pad_type = q_155_pad_type_0, strides = var_7802, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_313_cast_fp16)[name = tensor("q_155_cast_fp16")]; + tensor var_7808 = const()[name = tensor("op_7808"), val = tensor([1, 1])]; + tensor var_7810 = const()[name = tensor("op_7810"), val = tensor([1, 1])]; + tensor k_155_pad_type_0 = const()[name = tensor("k_155_pad_type_0"), val = tensor("custom")]; + tensor k_155_pad_0 = const()[name = tensor("k_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1081846656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1083812800))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_155_cast_fp16 = conv(dilations = var_7810, groups = var_6895, pad = k_155_pad_0, pad_type = k_155_pad_type_0, strides = var_7808, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_155_cast_fp16")]; + tensor var_7814 = const()[name = tensor("op_7814"), val = tensor([1, 1])]; + tensor var_7816 = const()[name = tensor("op_7816"), val = tensor([1, 1])]; + tensor v_155_pad_type_0 = const()[name = tensor("v_155_pad_type_0"), val = tensor("custom")]; + tensor v_155_pad_0 = const()[name = tensor("v_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1083812992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1085779136))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_155_cast_fp16 = conv(dilations = var_7816, groups = var_6895, pad = v_155_pad_0, pad_type = v_155_pad_type_0, strides = var_7814, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_155_cast_fp16")]; + tensor var_7820 = const()[name = tensor("op_7820"), val = tensor([1, 20, 64, -1])]; + tensor var_7821_cast_fp16 = reshape(shape = var_7820, x = q_155_cast_fp16)[name = tensor("op_7821_cast_fp16")]; + tensor var_7822 = const()[name = tensor("op_7822"), val = tensor([1, 20, 64, -1])]; + tensor var_7823_cast_fp16 = reshape(shape = var_7822, x = k_155_cast_fp16)[name = tensor("op_7823_cast_fp16")]; + tensor var_7824 = const()[name = tensor("op_7824"), val = tensor([1, 20, 64, -1])]; + tensor var_7825_cast_fp16 = reshape(shape = var_7824, x = v_155_cast_fp16)[name = tensor("op_7825_cast_fp16")]; + tensor attn_weights_309_transpose_x_0 = const()[name = tensor("attn_weights_309_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_309_transpose_y_0 = const()[name = tensor("attn_weights_309_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_309_cast_fp16 = matmul(transpose_x = attn_weights_309_transpose_x_0, transpose_y = attn_weights_309_transpose_y_0, x = var_7821_cast_fp16, y = var_7823_cast_fp16)[name = tensor("attn_weights_309_cast_fp16")]; + tensor attn_weights_311_cast_fp16 = mul(x = attn_weights_309_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_311_cast_fp16")]; + tensor var_7829_cast_fp16 = softmax(axis = var_6879, x = attn_weights_311_cast_fp16)[name = tensor("op_7829_cast_fp16")]; + tensor attn_155_transpose_x_0 = const()[name = tensor("attn_155_transpose_x_0"), val = tensor(false)]; + tensor attn_155_transpose_y_0 = const()[name = tensor("attn_155_transpose_y_0"), val = tensor(true)]; + tensor attn_155_cast_fp16 = matmul(transpose_x = attn_155_transpose_x_0, transpose_y = attn_155_transpose_y_0, x = var_7825_cast_fp16, y = var_7829_cast_fp16)[name = tensor("attn_155_cast_fp16")]; + tensor var_7833 = const()[name = tensor("op_7833"), val = tensor([1, 1280, 1, -1])]; + tensor input_475_cast_fp16 = reshape(shape = var_7833, x = attn_155_cast_fp16)[name = tensor("input_475_cast_fp16")]; + tensor var_7838 = const()[name = tensor("op_7838"), val = tensor([1, 1])]; + tensor var_7840 = const()[name = tensor("op_7840"), val = tensor([1, 1])]; + tensor var_7842_pad_type_0 = const()[name = tensor("op_7842_pad_type_0"), val = tensor("custom")]; + tensor var_7842_pad_0 = const()[name = tensor("op_7842_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1085779328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087008192))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087008384)))]; + tensor var_7842_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_7840, groups = var_6895, pad = var_7842_pad_0, pad_type = var_7842_pad_type_0, strides = var_7838, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_475_cast_fp16)[name = tensor("op_7842_cast_fp16")]; + tensor inputs_233_cast_fp16 = add(x = var_7842_cast_fp16, y = inputs_231_cast_fp16)[name = tensor("inputs_233_cast_fp16")]; + tensor input_477_axes_0 = const()[name = tensor("input_477_axes_0"), val = tensor([1])]; + tensor input_477_gamma_0_to_fp16 = const()[name = tensor("input_477_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087011008)))]; + tensor input_477_beta_0_to_fp16 = const()[name = tensor("input_477_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087013632)))]; + tensor var_7852_to_fp16 = const()[name = tensor("op_7852_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_477_cast_fp16 = layer_norm(axes = input_477_axes_0, beta = input_477_beta_0_to_fp16, epsilon = var_7852_to_fp16, gamma = input_477_gamma_0_to_fp16, x = inputs_233_cast_fp16)[name = tensor("input_477_cast_fp16")]; + tensor var_7868 = const()[name = tensor("op_7868"), val = tensor([1, 1])]; + tensor var_7870 = const()[name = tensor("op_7870"), val = tensor([1, 1])]; + tensor var_7872_pad_type_0 = const()[name = tensor("op_7872_pad_type_0"), val = tensor("custom")]; + tensor var_7872_pad_0 = const()[name = tensor("op_7872_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087016256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096846720))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096846912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096854656))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7872_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7870, groups = var_6895, pad = var_7872_pad_0, pad_type = var_7872_pad_type_0, strides = var_7868, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_477_cast_fp16)[name = tensor("op_7872_cast_fp16")]; + tensor var_7873_split_sizes_0 = const()[name = tensor("op_7873_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7873_axis_0 = const()[name = tensor("op_7873_axis_0"), val = tensor(1)]; + tensor var_7873_cast_fp16_0, tensor var_7873_cast_fp16_1 = split(axis = var_7873_axis_0, split_sizes = var_7873_split_sizes_0, x = var_7872_cast_fp16)[name = tensor("op_7873_cast_fp16")]; + tensor var_7875_mode_0 = const()[name = tensor("op_7875_mode_0"), val = tensor("EXACT")]; + tensor var_7875_cast_fp16 = gelu(mode = var_7875_mode_0, x = var_7873_cast_fp16_1)[name = tensor("op_7875_cast_fp16")]; + tensor input_479_cast_fp16 = mul(x = var_7873_cast_fp16_0, y = var_7875_cast_fp16)[name = tensor("input_479_cast_fp16")]; + tensor var_7879 = const()[name = tensor("op_7879"), val = tensor([1, 1])]; + tensor var_7881 = const()[name = tensor("op_7881"), val = tensor([1, 1])]; + tensor var_7883_pad_type_0 = const()[name = tensor("op_7883_pad_type_0"), val = tensor("custom")]; + tensor var_7883_pad_0 = const()[name = tensor("op_7883_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096854848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101770112))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101770304)))]; + tensor var_7883_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_7881, groups = var_6895, pad = var_7883_pad_0, pad_type = var_7883_pad_type_0, strides = var_7879, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_479_cast_fp16)[name = tensor("op_7883_cast_fp16")]; + tensor inputs_235_cast_fp16 = add(x = var_7883_cast_fp16, y = inputs_233_cast_fp16)[name = tensor("inputs_235_cast_fp16")]; + tensor hidden_states_317_axes_0 = const()[name = tensor("hidden_states_317_axes_0"), val = tensor([1])]; + tensor hidden_states_317_gamma_0_to_fp16 = const()[name = tensor("hidden_states_317_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101772928)))]; + tensor hidden_states_317_beta_0_to_fp16 = const()[name = tensor("hidden_states_317_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101775552)))]; + tensor var_7899_to_fp16 = const()[name = tensor("op_7899_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_317_cast_fp16 = layer_norm(axes = hidden_states_317_axes_0, beta = hidden_states_317_beta_0_to_fp16, epsilon = var_7899_to_fp16, gamma = hidden_states_317_gamma_0_to_fp16, x = inputs_235_cast_fp16)[name = tensor("hidden_states_317_cast_fp16")]; + tensor var_7914 = const()[name = tensor("op_7914"), val = tensor([1, 1])]; + tensor var_7916 = const()[name = tensor("op_7916"), val = tensor([1, 1])]; + tensor q_157_pad_type_0 = const()[name = tensor("q_157_pad_type_0"), val = tensor("custom")]; + tensor q_157_pad_0 = const()[name = tensor("q_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101778176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1103007040))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_157_cast_fp16 = conv(dilations = var_7916, groups = var_6895, pad = q_157_pad_0, pad_type = q_157_pad_type_0, strides = var_7914, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_317_cast_fp16)[name = tensor("q_157_cast_fp16")]; + tensor var_7920 = const()[name = tensor("op_7920"), val = tensor([1, 1])]; + tensor var_7922 = const()[name = tensor("op_7922"), val = tensor([1, 1])]; + tensor k_157_pad_type_0 = const()[name = tensor("k_157_pad_type_0"), val = tensor("custom")]; + tensor k_157_pad_0 = const()[name = tensor("k_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1103007232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1104236096))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_157_cast_fp16 = conv(dilations = var_7922, groups = var_6895, pad = k_157_pad_0, pad_type = k_157_pad_type_0, strides = var_7920, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_317_cast_fp16)[name = tensor("k_157_cast_fp16")]; + tensor var_7926 = const()[name = tensor("op_7926"), val = tensor([1, 1])]; + tensor var_7928 = const()[name = tensor("op_7928"), val = tensor([1, 1])]; + tensor v_157_pad_type_0 = const()[name = tensor("v_157_pad_type_0"), val = tensor("custom")]; + tensor v_157_pad_0 = const()[name = tensor("v_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1104236288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1105465152))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_157_cast_fp16 = conv(dilations = var_7928, groups = var_6895, pad = v_157_pad_0, pad_type = v_157_pad_type_0, strides = var_7926, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_317_cast_fp16)[name = tensor("v_157_cast_fp16")]; + tensor var_7932 = const()[name = tensor("op_7932"), val = tensor([1, 20, 64, -1])]; + tensor var_7933_cast_fp16 = reshape(shape = var_7932, x = q_157_cast_fp16)[name = tensor("op_7933_cast_fp16")]; + tensor var_7934 = const()[name = tensor("op_7934"), val = tensor([1, 20, 64, -1])]; + tensor var_7935_cast_fp16 = reshape(shape = var_7934, x = k_157_cast_fp16)[name = tensor("op_7935_cast_fp16")]; + tensor var_7936 = const()[name = tensor("op_7936"), val = tensor([1, 20, 64, -1])]; + tensor var_7937_cast_fp16 = reshape(shape = var_7936, x = v_157_cast_fp16)[name = tensor("op_7937_cast_fp16")]; + tensor attn_weights_313_transpose_x_0 = const()[name = tensor("attn_weights_313_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_313_transpose_y_0 = const()[name = tensor("attn_weights_313_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7933_cast_fp16, y = var_7935_cast_fp16)[name = tensor("attn_weights_313_cast_fp16")]; + tensor attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_315_cast_fp16")]; + tensor var_7941_cast_fp16 = softmax(axis = var_6879, x = attn_weights_315_cast_fp16)[name = tensor("op_7941_cast_fp16")]; + tensor attn_157_transpose_x_0 = const()[name = tensor("attn_157_transpose_x_0"), val = tensor(false)]; + tensor attn_157_transpose_y_0 = const()[name = tensor("attn_157_transpose_y_0"), val = tensor(true)]; + tensor attn_157_cast_fp16 = matmul(transpose_x = attn_157_transpose_x_0, transpose_y = attn_157_transpose_y_0, x = var_7937_cast_fp16, y = var_7941_cast_fp16)[name = tensor("attn_157_cast_fp16")]; + tensor var_7945 = const()[name = tensor("op_7945"), val = tensor([1, 1280, 1, -1])]; + tensor input_481_cast_fp16 = reshape(shape = var_7945, x = attn_157_cast_fp16)[name = tensor("input_481_cast_fp16")]; + tensor var_7950 = const()[name = tensor("op_7950"), val = tensor([1, 1])]; + tensor var_7952 = const()[name = tensor("op_7952"), val = tensor([1, 1])]; + tensor var_7954_pad_type_0 = const()[name = tensor("op_7954_pad_type_0"), val = tensor("custom")]; + tensor var_7954_pad_0 = const()[name = tensor("op_7954_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1105465344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106694208))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106694400)))]; + tensor var_7954_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_7952, groups = var_6895, pad = var_7954_pad_0, pad_type = var_7954_pad_type_0, strides = var_7950, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_481_cast_fp16)[name = tensor("op_7954_cast_fp16")]; + tensor inputs_237_cast_fp16 = add(x = var_7954_cast_fp16, y = inputs_235_cast_fp16)[name = tensor("inputs_237_cast_fp16")]; + tensor hidden_states_319_axes_0 = const()[name = tensor("hidden_states_319_axes_0"), val = tensor([1])]; + tensor hidden_states_319_gamma_0_to_fp16 = const()[name = tensor("hidden_states_319_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106697024)))]; + tensor hidden_states_319_beta_0_to_fp16 = const()[name = tensor("hidden_states_319_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106699648)))]; + tensor var_7964_to_fp16 = const()[name = tensor("op_7964_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_319_cast_fp16 = layer_norm(axes = hidden_states_319_axes_0, beta = hidden_states_319_beta_0_to_fp16, epsilon = var_7964_to_fp16, gamma = hidden_states_319_gamma_0_to_fp16, x = inputs_237_cast_fp16)[name = tensor("hidden_states_319_cast_fp16")]; + tensor var_7979 = const()[name = tensor("op_7979"), val = tensor([1, 1])]; + tensor var_7981 = const()[name = tensor("op_7981"), val = tensor([1, 1])]; + tensor q_159_pad_type_0 = const()[name = tensor("q_159_pad_type_0"), val = tensor("custom")]; + tensor q_159_pad_0 = const()[name = tensor("q_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106702272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107931136))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_159_cast_fp16 = conv(dilations = var_7981, groups = var_6895, pad = q_159_pad_0, pad_type = q_159_pad_type_0, strides = var_7979, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_319_cast_fp16)[name = tensor("q_159_cast_fp16")]; + tensor var_7985 = const()[name = tensor("op_7985"), val = tensor([1, 1])]; + tensor var_7987 = const()[name = tensor("op_7987"), val = tensor([1, 1])]; + tensor k_159_pad_type_0 = const()[name = tensor("k_159_pad_type_0"), val = tensor("custom")]; + tensor k_159_pad_0 = const()[name = tensor("k_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107931328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1109897472))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_159_cast_fp16 = conv(dilations = var_7987, groups = var_6895, pad = k_159_pad_0, pad_type = k_159_pad_type_0, strides = var_7985, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_159_cast_fp16")]; + tensor var_7991 = const()[name = tensor("op_7991"), val = tensor([1, 1])]; + tensor var_7993 = const()[name = tensor("op_7993"), val = tensor([1, 1])]; + tensor v_159_pad_type_0 = const()[name = tensor("v_159_pad_type_0"), val = tensor("custom")]; + tensor v_159_pad_0 = const()[name = tensor("v_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1109897664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1111863808))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_159_cast_fp16 = conv(dilations = var_7993, groups = var_6895, pad = v_159_pad_0, pad_type = v_159_pad_type_0, strides = var_7991, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_159_cast_fp16")]; + tensor var_7997 = const()[name = tensor("op_7997"), val = tensor([1, 20, 64, -1])]; + tensor var_7998_cast_fp16 = reshape(shape = var_7997, x = q_159_cast_fp16)[name = tensor("op_7998_cast_fp16")]; + tensor var_7999 = const()[name = tensor("op_7999"), val = tensor([1, 20, 64, -1])]; + tensor var_8000_cast_fp16 = reshape(shape = var_7999, x = k_159_cast_fp16)[name = tensor("op_8000_cast_fp16")]; + tensor var_8001 = const()[name = tensor("op_8001"), val = tensor([1, 20, 64, -1])]; + tensor var_8002_cast_fp16 = reshape(shape = var_8001, x = v_159_cast_fp16)[name = tensor("op_8002_cast_fp16")]; + tensor attn_weights_317_transpose_x_0 = const()[name = tensor("attn_weights_317_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_317_transpose_y_0 = const()[name = tensor("attn_weights_317_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_317_cast_fp16 = matmul(transpose_x = attn_weights_317_transpose_x_0, transpose_y = attn_weights_317_transpose_y_0, x = var_7998_cast_fp16, y = var_8000_cast_fp16)[name = tensor("attn_weights_317_cast_fp16")]; + tensor attn_weights_319_cast_fp16 = mul(x = attn_weights_317_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_319_cast_fp16")]; + tensor var_8006_cast_fp16 = softmax(axis = var_6879, x = attn_weights_319_cast_fp16)[name = tensor("op_8006_cast_fp16")]; + tensor attn_159_transpose_x_0 = const()[name = tensor("attn_159_transpose_x_0"), val = tensor(false)]; + tensor attn_159_transpose_y_0 = const()[name = tensor("attn_159_transpose_y_0"), val = tensor(true)]; + tensor attn_159_cast_fp16 = matmul(transpose_x = attn_159_transpose_x_0, transpose_y = attn_159_transpose_y_0, x = var_8002_cast_fp16, y = var_8006_cast_fp16)[name = tensor("attn_159_cast_fp16")]; + tensor var_8010 = const()[name = tensor("op_8010"), val = tensor([1, 1280, 1, -1])]; + tensor input_483_cast_fp16 = reshape(shape = var_8010, x = attn_159_cast_fp16)[name = tensor("input_483_cast_fp16")]; + tensor var_8015 = const()[name = tensor("op_8015"), val = tensor([1, 1])]; + tensor var_8017 = const()[name = tensor("op_8017"), val = tensor([1, 1])]; + tensor var_8019_pad_type_0 = const()[name = tensor("op_8019_pad_type_0"), val = tensor("custom")]; + tensor var_8019_pad_0 = const()[name = tensor("op_8019_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1111864000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113092864))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113093056)))]; + tensor var_8019_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_8017, groups = var_6895, pad = var_8019_pad_0, pad_type = var_8019_pad_type_0, strides = var_8015, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_483_cast_fp16)[name = tensor("op_8019_cast_fp16")]; + tensor inputs_239_cast_fp16 = add(x = var_8019_cast_fp16, y = inputs_237_cast_fp16)[name = tensor("inputs_239_cast_fp16")]; + tensor input_485_axes_0 = const()[name = tensor("input_485_axes_0"), val = tensor([1])]; + tensor input_485_gamma_0_to_fp16 = const()[name = tensor("input_485_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113095680)))]; + tensor input_485_beta_0_to_fp16 = const()[name = tensor("input_485_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113098304)))]; + tensor var_8029_to_fp16 = const()[name = tensor("op_8029_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_485_cast_fp16 = layer_norm(axes = input_485_axes_0, beta = input_485_beta_0_to_fp16, epsilon = var_8029_to_fp16, gamma = input_485_gamma_0_to_fp16, x = inputs_239_cast_fp16)[name = tensor("input_485_cast_fp16")]; + tensor var_8045 = const()[name = tensor("op_8045"), val = tensor([1, 1])]; + tensor var_8047 = const()[name = tensor("op_8047"), val = tensor([1, 1])]; + tensor var_8049_pad_type_0 = const()[name = tensor("op_8049_pad_type_0"), val = tensor("custom")]; + tensor var_8049_pad_0 = const()[name = tensor("op_8049_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113100928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122931392))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122931584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122939328))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8049_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8047, groups = var_6895, pad = var_8049_pad_0, pad_type = var_8049_pad_type_0, strides = var_8045, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_485_cast_fp16)[name = tensor("op_8049_cast_fp16")]; + tensor var_8050_split_sizes_0 = const()[name = tensor("op_8050_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8050_axis_0 = const()[name = tensor("op_8050_axis_0"), val = tensor(1)]; + tensor var_8050_cast_fp16_0, tensor var_8050_cast_fp16_1 = split(axis = var_8050_axis_0, split_sizes = var_8050_split_sizes_0, x = var_8049_cast_fp16)[name = tensor("op_8050_cast_fp16")]; + tensor var_8052_mode_0 = const()[name = tensor("op_8052_mode_0"), val = tensor("EXACT")]; + tensor var_8052_cast_fp16 = gelu(mode = var_8052_mode_0, x = var_8050_cast_fp16_1)[name = tensor("op_8052_cast_fp16")]; + tensor input_487_cast_fp16 = mul(x = var_8050_cast_fp16_0, y = var_8052_cast_fp16)[name = tensor("input_487_cast_fp16")]; + tensor var_8056 = const()[name = tensor("op_8056"), val = tensor([1, 1])]; + tensor var_8058 = const()[name = tensor("op_8058"), val = tensor([1, 1])]; + tensor var_8060_pad_type_0 = const()[name = tensor("op_8060_pad_type_0"), val = tensor("custom")]; + tensor var_8060_pad_0 = const()[name = tensor("op_8060_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122939520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127854784))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127854976)))]; + tensor var_8060_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_8058, groups = var_6895, pad = var_8060_pad_0, pad_type = var_8060_pad_type_0, strides = var_8056, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_487_cast_fp16)[name = tensor("op_8060_cast_fp16")]; + tensor inputs_241_cast_fp16 = add(x = var_8060_cast_fp16, y = inputs_239_cast_fp16)[name = tensor("inputs_241_cast_fp16")]; + tensor hidden_states_323_axes_0 = const()[name = tensor("hidden_states_323_axes_0"), val = tensor([1])]; + tensor hidden_states_323_gamma_0_to_fp16 = const()[name = tensor("hidden_states_323_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127857600)))]; + tensor hidden_states_323_beta_0_to_fp16 = const()[name = tensor("hidden_states_323_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127860224)))]; + tensor var_8076_to_fp16 = const()[name = tensor("op_8076_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_323_cast_fp16 = layer_norm(axes = hidden_states_323_axes_0, beta = hidden_states_323_beta_0_to_fp16, epsilon = var_8076_to_fp16, gamma = hidden_states_323_gamma_0_to_fp16, x = inputs_241_cast_fp16)[name = tensor("hidden_states_323_cast_fp16")]; + tensor var_8091 = const()[name = tensor("op_8091"), val = tensor([1, 1])]; + tensor var_8093 = const()[name = tensor("op_8093"), val = tensor([1, 1])]; + tensor q_161_pad_type_0 = const()[name = tensor("q_161_pad_type_0"), val = tensor("custom")]; + tensor q_161_pad_0 = const()[name = tensor("q_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127862848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129091712))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_161_cast_fp16 = conv(dilations = var_8093, groups = var_6895, pad = q_161_pad_0, pad_type = q_161_pad_type_0, strides = var_8091, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_323_cast_fp16)[name = tensor("q_161_cast_fp16")]; + tensor var_8097 = const()[name = tensor("op_8097"), val = tensor([1, 1])]; + tensor var_8099 = const()[name = tensor("op_8099"), val = tensor([1, 1])]; + tensor k_161_pad_type_0 = const()[name = tensor("k_161_pad_type_0"), val = tensor("custom")]; + tensor k_161_pad_0 = const()[name = tensor("k_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129091904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1130320768))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_161_cast_fp16 = conv(dilations = var_8099, groups = var_6895, pad = k_161_pad_0, pad_type = k_161_pad_type_0, strides = var_8097, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_323_cast_fp16)[name = tensor("k_161_cast_fp16")]; + tensor var_8103 = const()[name = tensor("op_8103"), val = tensor([1, 1])]; + tensor var_8105 = const()[name = tensor("op_8105"), val = tensor([1, 1])]; + tensor v_161_pad_type_0 = const()[name = tensor("v_161_pad_type_0"), val = tensor("custom")]; + tensor v_161_pad_0 = const()[name = tensor("v_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1130320960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131549824))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_161_cast_fp16 = conv(dilations = var_8105, groups = var_6895, pad = v_161_pad_0, pad_type = v_161_pad_type_0, strides = var_8103, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_323_cast_fp16)[name = tensor("v_161_cast_fp16")]; + tensor var_8109 = const()[name = tensor("op_8109"), val = tensor([1, 20, 64, -1])]; + tensor var_8110_cast_fp16 = reshape(shape = var_8109, x = q_161_cast_fp16)[name = tensor("op_8110_cast_fp16")]; + tensor var_8111 = const()[name = tensor("op_8111"), val = tensor([1, 20, 64, -1])]; + tensor var_8112_cast_fp16 = reshape(shape = var_8111, x = k_161_cast_fp16)[name = tensor("op_8112_cast_fp16")]; + tensor var_8113 = const()[name = tensor("op_8113"), val = tensor([1, 20, 64, -1])]; + tensor var_8114_cast_fp16 = reshape(shape = var_8113, x = v_161_cast_fp16)[name = tensor("op_8114_cast_fp16")]; + tensor attn_weights_321_transpose_x_0 = const()[name = tensor("attn_weights_321_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_321_transpose_y_0 = const()[name = tensor("attn_weights_321_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_8110_cast_fp16, y = var_8112_cast_fp16)[name = tensor("attn_weights_321_cast_fp16")]; + tensor attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_323_cast_fp16")]; + tensor var_8118_cast_fp16 = softmax(axis = var_6879, x = attn_weights_323_cast_fp16)[name = tensor("op_8118_cast_fp16")]; + tensor attn_161_transpose_x_0 = const()[name = tensor("attn_161_transpose_x_0"), val = tensor(false)]; + tensor attn_161_transpose_y_0 = const()[name = tensor("attn_161_transpose_y_0"), val = tensor(true)]; + tensor attn_161_cast_fp16 = matmul(transpose_x = attn_161_transpose_x_0, transpose_y = attn_161_transpose_y_0, x = var_8114_cast_fp16, y = var_8118_cast_fp16)[name = tensor("attn_161_cast_fp16")]; + tensor var_8122 = const()[name = tensor("op_8122"), val = tensor([1, 1280, 1, -1])]; + tensor input_489_cast_fp16 = reshape(shape = var_8122, x = attn_161_cast_fp16)[name = tensor("input_489_cast_fp16")]; + tensor var_8127 = const()[name = tensor("op_8127"), val = tensor([1, 1])]; + tensor var_8129 = const()[name = tensor("op_8129"), val = tensor([1, 1])]; + tensor var_8131_pad_type_0 = const()[name = tensor("op_8131_pad_type_0"), val = tensor("custom")]; + tensor var_8131_pad_0 = const()[name = tensor("op_8131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131550016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132778880))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132779072)))]; + tensor var_8131_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_8129, groups = var_6895, pad = var_8131_pad_0, pad_type = var_8131_pad_type_0, strides = var_8127, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_489_cast_fp16)[name = tensor("op_8131_cast_fp16")]; + tensor inputs_243_cast_fp16 = add(x = var_8131_cast_fp16, y = inputs_241_cast_fp16)[name = tensor("inputs_243_cast_fp16")]; + tensor hidden_states_325_axes_0 = const()[name = tensor("hidden_states_325_axes_0"), val = tensor([1])]; + tensor hidden_states_325_gamma_0_to_fp16 = const()[name = tensor("hidden_states_325_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132781696)))]; + tensor hidden_states_325_beta_0_to_fp16 = const()[name = tensor("hidden_states_325_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132784320)))]; + tensor var_8141_to_fp16 = const()[name = tensor("op_8141_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_325_cast_fp16 = layer_norm(axes = hidden_states_325_axes_0, beta = hidden_states_325_beta_0_to_fp16, epsilon = var_8141_to_fp16, gamma = hidden_states_325_gamma_0_to_fp16, x = inputs_243_cast_fp16)[name = tensor("hidden_states_325_cast_fp16")]; + tensor var_8156 = const()[name = tensor("op_8156"), val = tensor([1, 1])]; + tensor var_8158 = const()[name = tensor("op_8158"), val = tensor([1, 1])]; + tensor q_163_pad_type_0 = const()[name = tensor("q_163_pad_type_0"), val = tensor("custom")]; + tensor q_163_pad_0 = const()[name = tensor("q_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132786944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1134015808))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_163_cast_fp16 = conv(dilations = var_8158, groups = var_6895, pad = q_163_pad_0, pad_type = q_163_pad_type_0, strides = var_8156, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_325_cast_fp16)[name = tensor("q_163_cast_fp16")]; + tensor var_8162 = const()[name = tensor("op_8162"), val = tensor([1, 1])]; + tensor var_8164 = const()[name = tensor("op_8164"), val = tensor([1, 1])]; + tensor k_163_pad_type_0 = const()[name = tensor("k_163_pad_type_0"), val = tensor("custom")]; + tensor k_163_pad_0 = const()[name = tensor("k_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1134016000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1135982144))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_163_cast_fp16 = conv(dilations = var_8164, groups = var_6895, pad = k_163_pad_0, pad_type = k_163_pad_type_0, strides = var_8162, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_163_cast_fp16")]; + tensor var_8168 = const()[name = tensor("op_8168"), val = tensor([1, 1])]; + tensor var_8170 = const()[name = tensor("op_8170"), val = tensor([1, 1])]; + tensor v_163_pad_type_0 = const()[name = tensor("v_163_pad_type_0"), val = tensor("custom")]; + tensor v_163_pad_0 = const()[name = tensor("v_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1135982336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137948480))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_163_cast_fp16 = conv(dilations = var_8170, groups = var_6895, pad = v_163_pad_0, pad_type = v_163_pad_type_0, strides = var_8168, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_163_cast_fp16")]; + tensor var_8174 = const()[name = tensor("op_8174"), val = tensor([1, 20, 64, -1])]; + tensor var_8175_cast_fp16 = reshape(shape = var_8174, x = q_163_cast_fp16)[name = tensor("op_8175_cast_fp16")]; + tensor var_8176 = const()[name = tensor("op_8176"), val = tensor([1, 20, 64, -1])]; + tensor var_8177_cast_fp16 = reshape(shape = var_8176, x = k_163_cast_fp16)[name = tensor("op_8177_cast_fp16")]; + tensor var_8178 = const()[name = tensor("op_8178"), val = tensor([1, 20, 64, -1])]; + tensor var_8179_cast_fp16 = reshape(shape = var_8178, x = v_163_cast_fp16)[name = tensor("op_8179_cast_fp16")]; + tensor attn_weights_325_transpose_x_0 = const()[name = tensor("attn_weights_325_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_325_transpose_y_0 = const()[name = tensor("attn_weights_325_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_325_cast_fp16 = matmul(transpose_x = attn_weights_325_transpose_x_0, transpose_y = attn_weights_325_transpose_y_0, x = var_8175_cast_fp16, y = var_8177_cast_fp16)[name = tensor("attn_weights_325_cast_fp16")]; + tensor attn_weights_327_cast_fp16 = mul(x = attn_weights_325_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_327_cast_fp16")]; + tensor var_8183_cast_fp16 = softmax(axis = var_6879, x = attn_weights_327_cast_fp16)[name = tensor("op_8183_cast_fp16")]; + tensor attn_163_transpose_x_0 = const()[name = tensor("attn_163_transpose_x_0"), val = tensor(false)]; + tensor attn_163_transpose_y_0 = const()[name = tensor("attn_163_transpose_y_0"), val = tensor(true)]; + tensor attn_163_cast_fp16 = matmul(transpose_x = attn_163_transpose_x_0, transpose_y = attn_163_transpose_y_0, x = var_8179_cast_fp16, y = var_8183_cast_fp16)[name = tensor("attn_163_cast_fp16")]; + tensor var_8187 = const()[name = tensor("op_8187"), val = tensor([1, 1280, 1, -1])]; + tensor input_491_cast_fp16 = reshape(shape = var_8187, x = attn_163_cast_fp16)[name = tensor("input_491_cast_fp16")]; + tensor var_8192 = const()[name = tensor("op_8192"), val = tensor([1, 1])]; + tensor var_8194 = const()[name = tensor("op_8194"), val = tensor([1, 1])]; + tensor var_8196_pad_type_0 = const()[name = tensor("op_8196_pad_type_0"), val = tensor("custom")]; + tensor var_8196_pad_0 = const()[name = tensor("op_8196_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137948672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139177536))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139177728)))]; + tensor var_8196_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_8194, groups = var_6895, pad = var_8196_pad_0, pad_type = var_8196_pad_type_0, strides = var_8192, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_491_cast_fp16)[name = tensor("op_8196_cast_fp16")]; + tensor inputs_245_cast_fp16 = add(x = var_8196_cast_fp16, y = inputs_243_cast_fp16)[name = tensor("inputs_245_cast_fp16")]; + tensor input_493_axes_0 = const()[name = tensor("input_493_axes_0"), val = tensor([1])]; + tensor input_493_gamma_0_to_fp16 = const()[name = tensor("input_493_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139180352)))]; + tensor input_493_beta_0_to_fp16 = const()[name = tensor("input_493_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139182976)))]; + tensor var_8206_to_fp16 = const()[name = tensor("op_8206_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_493_cast_fp16 = layer_norm(axes = input_493_axes_0, beta = input_493_beta_0_to_fp16, epsilon = var_8206_to_fp16, gamma = input_493_gamma_0_to_fp16, x = inputs_245_cast_fp16)[name = tensor("input_493_cast_fp16")]; + tensor var_8222 = const()[name = tensor("op_8222"), val = tensor([1, 1])]; + tensor var_8224 = const()[name = tensor("op_8224"), val = tensor([1, 1])]; + tensor var_8226_pad_type_0 = const()[name = tensor("op_8226_pad_type_0"), val = tensor("custom")]; + tensor var_8226_pad_0 = const()[name = tensor("op_8226_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139185600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149016064))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149016256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149024000))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8226_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8224, groups = var_6895, pad = var_8226_pad_0, pad_type = var_8226_pad_type_0, strides = var_8222, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_493_cast_fp16)[name = tensor("op_8226_cast_fp16")]; + tensor var_8227_split_sizes_0 = const()[name = tensor("op_8227_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8227_axis_0 = const()[name = tensor("op_8227_axis_0"), val = tensor(1)]; + tensor var_8227_cast_fp16_0, tensor var_8227_cast_fp16_1 = split(axis = var_8227_axis_0, split_sizes = var_8227_split_sizes_0, x = var_8226_cast_fp16)[name = tensor("op_8227_cast_fp16")]; + tensor var_8229_mode_0 = const()[name = tensor("op_8229_mode_0"), val = tensor("EXACT")]; + tensor var_8229_cast_fp16 = gelu(mode = var_8229_mode_0, x = var_8227_cast_fp16_1)[name = tensor("op_8229_cast_fp16")]; + tensor input_495_cast_fp16 = mul(x = var_8227_cast_fp16_0, y = var_8229_cast_fp16)[name = tensor("input_495_cast_fp16")]; + tensor var_8233 = const()[name = tensor("op_8233"), val = tensor([1, 1])]; + tensor var_8235 = const()[name = tensor("op_8235"), val = tensor([1, 1])]; + tensor var_8237_pad_type_0 = const()[name = tensor("op_8237_pad_type_0"), val = tensor("custom")]; + tensor var_8237_pad_0 = const()[name = tensor("op_8237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149024192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153939456))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153939648)))]; + tensor var_8237_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_8235, groups = var_6895, pad = var_8237_pad_0, pad_type = var_8237_pad_type_0, strides = var_8233, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_495_cast_fp16)[name = tensor("op_8237_cast_fp16")]; + tensor inputs_247_cast_fp16 = add(x = var_8237_cast_fp16, y = inputs_245_cast_fp16)[name = tensor("inputs_247_cast_fp16")]; + tensor hidden_states_329_axes_0 = const()[name = tensor("hidden_states_329_axes_0"), val = tensor([1])]; + tensor hidden_states_329_gamma_0_to_fp16 = const()[name = tensor("hidden_states_329_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153942272)))]; + tensor hidden_states_329_beta_0_to_fp16 = const()[name = tensor("hidden_states_329_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153944896)))]; + tensor var_8253_to_fp16 = const()[name = tensor("op_8253_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_329_cast_fp16 = layer_norm(axes = hidden_states_329_axes_0, beta = hidden_states_329_beta_0_to_fp16, epsilon = var_8253_to_fp16, gamma = hidden_states_329_gamma_0_to_fp16, x = inputs_247_cast_fp16)[name = tensor("hidden_states_329_cast_fp16")]; + tensor var_8268 = const()[name = tensor("op_8268"), val = tensor([1, 1])]; + tensor var_8270 = const()[name = tensor("op_8270"), val = tensor([1, 1])]; + tensor q_165_pad_type_0 = const()[name = tensor("q_165_pad_type_0"), val = tensor("custom")]; + tensor q_165_pad_0 = const()[name = tensor("q_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153947520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1155176384))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_165_cast_fp16 = conv(dilations = var_8270, groups = var_6895, pad = q_165_pad_0, pad_type = q_165_pad_type_0, strides = var_8268, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_329_cast_fp16)[name = tensor("q_165_cast_fp16")]; + tensor var_8274 = const()[name = tensor("op_8274"), val = tensor([1, 1])]; + tensor var_8276 = const()[name = tensor("op_8276"), val = tensor([1, 1])]; + tensor k_165_pad_type_0 = const()[name = tensor("k_165_pad_type_0"), val = tensor("custom")]; + tensor k_165_pad_0 = const()[name = tensor("k_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1155176576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156405440))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_165_cast_fp16 = conv(dilations = var_8276, groups = var_6895, pad = k_165_pad_0, pad_type = k_165_pad_type_0, strides = var_8274, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_329_cast_fp16)[name = tensor("k_165_cast_fp16")]; + tensor var_8280 = const()[name = tensor("op_8280"), val = tensor([1, 1])]; + tensor var_8282 = const()[name = tensor("op_8282"), val = tensor([1, 1])]; + tensor v_165_pad_type_0 = const()[name = tensor("v_165_pad_type_0"), val = tensor("custom")]; + tensor v_165_pad_0 = const()[name = tensor("v_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156405632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157634496))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_165_cast_fp16 = conv(dilations = var_8282, groups = var_6895, pad = v_165_pad_0, pad_type = v_165_pad_type_0, strides = var_8280, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_329_cast_fp16)[name = tensor("v_165_cast_fp16")]; + tensor var_8286 = const()[name = tensor("op_8286"), val = tensor([1, 20, 64, -1])]; + tensor var_8287_cast_fp16 = reshape(shape = var_8286, x = q_165_cast_fp16)[name = tensor("op_8287_cast_fp16")]; + tensor var_8288 = const()[name = tensor("op_8288"), val = tensor([1, 20, 64, -1])]; + tensor var_8289_cast_fp16 = reshape(shape = var_8288, x = k_165_cast_fp16)[name = tensor("op_8289_cast_fp16")]; + tensor var_8290 = const()[name = tensor("op_8290"), val = tensor([1, 20, 64, -1])]; + tensor var_8291_cast_fp16 = reshape(shape = var_8290, x = v_165_cast_fp16)[name = tensor("op_8291_cast_fp16")]; + tensor attn_weights_329_transpose_x_0 = const()[name = tensor("attn_weights_329_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_329_transpose_y_0 = const()[name = tensor("attn_weights_329_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_8287_cast_fp16, y = var_8289_cast_fp16)[name = tensor("attn_weights_329_cast_fp16")]; + tensor attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_331_cast_fp16")]; + tensor var_8295_cast_fp16 = softmax(axis = var_6879, x = attn_weights_331_cast_fp16)[name = tensor("op_8295_cast_fp16")]; + tensor attn_165_transpose_x_0 = const()[name = tensor("attn_165_transpose_x_0"), val = tensor(false)]; + tensor attn_165_transpose_y_0 = const()[name = tensor("attn_165_transpose_y_0"), val = tensor(true)]; + tensor attn_165_cast_fp16 = matmul(transpose_x = attn_165_transpose_x_0, transpose_y = attn_165_transpose_y_0, x = var_8291_cast_fp16, y = var_8295_cast_fp16)[name = tensor("attn_165_cast_fp16")]; + tensor var_8299 = const()[name = tensor("op_8299"), val = tensor([1, 1280, 1, -1])]; + tensor input_497_cast_fp16 = reshape(shape = var_8299, x = attn_165_cast_fp16)[name = tensor("input_497_cast_fp16")]; + tensor var_8304 = const()[name = tensor("op_8304"), val = tensor([1, 1])]; + tensor var_8306 = const()[name = tensor("op_8306"), val = tensor([1, 1])]; + tensor var_8308_pad_type_0 = const()[name = tensor("op_8308_pad_type_0"), val = tensor("custom")]; + tensor var_8308_pad_0 = const()[name = tensor("op_8308_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157634688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158863552))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158863744)))]; + tensor var_8308_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_8306, groups = var_6895, pad = var_8308_pad_0, pad_type = var_8308_pad_type_0, strides = var_8304, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_497_cast_fp16)[name = tensor("op_8308_cast_fp16")]; + tensor inputs_249_cast_fp16 = add(x = var_8308_cast_fp16, y = inputs_247_cast_fp16)[name = tensor("inputs_249_cast_fp16")]; + tensor hidden_states_331_axes_0 = const()[name = tensor("hidden_states_331_axes_0"), val = tensor([1])]; + tensor hidden_states_331_gamma_0_to_fp16 = const()[name = tensor("hidden_states_331_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158866368)))]; + tensor hidden_states_331_beta_0_to_fp16 = const()[name = tensor("hidden_states_331_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158868992)))]; + tensor var_8318_to_fp16 = const()[name = tensor("op_8318_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_331_cast_fp16 = layer_norm(axes = hidden_states_331_axes_0, beta = hidden_states_331_beta_0_to_fp16, epsilon = var_8318_to_fp16, gamma = hidden_states_331_gamma_0_to_fp16, x = inputs_249_cast_fp16)[name = tensor("hidden_states_331_cast_fp16")]; + tensor var_8333 = const()[name = tensor("op_8333"), val = tensor([1, 1])]; + tensor var_8335 = const()[name = tensor("op_8335"), val = tensor([1, 1])]; + tensor q_167_pad_type_0 = const()[name = tensor("q_167_pad_type_0"), val = tensor("custom")]; + tensor q_167_pad_0 = const()[name = tensor("q_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158871616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1160100480))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_167_cast_fp16 = conv(dilations = var_8335, groups = var_6895, pad = q_167_pad_0, pad_type = q_167_pad_type_0, strides = var_8333, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_331_cast_fp16)[name = tensor("q_167_cast_fp16")]; + tensor var_8339 = const()[name = tensor("op_8339"), val = tensor([1, 1])]; + tensor var_8341 = const()[name = tensor("op_8341"), val = tensor([1, 1])]; + tensor k_167_pad_type_0 = const()[name = tensor("k_167_pad_type_0"), val = tensor("custom")]; + tensor k_167_pad_0 = const()[name = tensor("k_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1160100672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1162066816))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_167_cast_fp16 = conv(dilations = var_8341, groups = var_6895, pad = k_167_pad_0, pad_type = k_167_pad_type_0, strides = var_8339, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_167_cast_fp16")]; + tensor var_8345 = const()[name = tensor("op_8345"), val = tensor([1, 1])]; + tensor var_8347 = const()[name = tensor("op_8347"), val = tensor([1, 1])]; + tensor v_167_pad_type_0 = const()[name = tensor("v_167_pad_type_0"), val = tensor("custom")]; + tensor v_167_pad_0 = const()[name = tensor("v_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1162067008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1164033152))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_167_cast_fp16 = conv(dilations = var_8347, groups = var_6895, pad = v_167_pad_0, pad_type = v_167_pad_type_0, strides = var_8345, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_167_cast_fp16")]; + tensor var_8351 = const()[name = tensor("op_8351"), val = tensor([1, 20, 64, -1])]; + tensor var_8352_cast_fp16 = reshape(shape = var_8351, x = q_167_cast_fp16)[name = tensor("op_8352_cast_fp16")]; + tensor var_8353 = const()[name = tensor("op_8353"), val = tensor([1, 20, 64, -1])]; + tensor var_8354_cast_fp16 = reshape(shape = var_8353, x = k_167_cast_fp16)[name = tensor("op_8354_cast_fp16")]; + tensor var_8355 = const()[name = tensor("op_8355"), val = tensor([1, 20, 64, -1])]; + tensor var_8356_cast_fp16 = reshape(shape = var_8355, x = v_167_cast_fp16)[name = tensor("op_8356_cast_fp16")]; + tensor attn_weights_333_transpose_x_0 = const()[name = tensor("attn_weights_333_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_333_transpose_y_0 = const()[name = tensor("attn_weights_333_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_333_cast_fp16 = matmul(transpose_x = attn_weights_333_transpose_x_0, transpose_y = attn_weights_333_transpose_y_0, x = var_8352_cast_fp16, y = var_8354_cast_fp16)[name = tensor("attn_weights_333_cast_fp16")]; + tensor attn_weights_335_cast_fp16 = mul(x = attn_weights_333_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_335_cast_fp16")]; + tensor var_8360_cast_fp16 = softmax(axis = var_6879, x = attn_weights_335_cast_fp16)[name = tensor("op_8360_cast_fp16")]; + tensor attn_167_transpose_x_0 = const()[name = tensor("attn_167_transpose_x_0"), val = tensor(false)]; + tensor attn_167_transpose_y_0 = const()[name = tensor("attn_167_transpose_y_0"), val = tensor(true)]; + tensor attn_167_cast_fp16 = matmul(transpose_x = attn_167_transpose_x_0, transpose_y = attn_167_transpose_y_0, x = var_8356_cast_fp16, y = var_8360_cast_fp16)[name = tensor("attn_167_cast_fp16")]; + tensor var_8364 = const()[name = tensor("op_8364"), val = tensor([1, 1280, 1, -1])]; + tensor input_499_cast_fp16 = reshape(shape = var_8364, x = attn_167_cast_fp16)[name = tensor("input_499_cast_fp16")]; + tensor var_8369 = const()[name = tensor("op_8369"), val = tensor([1, 1])]; + tensor var_8371 = const()[name = tensor("op_8371"), val = tensor([1, 1])]; + tensor var_8373_pad_type_0 = const()[name = tensor("op_8373_pad_type_0"), val = tensor("custom")]; + tensor var_8373_pad_0 = const()[name = tensor("op_8373_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1164033344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165262208))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165262400)))]; + tensor var_8373_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_8371, groups = var_6895, pad = var_8373_pad_0, pad_type = var_8373_pad_type_0, strides = var_8369, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_499_cast_fp16)[name = tensor("op_8373_cast_fp16")]; + tensor inputs_251_cast_fp16 = add(x = var_8373_cast_fp16, y = inputs_249_cast_fp16)[name = tensor("inputs_251_cast_fp16")]; + tensor input_501_axes_0 = const()[name = tensor("input_501_axes_0"), val = tensor([1])]; + tensor input_501_gamma_0_to_fp16 = const()[name = tensor("input_501_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165265024)))]; + tensor input_501_beta_0_to_fp16 = const()[name = tensor("input_501_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165267648)))]; + tensor var_8383_to_fp16 = const()[name = tensor("op_8383_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_501_cast_fp16 = layer_norm(axes = input_501_axes_0, beta = input_501_beta_0_to_fp16, epsilon = var_8383_to_fp16, gamma = input_501_gamma_0_to_fp16, x = inputs_251_cast_fp16)[name = tensor("input_501_cast_fp16")]; + tensor var_8399 = const()[name = tensor("op_8399"), val = tensor([1, 1])]; + tensor var_8401 = const()[name = tensor("op_8401"), val = tensor([1, 1])]; + tensor var_8403_pad_type_0 = const()[name = tensor("op_8403_pad_type_0"), val = tensor("custom")]; + tensor var_8403_pad_0 = const()[name = tensor("op_8403_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165270272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175100736))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175100928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175108672))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8403_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8401, groups = var_6895, pad = var_8403_pad_0, pad_type = var_8403_pad_type_0, strides = var_8399, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_501_cast_fp16)[name = tensor("op_8403_cast_fp16")]; + tensor var_8404_split_sizes_0 = const()[name = tensor("op_8404_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8404_axis_0 = const()[name = tensor("op_8404_axis_0"), val = tensor(1)]; + tensor var_8404_cast_fp16_0, tensor var_8404_cast_fp16_1 = split(axis = var_8404_axis_0, split_sizes = var_8404_split_sizes_0, x = var_8403_cast_fp16)[name = tensor("op_8404_cast_fp16")]; + tensor var_8406_mode_0 = const()[name = tensor("op_8406_mode_0"), val = tensor("EXACT")]; + tensor var_8406_cast_fp16 = gelu(mode = var_8406_mode_0, x = var_8404_cast_fp16_1)[name = tensor("op_8406_cast_fp16")]; + tensor input_503_cast_fp16 = mul(x = var_8404_cast_fp16_0, y = var_8406_cast_fp16)[name = tensor("input_503_cast_fp16")]; + tensor var_8410 = const()[name = tensor("op_8410"), val = tensor([1, 1])]; + tensor var_8412 = const()[name = tensor("op_8412"), val = tensor([1, 1])]; + tensor var_8414_pad_type_0 = const()[name = tensor("op_8414_pad_type_0"), val = tensor("custom")]; + tensor var_8414_pad_0 = const()[name = tensor("op_8414_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175108864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180024128))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180024320)))]; + tensor var_8414_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_8412, groups = var_6895, pad = var_8414_pad_0, pad_type = var_8414_pad_type_0, strides = var_8410, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_503_cast_fp16)[name = tensor("op_8414_cast_fp16")]; + tensor inputs_253_cast_fp16 = add(x = var_8414_cast_fp16, y = inputs_251_cast_fp16)[name = tensor("inputs_253_cast_fp16")]; + tensor hidden_states_335_axes_0 = const()[name = tensor("hidden_states_335_axes_0"), val = tensor([1])]; + tensor hidden_states_335_gamma_0_to_fp16 = const()[name = tensor("hidden_states_335_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180026944)))]; + tensor hidden_states_335_beta_0_to_fp16 = const()[name = tensor("hidden_states_335_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180029568)))]; + tensor var_8430_to_fp16 = const()[name = tensor("op_8430_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_335_cast_fp16 = layer_norm(axes = hidden_states_335_axes_0, beta = hidden_states_335_beta_0_to_fp16, epsilon = var_8430_to_fp16, gamma = hidden_states_335_gamma_0_to_fp16, x = inputs_253_cast_fp16)[name = tensor("hidden_states_335_cast_fp16")]; + tensor var_8445 = const()[name = tensor("op_8445"), val = tensor([1, 1])]; + tensor var_8447 = const()[name = tensor("op_8447"), val = tensor([1, 1])]; + tensor q_169_pad_type_0 = const()[name = tensor("q_169_pad_type_0"), val = tensor("custom")]; + tensor q_169_pad_0 = const()[name = tensor("q_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180032192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1181261056))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_169_cast_fp16 = conv(dilations = var_8447, groups = var_6895, pad = q_169_pad_0, pad_type = q_169_pad_type_0, strides = var_8445, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_335_cast_fp16)[name = tensor("q_169_cast_fp16")]; + tensor var_8451 = const()[name = tensor("op_8451"), val = tensor([1, 1])]; + tensor var_8453 = const()[name = tensor("op_8453"), val = tensor([1, 1])]; + tensor k_169_pad_type_0 = const()[name = tensor("k_169_pad_type_0"), val = tensor("custom")]; + tensor k_169_pad_0 = const()[name = tensor("k_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1181261248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1182490112))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_169_cast_fp16 = conv(dilations = var_8453, groups = var_6895, pad = k_169_pad_0, pad_type = k_169_pad_type_0, strides = var_8451, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_335_cast_fp16)[name = tensor("k_169_cast_fp16")]; + tensor var_8457 = const()[name = tensor("op_8457"), val = tensor([1, 1])]; + tensor var_8459 = const()[name = tensor("op_8459"), val = tensor([1, 1])]; + tensor v_169_pad_type_0 = const()[name = tensor("v_169_pad_type_0"), val = tensor("custom")]; + tensor v_169_pad_0 = const()[name = tensor("v_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1182490304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183719168))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_169_cast_fp16 = conv(dilations = var_8459, groups = var_6895, pad = v_169_pad_0, pad_type = v_169_pad_type_0, strides = var_8457, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_335_cast_fp16)[name = tensor("v_169_cast_fp16")]; + tensor var_8463 = const()[name = tensor("op_8463"), val = tensor([1, 20, 64, -1])]; + tensor var_8464_cast_fp16 = reshape(shape = var_8463, x = q_169_cast_fp16)[name = tensor("op_8464_cast_fp16")]; + tensor var_8465 = const()[name = tensor("op_8465"), val = tensor([1, 20, 64, -1])]; + tensor var_8466_cast_fp16 = reshape(shape = var_8465, x = k_169_cast_fp16)[name = tensor("op_8466_cast_fp16")]; + tensor var_8467 = const()[name = tensor("op_8467"), val = tensor([1, 20, 64, -1])]; + tensor var_8468_cast_fp16 = reshape(shape = var_8467, x = v_169_cast_fp16)[name = tensor("op_8468_cast_fp16")]; + tensor attn_weights_337_transpose_x_0 = const()[name = tensor("attn_weights_337_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_337_transpose_y_0 = const()[name = tensor("attn_weights_337_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_8464_cast_fp16, y = var_8466_cast_fp16)[name = tensor("attn_weights_337_cast_fp16")]; + tensor attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_339_cast_fp16")]; + tensor var_8472_cast_fp16 = softmax(axis = var_6879, x = attn_weights_339_cast_fp16)[name = tensor("op_8472_cast_fp16")]; + tensor attn_169_transpose_x_0 = const()[name = tensor("attn_169_transpose_x_0"), val = tensor(false)]; + tensor attn_169_transpose_y_0 = const()[name = tensor("attn_169_transpose_y_0"), val = tensor(true)]; + tensor attn_169_cast_fp16 = matmul(transpose_x = attn_169_transpose_x_0, transpose_y = attn_169_transpose_y_0, x = var_8468_cast_fp16, y = var_8472_cast_fp16)[name = tensor("attn_169_cast_fp16")]; + tensor var_8476 = const()[name = tensor("op_8476"), val = tensor([1, 1280, 1, -1])]; + tensor input_505_cast_fp16 = reshape(shape = var_8476, x = attn_169_cast_fp16)[name = tensor("input_505_cast_fp16")]; + tensor var_8481 = const()[name = tensor("op_8481"), val = tensor([1, 1])]; + tensor var_8483 = const()[name = tensor("op_8483"), val = tensor([1, 1])]; + tensor var_8485_pad_type_0 = const()[name = tensor("op_8485_pad_type_0"), val = tensor("custom")]; + tensor var_8485_pad_0 = const()[name = tensor("op_8485_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183719360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184948224))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184948416)))]; + tensor var_8485_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_8483, groups = var_6895, pad = var_8485_pad_0, pad_type = var_8485_pad_type_0, strides = var_8481, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_505_cast_fp16)[name = tensor("op_8485_cast_fp16")]; + tensor inputs_255_cast_fp16 = add(x = var_8485_cast_fp16, y = inputs_253_cast_fp16)[name = tensor("inputs_255_cast_fp16")]; + tensor hidden_states_337_axes_0 = const()[name = tensor("hidden_states_337_axes_0"), val = tensor([1])]; + tensor hidden_states_337_gamma_0_to_fp16 = const()[name = tensor("hidden_states_337_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184951040)))]; + tensor hidden_states_337_beta_0_to_fp16 = const()[name = tensor("hidden_states_337_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184953664)))]; + tensor var_8495_to_fp16 = const()[name = tensor("op_8495_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_337_cast_fp16 = layer_norm(axes = hidden_states_337_axes_0, beta = hidden_states_337_beta_0_to_fp16, epsilon = var_8495_to_fp16, gamma = hidden_states_337_gamma_0_to_fp16, x = inputs_255_cast_fp16)[name = tensor("hidden_states_337_cast_fp16")]; + tensor var_8510 = const()[name = tensor("op_8510"), val = tensor([1, 1])]; + tensor var_8512 = const()[name = tensor("op_8512"), val = tensor([1, 1])]; + tensor q_171_pad_type_0 = const()[name = tensor("q_171_pad_type_0"), val = tensor("custom")]; + tensor q_171_pad_0 = const()[name = tensor("q_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184956288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1186185152))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_171_cast_fp16 = conv(dilations = var_8512, groups = var_6895, pad = q_171_pad_0, pad_type = q_171_pad_type_0, strides = var_8510, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_337_cast_fp16)[name = tensor("q_171_cast_fp16")]; + tensor var_8516 = const()[name = tensor("op_8516"), val = tensor([1, 1])]; + tensor var_8518 = const()[name = tensor("op_8518"), val = tensor([1, 1])]; + tensor k_171_pad_type_0 = const()[name = tensor("k_171_pad_type_0"), val = tensor("custom")]; + tensor k_171_pad_0 = const()[name = tensor("k_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1186185344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1188151488))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_171_cast_fp16 = conv(dilations = var_8518, groups = var_6895, pad = k_171_pad_0, pad_type = k_171_pad_type_0, strides = var_8516, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_171_cast_fp16")]; + tensor var_8522 = const()[name = tensor("op_8522"), val = tensor([1, 1])]; + tensor var_8524 = const()[name = tensor("op_8524"), val = tensor([1, 1])]; + tensor v_171_pad_type_0 = const()[name = tensor("v_171_pad_type_0"), val = tensor("custom")]; + tensor v_171_pad_0 = const()[name = tensor("v_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1188151680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1190117824))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_171_cast_fp16 = conv(dilations = var_8524, groups = var_6895, pad = v_171_pad_0, pad_type = v_171_pad_type_0, strides = var_8522, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_171_cast_fp16")]; + tensor var_8528 = const()[name = tensor("op_8528"), val = tensor([1, 20, 64, -1])]; + tensor var_8529_cast_fp16 = reshape(shape = var_8528, x = q_171_cast_fp16)[name = tensor("op_8529_cast_fp16")]; + tensor var_8530 = const()[name = tensor("op_8530"), val = tensor([1, 20, 64, -1])]; + tensor var_8531_cast_fp16 = reshape(shape = var_8530, x = k_171_cast_fp16)[name = tensor("op_8531_cast_fp16")]; + tensor var_8532 = const()[name = tensor("op_8532"), val = tensor([1, 20, 64, -1])]; + tensor var_8533_cast_fp16 = reshape(shape = var_8532, x = v_171_cast_fp16)[name = tensor("op_8533_cast_fp16")]; + tensor attn_weights_341_transpose_x_0 = const()[name = tensor("attn_weights_341_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_341_transpose_y_0 = const()[name = tensor("attn_weights_341_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_341_cast_fp16 = matmul(transpose_x = attn_weights_341_transpose_x_0, transpose_y = attn_weights_341_transpose_y_0, x = var_8529_cast_fp16, y = var_8531_cast_fp16)[name = tensor("attn_weights_341_cast_fp16")]; + tensor attn_weights_343_cast_fp16 = mul(x = attn_weights_341_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_343_cast_fp16")]; + tensor var_8537_cast_fp16 = softmax(axis = var_6879, x = attn_weights_343_cast_fp16)[name = tensor("op_8537_cast_fp16")]; + tensor attn_171_transpose_x_0 = const()[name = tensor("attn_171_transpose_x_0"), val = tensor(false)]; + tensor attn_171_transpose_y_0 = const()[name = tensor("attn_171_transpose_y_0"), val = tensor(true)]; + tensor attn_171_cast_fp16 = matmul(transpose_x = attn_171_transpose_x_0, transpose_y = attn_171_transpose_y_0, x = var_8533_cast_fp16, y = var_8537_cast_fp16)[name = tensor("attn_171_cast_fp16")]; + tensor var_8541 = const()[name = tensor("op_8541"), val = tensor([1, 1280, 1, -1])]; + tensor input_507_cast_fp16 = reshape(shape = var_8541, x = attn_171_cast_fp16)[name = tensor("input_507_cast_fp16")]; + tensor var_8546 = const()[name = tensor("op_8546"), val = tensor([1, 1])]; + tensor var_8548 = const()[name = tensor("op_8548"), val = tensor([1, 1])]; + tensor var_8550_pad_type_0 = const()[name = tensor("op_8550_pad_type_0"), val = tensor("custom")]; + tensor var_8550_pad_0 = const()[name = tensor("op_8550_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1190118016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191346880))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191347072)))]; + tensor var_8550_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_8548, groups = var_6895, pad = var_8550_pad_0, pad_type = var_8550_pad_type_0, strides = var_8546, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = tensor("op_8550_cast_fp16")]; + tensor inputs_257_cast_fp16 = add(x = var_8550_cast_fp16, y = inputs_255_cast_fp16)[name = tensor("inputs_257_cast_fp16")]; + tensor input_509_axes_0 = const()[name = tensor("input_509_axes_0"), val = tensor([1])]; + tensor input_509_gamma_0_to_fp16 = const()[name = tensor("input_509_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191349696)))]; + tensor input_509_beta_0_to_fp16 = const()[name = tensor("input_509_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191352320)))]; + tensor var_8560_to_fp16 = const()[name = tensor("op_8560_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_509_cast_fp16 = layer_norm(axes = input_509_axes_0, beta = input_509_beta_0_to_fp16, epsilon = var_8560_to_fp16, gamma = input_509_gamma_0_to_fp16, x = inputs_257_cast_fp16)[name = tensor("input_509_cast_fp16")]; + tensor var_8576 = const()[name = tensor("op_8576"), val = tensor([1, 1])]; + tensor var_8578 = const()[name = tensor("op_8578"), val = tensor([1, 1])]; + tensor var_8580_pad_type_0 = const()[name = tensor("op_8580_pad_type_0"), val = tensor("custom")]; + tensor var_8580_pad_0 = const()[name = tensor("op_8580_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191354944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201185408))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201185600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201193344))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8580_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8578, groups = var_6895, pad = var_8580_pad_0, pad_type = var_8580_pad_type_0, strides = var_8576, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_509_cast_fp16)[name = tensor("op_8580_cast_fp16")]; + tensor var_8581_split_sizes_0 = const()[name = tensor("op_8581_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8581_axis_0 = const()[name = tensor("op_8581_axis_0"), val = tensor(1)]; + tensor var_8581_cast_fp16_0, tensor var_8581_cast_fp16_1 = split(axis = var_8581_axis_0, split_sizes = var_8581_split_sizes_0, x = var_8580_cast_fp16)[name = tensor("op_8581_cast_fp16")]; + tensor var_8583_mode_0 = const()[name = tensor("op_8583_mode_0"), val = tensor("EXACT")]; + tensor var_8583_cast_fp16 = gelu(mode = var_8583_mode_0, x = var_8581_cast_fp16_1)[name = tensor("op_8583_cast_fp16")]; + tensor input_511_cast_fp16 = mul(x = var_8581_cast_fp16_0, y = var_8583_cast_fp16)[name = tensor("input_511_cast_fp16")]; + tensor var_8587 = const()[name = tensor("op_8587"), val = tensor([1, 1])]; + tensor var_8589 = const()[name = tensor("op_8589"), val = tensor([1, 1])]; + tensor var_8591_pad_type_0 = const()[name = tensor("op_8591_pad_type_0"), val = tensor("custom")]; + tensor var_8591_pad_0 = const()[name = tensor("op_8591_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201193536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206108800))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206108992)))]; + tensor var_8591_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_8589, groups = var_6895, pad = var_8591_pad_0, pad_type = var_8591_pad_type_0, strides = var_8587, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = tensor("op_8591_cast_fp16")]; + tensor inputs_259_cast_fp16 = add(x = var_8591_cast_fp16, y = inputs_257_cast_fp16)[name = tensor("inputs_259_cast_fp16")]; + tensor hidden_states_341_axes_0 = const()[name = tensor("hidden_states_341_axes_0"), val = tensor([1])]; + tensor hidden_states_341_gamma_0_to_fp16 = const()[name = tensor("hidden_states_341_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206111616)))]; + tensor hidden_states_341_beta_0_to_fp16 = const()[name = tensor("hidden_states_341_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206114240)))]; + tensor var_8607_to_fp16 = const()[name = tensor("op_8607_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_341_cast_fp16 = layer_norm(axes = hidden_states_341_axes_0, beta = hidden_states_341_beta_0_to_fp16, epsilon = var_8607_to_fp16, gamma = hidden_states_341_gamma_0_to_fp16, x = inputs_259_cast_fp16)[name = tensor("hidden_states_341_cast_fp16")]; + tensor var_8622 = const()[name = tensor("op_8622"), val = tensor([1, 1])]; + tensor var_8624 = const()[name = tensor("op_8624"), val = tensor([1, 1])]; + tensor q_173_pad_type_0 = const()[name = tensor("q_173_pad_type_0"), val = tensor("custom")]; + tensor q_173_pad_0 = const()[name = tensor("q_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206116864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1207345728))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_173_cast_fp16 = conv(dilations = var_8624, groups = var_6895, pad = q_173_pad_0, pad_type = q_173_pad_type_0, strides = var_8622, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_341_cast_fp16)[name = tensor("q_173_cast_fp16")]; + tensor var_8628 = const()[name = tensor("op_8628"), val = tensor([1, 1])]; + tensor var_8630 = const()[name = tensor("op_8630"), val = tensor([1, 1])]; + tensor k_173_pad_type_0 = const()[name = tensor("k_173_pad_type_0"), val = tensor("custom")]; + tensor k_173_pad_0 = const()[name = tensor("k_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1207345920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208574784))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_173_cast_fp16 = conv(dilations = var_8630, groups = var_6895, pad = k_173_pad_0, pad_type = k_173_pad_type_0, strides = var_8628, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_341_cast_fp16)[name = tensor("k_173_cast_fp16")]; + tensor var_8634 = const()[name = tensor("op_8634"), val = tensor([1, 1])]; + tensor var_8636 = const()[name = tensor("op_8636"), val = tensor([1, 1])]; + tensor v_173_pad_type_0 = const()[name = tensor("v_173_pad_type_0"), val = tensor("custom")]; + tensor v_173_pad_0 = const()[name = tensor("v_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208574976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1209803840))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_173_cast_fp16 = conv(dilations = var_8636, groups = var_6895, pad = v_173_pad_0, pad_type = v_173_pad_type_0, strides = var_8634, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_341_cast_fp16)[name = tensor("v_173_cast_fp16")]; + tensor var_8640 = const()[name = tensor("op_8640"), val = tensor([1, 20, 64, -1])]; + tensor var_8641_cast_fp16 = reshape(shape = var_8640, x = q_173_cast_fp16)[name = tensor("op_8641_cast_fp16")]; + tensor var_8642 = const()[name = tensor("op_8642"), val = tensor([1, 20, 64, -1])]; + tensor var_8643_cast_fp16 = reshape(shape = var_8642, x = k_173_cast_fp16)[name = tensor("op_8643_cast_fp16")]; + tensor var_8644 = const()[name = tensor("op_8644"), val = tensor([1, 20, 64, -1])]; + tensor var_8645_cast_fp16 = reshape(shape = var_8644, x = v_173_cast_fp16)[name = tensor("op_8645_cast_fp16")]; + tensor attn_weights_345_transpose_x_0 = const()[name = tensor("attn_weights_345_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_345_transpose_y_0 = const()[name = tensor("attn_weights_345_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_8641_cast_fp16, y = var_8643_cast_fp16)[name = tensor("attn_weights_345_cast_fp16")]; + tensor attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_347_cast_fp16")]; + tensor var_8649_cast_fp16 = softmax(axis = var_6879, x = attn_weights_347_cast_fp16)[name = tensor("op_8649_cast_fp16")]; + tensor attn_173_transpose_x_0 = const()[name = tensor("attn_173_transpose_x_0"), val = tensor(false)]; + tensor attn_173_transpose_y_0 = const()[name = tensor("attn_173_transpose_y_0"), val = tensor(true)]; + tensor attn_173_cast_fp16 = matmul(transpose_x = attn_173_transpose_x_0, transpose_y = attn_173_transpose_y_0, x = var_8645_cast_fp16, y = var_8649_cast_fp16)[name = tensor("attn_173_cast_fp16")]; + tensor var_8653 = const()[name = tensor("op_8653"), val = tensor([1, 1280, 1, -1])]; + tensor input_513_cast_fp16 = reshape(shape = var_8653, x = attn_173_cast_fp16)[name = tensor("input_513_cast_fp16")]; + 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_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1209804032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211032896))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211033088)))]; + tensor var_8662_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_8660, groups = var_6895, pad = var_8662_pad_0, pad_type = var_8662_pad_type_0, strides = var_8658, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_513_cast_fp16)[name = tensor("op_8662_cast_fp16")]; + tensor inputs_261_cast_fp16 = add(x = var_8662_cast_fp16, y = inputs_259_cast_fp16)[name = tensor("inputs_261_cast_fp16")]; + tensor hidden_states_343_axes_0 = const()[name = tensor("hidden_states_343_axes_0"), val = tensor([1])]; + tensor hidden_states_343_gamma_0_to_fp16 = const()[name = tensor("hidden_states_343_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211035712)))]; + tensor hidden_states_343_beta_0_to_fp16 = const()[name = tensor("hidden_states_343_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211038336)))]; + tensor var_8672_to_fp16 = const()[name = tensor("op_8672_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_343_cast_fp16 = layer_norm(axes = hidden_states_343_axes_0, beta = hidden_states_343_beta_0_to_fp16, epsilon = var_8672_to_fp16, gamma = hidden_states_343_gamma_0_to_fp16, x = inputs_261_cast_fp16)[name = tensor("hidden_states_343_cast_fp16")]; + tensor var_8687 = const()[name = tensor("op_8687"), val = tensor([1, 1])]; + tensor var_8689 = const()[name = tensor("op_8689"), val = tensor([1, 1])]; + tensor q_175_pad_type_0 = const()[name = tensor("q_175_pad_type_0"), val = tensor("custom")]; + tensor q_175_pad_0 = const()[name = tensor("q_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211040960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1212269824))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_175_cast_fp16 = conv(dilations = var_8689, groups = var_6895, pad = q_175_pad_0, pad_type = q_175_pad_type_0, strides = var_8687, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_343_cast_fp16)[name = tensor("q_175_cast_fp16")]; + tensor var_8693 = const()[name = tensor("op_8693"), val = tensor([1, 1])]; + tensor var_8695 = const()[name = tensor("op_8695"), val = tensor([1, 1])]; + tensor k_175_pad_type_0 = const()[name = tensor("k_175_pad_type_0"), val = tensor("custom")]; + tensor k_175_pad_0 = const()[name = tensor("k_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1212270016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1214236160))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_175_cast_fp16 = conv(dilations = var_8695, groups = var_6895, pad = k_175_pad_0, pad_type = k_175_pad_type_0, strides = var_8693, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_175_cast_fp16")]; + tensor var_8699 = const()[name = tensor("op_8699"), val = tensor([1, 1])]; + tensor var_8701 = const()[name = tensor("op_8701"), val = tensor([1, 1])]; + tensor v_175_pad_type_0 = const()[name = tensor("v_175_pad_type_0"), val = tensor("custom")]; + tensor v_175_pad_0 = const()[name = tensor("v_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1214236352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1216202496))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_175_cast_fp16 = conv(dilations = var_8701, groups = var_6895, pad = v_175_pad_0, pad_type = v_175_pad_type_0, strides = var_8699, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_175_cast_fp16")]; + tensor var_8705 = const()[name = tensor("op_8705"), val = tensor([1, 20, 64, -1])]; + tensor var_8706_cast_fp16 = reshape(shape = var_8705, x = q_175_cast_fp16)[name = tensor("op_8706_cast_fp16")]; + tensor var_8707 = const()[name = tensor("op_8707"), val = tensor([1, 20, 64, -1])]; + tensor var_8708_cast_fp16 = reshape(shape = var_8707, x = k_175_cast_fp16)[name = tensor("op_8708_cast_fp16")]; + tensor var_8709 = const()[name = tensor("op_8709"), val = tensor([1, 20, 64, -1])]; + tensor var_8710_cast_fp16 = reshape(shape = var_8709, x = v_175_cast_fp16)[name = tensor("op_8710_cast_fp16")]; + tensor attn_weights_349_transpose_x_0 = const()[name = tensor("attn_weights_349_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_349_transpose_y_0 = const()[name = tensor("attn_weights_349_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_349_cast_fp16 = matmul(transpose_x = attn_weights_349_transpose_x_0, transpose_y = attn_weights_349_transpose_y_0, x = var_8706_cast_fp16, y = var_8708_cast_fp16)[name = tensor("attn_weights_349_cast_fp16")]; + tensor attn_weights_351_cast_fp16 = mul(x = attn_weights_349_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_351_cast_fp16")]; + tensor var_8714_cast_fp16 = softmax(axis = var_6879, x = attn_weights_351_cast_fp16)[name = tensor("op_8714_cast_fp16")]; + tensor attn_175_transpose_x_0 = const()[name = tensor("attn_175_transpose_x_0"), val = tensor(false)]; + tensor attn_175_transpose_y_0 = const()[name = tensor("attn_175_transpose_y_0"), val = tensor(true)]; + tensor attn_175_cast_fp16 = matmul(transpose_x = attn_175_transpose_x_0, transpose_y = attn_175_transpose_y_0, x = var_8710_cast_fp16, y = var_8714_cast_fp16)[name = tensor("attn_175_cast_fp16")]; + tensor var_8718 = const()[name = tensor("op_8718"), val = tensor([1, 1280, 1, -1])]; + tensor input_515_cast_fp16 = reshape(shape = var_8718, x = attn_175_cast_fp16)[name = tensor("input_515_cast_fp16")]; + tensor var_8723 = const()[name = tensor("op_8723"), val = tensor([1, 1])]; + tensor var_8725 = const()[name = tensor("op_8725"), val = tensor([1, 1])]; + tensor var_8727_pad_type_0 = const()[name = tensor("op_8727_pad_type_0"), val = tensor("custom")]; + tensor var_8727_pad_0 = const()[name = tensor("op_8727_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1216202688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217431552))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217431744)))]; + tensor var_8727_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_8725, groups = var_6895, pad = var_8727_pad_0, pad_type = var_8727_pad_type_0, strides = var_8723, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_515_cast_fp16)[name = tensor("op_8727_cast_fp16")]; + tensor inputs_263_cast_fp16 = add(x = var_8727_cast_fp16, y = inputs_261_cast_fp16)[name = tensor("inputs_263_cast_fp16")]; + tensor input_517_axes_0 = const()[name = tensor("input_517_axes_0"), val = tensor([1])]; + tensor input_517_gamma_0_to_fp16 = const()[name = tensor("input_517_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217434368)))]; + tensor input_517_beta_0_to_fp16 = const()[name = tensor("input_517_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217436992)))]; + tensor var_8737_to_fp16 = const()[name = tensor("op_8737_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_517_cast_fp16 = layer_norm(axes = input_517_axes_0, beta = input_517_beta_0_to_fp16, epsilon = var_8737_to_fp16, gamma = input_517_gamma_0_to_fp16, x = inputs_263_cast_fp16)[name = tensor("input_517_cast_fp16")]; + tensor var_8753 = const()[name = tensor("op_8753"), val = tensor([1, 1])]; + tensor var_8755 = const()[name = tensor("op_8755"), val = tensor([1, 1])]; + tensor var_8757_pad_type_0 = const()[name = tensor("op_8757_pad_type_0"), val = tensor("custom")]; + tensor var_8757_pad_0 = const()[name = tensor("op_8757_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217439616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227270080))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227270272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227278016))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8757_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8755, groups = var_6895, pad = var_8757_pad_0, pad_type = var_8757_pad_type_0, strides = var_8753, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_517_cast_fp16)[name = tensor("op_8757_cast_fp16")]; + tensor var_8758_split_sizes_0 = const()[name = tensor("op_8758_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8758_axis_0 = const()[name = tensor("op_8758_axis_0"), val = tensor(1)]; + tensor var_8758_cast_fp16_0, tensor var_8758_cast_fp16_1 = split(axis = var_8758_axis_0, split_sizes = var_8758_split_sizes_0, x = var_8757_cast_fp16)[name = tensor("op_8758_cast_fp16")]; + tensor var_8760_mode_0 = const()[name = tensor("op_8760_mode_0"), val = tensor("EXACT")]; + tensor var_8760_cast_fp16 = gelu(mode = var_8760_mode_0, x = var_8758_cast_fp16_1)[name = tensor("op_8760_cast_fp16")]; + tensor input_519_cast_fp16 = mul(x = var_8758_cast_fp16_0, y = var_8760_cast_fp16)[name = tensor("input_519_cast_fp16")]; + tensor var_8764 = const()[name = tensor("op_8764"), val = tensor([1, 1])]; + tensor var_8766 = const()[name = tensor("op_8766"), val = tensor([1, 1])]; + tensor var_8768_pad_type_0 = const()[name = tensor("op_8768_pad_type_0"), val = tensor("custom")]; + tensor var_8768_pad_0 = const()[name = tensor("op_8768_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227278208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1232193472))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1232193664)))]; + tensor var_8768_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_8766, groups = var_6895, pad = var_8768_pad_0, pad_type = var_8768_pad_type_0, strides = var_8764, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_519_cast_fp16)[name = tensor("op_8768_cast_fp16")]; + tensor hidden_states_347_cast_fp16 = add(x = var_8768_cast_fp16, y = inputs_263_cast_fp16)[name = tensor("hidden_states_347_cast_fp16")]; + tensor var_8770 = const()[name = tensor("op_8770"), val = tensor([1, 1280, 32, 32])]; + tensor input_521_cast_fp16 = reshape(shape = var_8770, x = hidden_states_347_cast_fp16)[name = tensor("input_521_cast_fp16")]; + tensor var_8774 = const()[name = tensor("op_8774"), val = tensor([1, 1])]; + tensor var_8776 = const()[name = tensor("op_8776"), val = tensor([1, 1])]; + tensor hidden_states_349_pad_type_0 = const()[name = tensor("hidden_states_349_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_349_pad_0 = const()[name = tensor("hidden_states_349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1232196288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233425152))), name = tensor("up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233425344)))]; + tensor hidden_states_349_cast_fp16 = conv(bias = up_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_8776, groups = var_6895, pad = hidden_states_349_pad_0, pad_type = hidden_states_349_pad_type_0, strides = var_8774, weight = up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized, x = input_521_cast_fp16)[name = tensor("hidden_states_349_cast_fp16")]; + tensor hidden_states_351_cast_fp16 = add(x = hidden_states_349_cast_fp16, y = hidden_states_283_cast_fp16)[name = tensor("hidden_states_351_cast_fp16")]; + tensor input_523_interleave_0 = const()[name = tensor("input_523_interleave_0"), val = tensor(false)]; + tensor input_523_cast_fp16 = concat(axis = var_6895, interleave = input_523_interleave_0, values = (hidden_states_351_cast_fp16, res_hidden_states_3_cast_fp16))[name = tensor("input_523_cast_fp16")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 80, 32, 32])]; + tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_523_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; + tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; + tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; + tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; + tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 2560, 32, 32])]; + tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; + tensor add_49_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233427968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233429952))), name = tensor("add_49_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_49_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233430144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233432128))), name = tensor("add_49_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + 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_fp16 = batch_norm(beta = add_49_beta_0_to_fp16_palettized, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16_palettized, mean = add_43_mean_0_to_fp16_palettized, variance = add_43_variance_0_to_fp16_palettized, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; + tensor input_527_cast_fp16 = silu(x = add_49_cast_fp16)[name = tensor("input_527_cast_fp16")]; + tensor var_8794 = const()[name = tensor("op_8794"), val = tensor([1, 1])]; + tensor var_8796 = const()[name = tensor("op_8796"), val = tensor([1, 1])]; + tensor hidden_states_353_pad_type_0 = const()[name = tensor("hidden_states_353_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_353_pad_0 = const()[name = tensor("hidden_states_353_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(1233432320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1255550784))), 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(1255550976)))]; + tensor hidden_states_353_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_8796, groups = var_6895, pad = hidden_states_353_pad_0, pad_type = hidden_states_353_pad_type_0, strides = var_8794, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_527_cast_fp16)[name = tensor("hidden_states_353_cast_fp16")]; + tensor var_8802 = const()[name = tensor("op_8802"), val = tensor([1, 1])]; + tensor var_8804 = const()[name = tensor("op_8804"), 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 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(1255553600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256782464))), 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(1256782656)))]; + tensor temb_19_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_8804, groups = var_6895, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_8802, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_19_cast_fp16")]; + tensor input_531_cast_fp16 = add(x = hidden_states_353_cast_fp16, y = temb_19_cast_fp16)[name = tensor("input_531_cast_fp16")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = input_531_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; + tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; + tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; + tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; + tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; + 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(1256785280)))]; + 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(1256787904)))]; + 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_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; + tensor input_535_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("input_535_cast_fp16")]; + tensor var_8814 = const()[name = tensor("op_8814"), val = tensor([1, 1])]; + tensor var_8816 = const()[name = tensor("op_8816"), val = tensor([1, 1])]; + tensor hidden_states_355_pad_type_0 = const()[name = tensor("hidden_states_355_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_355_pad_0 = const()[name = tensor("hidden_states_355_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(1256790528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1267849792))), 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(1267849984)))]; + tensor hidden_states_355_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_8816, groups = var_6895, pad = hidden_states_355_pad_0, pad_type = hidden_states_355_pad_type_0, strides = var_8814, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_535_cast_fp16)[name = tensor("hidden_states_355_cast_fp16")]; + tensor var_8821 = const()[name = tensor("op_8821"), val = tensor([1, 1])]; + tensor var_8823 = const()[name = tensor("op_8823"), 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(1267852608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270310272))), 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(1270310464)))]; + tensor x_7_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_8823, groups = var_6895, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_8821, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_523_cast_fp16)[name = tensor("x_7_cast_fp16")]; + tensor hidden_states_357_cast_fp16 = add(x = x_7_cast_fp16, y = hidden_states_355_cast_fp16)[name = tensor("hidden_states_357_cast_fp16")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = hidden_states_357_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; + tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; + tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; + tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; + tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; + 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(1270313088)))]; + 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(1270315712)))]; + 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_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; + tensor var_8861 = const()[name = tensor("op_8861"), val = tensor([1, 1])]; + tensor var_8863 = const()[name = tensor("op_8863"), val = tensor([1, 1])]; + tensor hidden_states_359_pad_type_0 = const()[name = tensor("hidden_states_359_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_359_pad_0 = const()[name = tensor("hidden_states_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1270318336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271547200))), name = tensor("up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271547392)))]; + tensor hidden_states_359_cast_fp16 = conv(bias = up_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_8863, groups = var_6895, pad = hidden_states_359_pad_0, pad_type = hidden_states_359_pad_type_0, strides = var_8861, weight = up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized, x = add_53_cast_fp16)[name = tensor("hidden_states_359_cast_fp16")]; + tensor var_8868 = const()[name = tensor("op_8868"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_265_cast_fp16 = reshape(shape = var_8868, x = hidden_states_359_cast_fp16)[name = tensor("inputs_265_cast_fp16")]; + tensor hidden_states_361_axes_0 = const()[name = tensor("hidden_states_361_axes_0"), val = tensor([1])]; + tensor hidden_states_361_gamma_0_to_fp16 = const()[name = tensor("hidden_states_361_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271550016)))]; + tensor hidden_states_361_beta_0_to_fp16 = const()[name = tensor("hidden_states_361_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271552640)))]; + tensor var_8884_to_fp16 = const()[name = tensor("op_8884_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_361_cast_fp16 = layer_norm(axes = hidden_states_361_axes_0, beta = hidden_states_361_beta_0_to_fp16, epsilon = var_8884_to_fp16, gamma = hidden_states_361_gamma_0_to_fp16, x = inputs_265_cast_fp16)[name = tensor("hidden_states_361_cast_fp16")]; + tensor var_8899 = const()[name = tensor("op_8899"), val = tensor([1, 1])]; + tensor var_8901 = const()[name = tensor("op_8901"), val = tensor([1, 1])]; + tensor q_177_pad_type_0 = const()[name = tensor("q_177_pad_type_0"), val = tensor("custom")]; + tensor q_177_pad_0 = const()[name = tensor("q_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1271555264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1272784128))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_177_cast_fp16 = conv(dilations = var_8901, groups = var_6895, pad = q_177_pad_0, pad_type = q_177_pad_type_0, strides = var_8899, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_361_cast_fp16)[name = tensor("q_177_cast_fp16")]; + tensor var_8905 = const()[name = tensor("op_8905"), val = tensor([1, 1])]; + tensor var_8907 = const()[name = tensor("op_8907"), val = tensor([1, 1])]; + tensor k_177_pad_type_0 = const()[name = tensor("k_177_pad_type_0"), val = tensor("custom")]; + tensor k_177_pad_0 = const()[name = tensor("k_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1272784320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1274013184))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_177_cast_fp16 = conv(dilations = var_8907, groups = var_6895, pad = k_177_pad_0, pad_type = k_177_pad_type_0, strides = var_8905, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_361_cast_fp16)[name = tensor("k_177_cast_fp16")]; + tensor var_8911 = const()[name = tensor("op_8911"), val = tensor([1, 1])]; + tensor var_8913 = const()[name = tensor("op_8913"), val = tensor([1, 1])]; + tensor v_177_pad_type_0 = const()[name = tensor("v_177_pad_type_0"), val = tensor("custom")]; + tensor v_177_pad_0 = const()[name = tensor("v_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1274013376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1275242240))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_177_cast_fp16 = conv(dilations = var_8913, groups = var_6895, pad = v_177_pad_0, pad_type = v_177_pad_type_0, strides = var_8911, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_361_cast_fp16)[name = tensor("v_177_cast_fp16")]; + tensor var_8917 = const()[name = tensor("op_8917"), val = tensor([1, 20, 64, -1])]; + tensor var_8918_cast_fp16 = reshape(shape = var_8917, x = q_177_cast_fp16)[name = tensor("op_8918_cast_fp16")]; + tensor var_8919 = const()[name = tensor("op_8919"), val = tensor([1, 20, 64, -1])]; + tensor var_8920_cast_fp16 = reshape(shape = var_8919, x = k_177_cast_fp16)[name = tensor("op_8920_cast_fp16")]; + tensor var_8921 = const()[name = tensor("op_8921"), val = tensor([1, 20, 64, -1])]; + tensor var_8922_cast_fp16 = reshape(shape = var_8921, x = v_177_cast_fp16)[name = tensor("op_8922_cast_fp16")]; + tensor attn_weights_353_transpose_x_0 = const()[name = tensor("attn_weights_353_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_353_transpose_y_0 = const()[name = tensor("attn_weights_353_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8918_cast_fp16, y = var_8920_cast_fp16)[name = tensor("attn_weights_353_cast_fp16")]; + tensor attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_355_cast_fp16")]; + tensor var_8926_cast_fp16 = softmax(axis = var_6879, x = attn_weights_355_cast_fp16)[name = tensor("op_8926_cast_fp16")]; + tensor attn_177_transpose_x_0 = const()[name = tensor("attn_177_transpose_x_0"), val = tensor(false)]; + tensor attn_177_transpose_y_0 = const()[name = tensor("attn_177_transpose_y_0"), val = tensor(true)]; + tensor attn_177_cast_fp16 = matmul(transpose_x = attn_177_transpose_x_0, transpose_y = attn_177_transpose_y_0, x = var_8922_cast_fp16, y = var_8926_cast_fp16)[name = tensor("attn_177_cast_fp16")]; + tensor var_8930 = const()[name = tensor("op_8930"), val = tensor([1, 1280, 1, -1])]; + tensor input_539_cast_fp16 = reshape(shape = var_8930, x = attn_177_cast_fp16)[name = tensor("input_539_cast_fp16")]; + tensor var_8935 = const()[name = tensor("op_8935"), val = tensor([1, 1])]; + tensor var_8937 = const()[name = tensor("op_8937"), val = tensor([1, 1])]; + tensor var_8939_pad_type_0 = const()[name = tensor("op_8939_pad_type_0"), val = tensor("custom")]; + tensor var_8939_pad_0 = const()[name = tensor("op_8939_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1275242432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276471296))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_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(1276471488)))]; + tensor var_8939_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8937, groups = var_6895, pad = var_8939_pad_0, pad_type = var_8939_pad_type_0, strides = var_8935, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_539_cast_fp16)[name = tensor("op_8939_cast_fp16")]; + tensor inputs_267_cast_fp16 = add(x = var_8939_cast_fp16, y = inputs_265_cast_fp16)[name = tensor("inputs_267_cast_fp16")]; + tensor hidden_states_363_axes_0 = const()[name = tensor("hidden_states_363_axes_0"), val = tensor([1])]; + tensor hidden_states_363_gamma_0_to_fp16 = const()[name = tensor("hidden_states_363_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276474112)))]; + tensor hidden_states_363_beta_0_to_fp16 = const()[name = tensor("hidden_states_363_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276476736)))]; + tensor var_8949_to_fp16 = const()[name = tensor("op_8949_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_363_cast_fp16 = layer_norm(axes = hidden_states_363_axes_0, beta = hidden_states_363_beta_0_to_fp16, epsilon = var_8949_to_fp16, gamma = hidden_states_363_gamma_0_to_fp16, x = inputs_267_cast_fp16)[name = tensor("hidden_states_363_cast_fp16")]; + tensor var_8964 = const()[name = tensor("op_8964"), val = tensor([1, 1])]; + tensor var_8966 = const()[name = tensor("op_8966"), val = tensor([1, 1])]; + tensor q_179_pad_type_0 = const()[name = tensor("q_179_pad_type_0"), val = tensor("custom")]; + tensor q_179_pad_0 = const()[name = tensor("q_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1276479360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1277708224))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_179_cast_fp16 = conv(dilations = var_8966, groups = var_6895, pad = q_179_pad_0, pad_type = q_179_pad_type_0, strides = var_8964, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_363_cast_fp16)[name = tensor("q_179_cast_fp16")]; + tensor var_8970 = const()[name = tensor("op_8970"), val = tensor([1, 1])]; + tensor var_8972 = const()[name = tensor("op_8972"), val = tensor([1, 1])]; + tensor k_179_pad_type_0 = const()[name = tensor("k_179_pad_type_0"), val = tensor("custom")]; + tensor k_179_pad_0 = const()[name = tensor("k_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1277708416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1279674560))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_179_cast_fp16 = conv(dilations = var_8972, groups = var_6895, pad = k_179_pad_0, pad_type = k_179_pad_type_0, strides = var_8970, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_179_cast_fp16")]; + tensor var_8976 = const()[name = tensor("op_8976"), val = tensor([1, 1])]; + tensor var_8978 = const()[name = tensor("op_8978"), val = tensor([1, 1])]; + tensor v_179_pad_type_0 = const()[name = tensor("v_179_pad_type_0"), val = tensor("custom")]; + tensor v_179_pad_0 = const()[name = tensor("v_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1279674752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1281640896))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_179_cast_fp16 = conv(dilations = var_8978, groups = var_6895, pad = v_179_pad_0, pad_type = v_179_pad_type_0, strides = var_8976, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_179_cast_fp16")]; + tensor var_8982 = const()[name = tensor("op_8982"), val = tensor([1, 20, 64, -1])]; + tensor var_8983_cast_fp16 = reshape(shape = var_8982, x = q_179_cast_fp16)[name = tensor("op_8983_cast_fp16")]; + tensor var_8984 = const()[name = tensor("op_8984"), val = tensor([1, 20, 64, -1])]; + tensor var_8985_cast_fp16 = reshape(shape = var_8984, x = k_179_cast_fp16)[name = tensor("op_8985_cast_fp16")]; + tensor var_8986 = const()[name = tensor("op_8986"), val = tensor([1, 20, 64, -1])]; + tensor var_8987_cast_fp16 = reshape(shape = var_8986, x = v_179_cast_fp16)[name = tensor("op_8987_cast_fp16")]; + tensor attn_weights_357_transpose_x_0 = const()[name = tensor("attn_weights_357_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_357_transpose_y_0 = const()[name = tensor("attn_weights_357_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_357_cast_fp16 = matmul(transpose_x = attn_weights_357_transpose_x_0, transpose_y = attn_weights_357_transpose_y_0, x = var_8983_cast_fp16, y = var_8985_cast_fp16)[name = tensor("attn_weights_357_cast_fp16")]; + tensor attn_weights_359_cast_fp16 = mul(x = attn_weights_357_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_359_cast_fp16")]; + tensor var_8991_cast_fp16 = softmax(axis = var_6879, x = attn_weights_359_cast_fp16)[name = tensor("op_8991_cast_fp16")]; + tensor attn_179_transpose_x_0 = const()[name = tensor("attn_179_transpose_x_0"), val = tensor(false)]; + tensor attn_179_transpose_y_0 = const()[name = tensor("attn_179_transpose_y_0"), val = tensor(true)]; + tensor attn_179_cast_fp16 = matmul(transpose_x = attn_179_transpose_x_0, transpose_y = attn_179_transpose_y_0, x = var_8987_cast_fp16, y = var_8991_cast_fp16)[name = tensor("attn_179_cast_fp16")]; + tensor var_8995 = const()[name = tensor("op_8995"), val = tensor([1, 1280, 1, -1])]; + tensor input_541_cast_fp16 = reshape(shape = var_8995, x = attn_179_cast_fp16)[name = tensor("input_541_cast_fp16")]; + tensor var_9000 = const()[name = tensor("op_9000"), val = tensor([1, 1])]; + tensor var_9002 = const()[name = tensor("op_9002"), val = tensor([1, 1])]; + tensor var_9004_pad_type_0 = const()[name = tensor("op_9004_pad_type_0"), val = tensor("custom")]; + tensor var_9004_pad_0 = const()[name = tensor("op_9004_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1281641088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282869952))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_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(1282870144)))]; + tensor var_9004_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_9002, groups = var_6895, pad = var_9004_pad_0, pad_type = var_9004_pad_type_0, strides = var_9000, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_541_cast_fp16)[name = tensor("op_9004_cast_fp16")]; + tensor inputs_269_cast_fp16 = add(x = var_9004_cast_fp16, y = inputs_267_cast_fp16)[name = tensor("inputs_269_cast_fp16")]; + tensor input_543_axes_0 = const()[name = tensor("input_543_axes_0"), val = tensor([1])]; + tensor input_543_gamma_0_to_fp16 = const()[name = tensor("input_543_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282872768)))]; + tensor input_543_beta_0_to_fp16 = const()[name = tensor("input_543_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282875392)))]; + tensor var_9014_to_fp16 = const()[name = tensor("op_9014_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_543_cast_fp16 = layer_norm(axes = input_543_axes_0, beta = input_543_beta_0_to_fp16, epsilon = var_9014_to_fp16, gamma = input_543_gamma_0_to_fp16, x = inputs_269_cast_fp16)[name = tensor("input_543_cast_fp16")]; + tensor var_9030 = const()[name = tensor("op_9030"), val = tensor([1, 1])]; + tensor var_9032 = const()[name = tensor("op_9032"), val = tensor([1, 1])]; + tensor var_9034_pad_type_0 = const()[name = tensor("op_9034_pad_type_0"), val = tensor("custom")]; + tensor var_9034_pad_0 = const()[name = tensor("op_9034_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1282878016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1292708480))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_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(1292708672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1292716416))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9034_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9032, groups = var_6895, pad = var_9034_pad_0, pad_type = var_9034_pad_type_0, strides = var_9030, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_543_cast_fp16)[name = tensor("op_9034_cast_fp16")]; + tensor var_9035_split_sizes_0 = const()[name = tensor("op_9035_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9035_axis_0 = const()[name = tensor("op_9035_axis_0"), val = tensor(1)]; + tensor var_9035_cast_fp16_0, tensor var_9035_cast_fp16_1 = split(axis = var_9035_axis_0, split_sizes = var_9035_split_sizes_0, x = var_9034_cast_fp16)[name = tensor("op_9035_cast_fp16")]; + tensor var_9037_mode_0 = const()[name = tensor("op_9037_mode_0"), val = tensor("EXACT")]; + tensor var_9037_cast_fp16 = gelu(mode = var_9037_mode_0, x = var_9035_cast_fp16_1)[name = tensor("op_9037_cast_fp16")]; + tensor input_545_cast_fp16 = mul(x = var_9035_cast_fp16_0, y = var_9037_cast_fp16)[name = tensor("input_545_cast_fp16")]; + tensor var_9041 = const()[name = tensor("op_9041"), val = tensor([1, 1])]; + tensor var_9043 = const()[name = tensor("op_9043"), val = tensor([1, 1])]; + tensor var_9045_pad_type_0 = const()[name = tensor("op_9045_pad_type_0"), val = tensor("custom")]; + tensor var_9045_pad_0 = const()[name = tensor("op_9045_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1292716608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297631872))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_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(1297632064)))]; + tensor var_9045_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_9043, groups = var_6895, pad = var_9045_pad_0, pad_type = var_9045_pad_type_0, strides = var_9041, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_545_cast_fp16)[name = tensor("op_9045_cast_fp16")]; + tensor inputs_271_cast_fp16 = add(x = var_9045_cast_fp16, y = inputs_269_cast_fp16)[name = tensor("inputs_271_cast_fp16")]; + tensor hidden_states_367_axes_0 = const()[name = tensor("hidden_states_367_axes_0"), val = tensor([1])]; + tensor hidden_states_367_gamma_0_to_fp16 = const()[name = tensor("hidden_states_367_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297634688)))]; + tensor hidden_states_367_beta_0_to_fp16 = const()[name = tensor("hidden_states_367_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297637312)))]; + tensor var_9061_to_fp16 = const()[name = tensor("op_9061_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_367_cast_fp16 = layer_norm(axes = hidden_states_367_axes_0, beta = hidden_states_367_beta_0_to_fp16, epsilon = var_9061_to_fp16, gamma = hidden_states_367_gamma_0_to_fp16, x = inputs_271_cast_fp16)[name = tensor("hidden_states_367_cast_fp16")]; + tensor var_9076 = const()[name = tensor("op_9076"), val = tensor([1, 1])]; + tensor var_9078 = const()[name = tensor("op_9078"), val = tensor([1, 1])]; + tensor q_181_pad_type_0 = const()[name = tensor("q_181_pad_type_0"), val = tensor("custom")]; + tensor q_181_pad_0 = const()[name = tensor("q_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297639936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298868800))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_181_cast_fp16 = conv(dilations = var_9078, groups = var_6895, pad = q_181_pad_0, pad_type = q_181_pad_type_0, strides = var_9076, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_367_cast_fp16)[name = tensor("q_181_cast_fp16")]; + tensor var_9082 = const()[name = tensor("op_9082"), val = tensor([1, 1])]; + tensor var_9084 = const()[name = tensor("op_9084"), val = tensor([1, 1])]; + tensor k_181_pad_type_0 = const()[name = tensor("k_181_pad_type_0"), val = tensor("custom")]; + tensor k_181_pad_0 = const()[name = tensor("k_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298868992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1300097856))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_181_cast_fp16 = conv(dilations = var_9084, groups = var_6895, pad = k_181_pad_0, pad_type = k_181_pad_type_0, strides = var_9082, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_367_cast_fp16)[name = tensor("k_181_cast_fp16")]; + tensor var_9088 = const()[name = tensor("op_9088"), val = tensor([1, 1])]; + tensor var_9090 = const()[name = tensor("op_9090"), val = tensor([1, 1])]; + tensor v_181_pad_type_0 = const()[name = tensor("v_181_pad_type_0"), val = tensor("custom")]; + tensor v_181_pad_0 = const()[name = tensor("v_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1300098048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1301326912))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_181_cast_fp16 = conv(dilations = var_9090, groups = var_6895, pad = v_181_pad_0, pad_type = v_181_pad_type_0, strides = var_9088, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_367_cast_fp16)[name = tensor("v_181_cast_fp16")]; + tensor var_9094 = const()[name = tensor("op_9094"), val = tensor([1, 20, 64, -1])]; + tensor var_9095_cast_fp16 = reshape(shape = var_9094, x = q_181_cast_fp16)[name = tensor("op_9095_cast_fp16")]; + tensor var_9096 = const()[name = tensor("op_9096"), val = tensor([1, 20, 64, -1])]; + tensor var_9097_cast_fp16 = reshape(shape = var_9096, x = k_181_cast_fp16)[name = tensor("op_9097_cast_fp16")]; + tensor var_9098 = const()[name = tensor("op_9098"), val = tensor([1, 20, 64, -1])]; + tensor var_9099_cast_fp16 = reshape(shape = var_9098, x = v_181_cast_fp16)[name = tensor("op_9099_cast_fp16")]; + tensor attn_weights_361_transpose_x_0 = const()[name = tensor("attn_weights_361_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_361_transpose_y_0 = const()[name = tensor("attn_weights_361_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_9095_cast_fp16, y = var_9097_cast_fp16)[name = tensor("attn_weights_361_cast_fp16")]; + tensor attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_363_cast_fp16")]; + tensor var_9103_cast_fp16 = softmax(axis = var_6879, x = attn_weights_363_cast_fp16)[name = tensor("op_9103_cast_fp16")]; + tensor attn_181_transpose_x_0 = const()[name = tensor("attn_181_transpose_x_0"), val = tensor(false)]; + tensor attn_181_transpose_y_0 = const()[name = tensor("attn_181_transpose_y_0"), val = tensor(true)]; + tensor attn_181_cast_fp16 = matmul(transpose_x = attn_181_transpose_x_0, transpose_y = attn_181_transpose_y_0, x = var_9099_cast_fp16, y = var_9103_cast_fp16)[name = tensor("attn_181_cast_fp16")]; + tensor var_9107 = const()[name = tensor("op_9107"), val = tensor([1, 1280, 1, -1])]; + tensor input_547_cast_fp16 = reshape(shape = var_9107, x = attn_181_cast_fp16)[name = tensor("input_547_cast_fp16")]; + tensor var_9112 = const()[name = tensor("op_9112"), val = tensor([1, 1])]; + tensor var_9114 = const()[name = tensor("op_9114"), val = tensor([1, 1])]; + tensor var_9116_pad_type_0 = const()[name = tensor("op_9116_pad_type_0"), val = tensor("custom")]; + tensor var_9116_pad_0 = const()[name = tensor("op_9116_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1301327104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302555968))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302556160)))]; + tensor var_9116_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_9114, groups = var_6895, pad = var_9116_pad_0, pad_type = var_9116_pad_type_0, strides = var_9112, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_547_cast_fp16)[name = tensor("op_9116_cast_fp16")]; + tensor inputs_273_cast_fp16 = add(x = var_9116_cast_fp16, y = inputs_271_cast_fp16)[name = tensor("inputs_273_cast_fp16")]; + tensor hidden_states_369_axes_0 = const()[name = tensor("hidden_states_369_axes_0"), val = tensor([1])]; + tensor hidden_states_369_gamma_0_to_fp16 = const()[name = tensor("hidden_states_369_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302558784)))]; + tensor hidden_states_369_beta_0_to_fp16 = const()[name = tensor("hidden_states_369_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302561408)))]; + tensor var_9126_to_fp16 = const()[name = tensor("op_9126_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_369_cast_fp16 = layer_norm(axes = hidden_states_369_axes_0, beta = hidden_states_369_beta_0_to_fp16, epsilon = var_9126_to_fp16, gamma = hidden_states_369_gamma_0_to_fp16, x = inputs_273_cast_fp16)[name = tensor("hidden_states_369_cast_fp16")]; + tensor var_9141 = const()[name = tensor("op_9141"), val = tensor([1, 1])]; + tensor var_9143 = const()[name = tensor("op_9143"), val = tensor([1, 1])]; + tensor q_183_pad_type_0 = const()[name = tensor("q_183_pad_type_0"), val = tensor("custom")]; + tensor q_183_pad_0 = const()[name = tensor("q_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302564032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1303792896))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_183_cast_fp16 = conv(dilations = var_9143, groups = var_6895, pad = q_183_pad_0, pad_type = q_183_pad_type_0, strides = var_9141, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_369_cast_fp16)[name = tensor("q_183_cast_fp16")]; + tensor var_9147 = const()[name = tensor("op_9147"), val = tensor([1, 1])]; + tensor var_9149 = const()[name = tensor("op_9149"), val = tensor([1, 1])]; + tensor k_183_pad_type_0 = const()[name = tensor("k_183_pad_type_0"), val = tensor("custom")]; + tensor k_183_pad_0 = const()[name = tensor("k_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1303793088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305759232))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_183_cast_fp16 = conv(dilations = var_9149, groups = var_6895, pad = k_183_pad_0, pad_type = k_183_pad_type_0, strides = var_9147, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_183_cast_fp16")]; + tensor var_9153 = const()[name = tensor("op_9153"), val = tensor([1, 1])]; + tensor var_9155 = const()[name = tensor("op_9155"), val = tensor([1, 1])]; + tensor v_183_pad_type_0 = const()[name = tensor("v_183_pad_type_0"), val = tensor("custom")]; + tensor v_183_pad_0 = const()[name = tensor("v_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305759424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307725568))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_183_cast_fp16 = conv(dilations = var_9155, groups = var_6895, pad = v_183_pad_0, pad_type = v_183_pad_type_0, strides = var_9153, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_183_cast_fp16")]; + tensor var_9159 = const()[name = tensor("op_9159"), val = tensor([1, 20, 64, -1])]; + tensor var_9160_cast_fp16 = reshape(shape = var_9159, x = q_183_cast_fp16)[name = tensor("op_9160_cast_fp16")]; + tensor var_9161 = const()[name = tensor("op_9161"), val = tensor([1, 20, 64, -1])]; + tensor var_9162_cast_fp16 = reshape(shape = var_9161, x = k_183_cast_fp16)[name = tensor("op_9162_cast_fp16")]; + tensor var_9163 = const()[name = tensor("op_9163"), val = tensor([1, 20, 64, -1])]; + tensor var_9164_cast_fp16 = reshape(shape = var_9163, x = v_183_cast_fp16)[name = tensor("op_9164_cast_fp16")]; + tensor attn_weights_365_transpose_x_0 = const()[name = tensor("attn_weights_365_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_365_transpose_y_0 = const()[name = tensor("attn_weights_365_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_365_cast_fp16 = matmul(transpose_x = attn_weights_365_transpose_x_0, transpose_y = attn_weights_365_transpose_y_0, x = var_9160_cast_fp16, y = var_9162_cast_fp16)[name = tensor("attn_weights_365_cast_fp16")]; + tensor attn_weights_367_cast_fp16 = mul(x = attn_weights_365_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_367_cast_fp16")]; + tensor var_9168_cast_fp16 = softmax(axis = var_6879, x = attn_weights_367_cast_fp16)[name = tensor("op_9168_cast_fp16")]; + tensor attn_183_transpose_x_0 = const()[name = tensor("attn_183_transpose_x_0"), val = tensor(false)]; + tensor attn_183_transpose_y_0 = const()[name = tensor("attn_183_transpose_y_0"), val = tensor(true)]; + tensor attn_183_cast_fp16 = matmul(transpose_x = attn_183_transpose_x_0, transpose_y = attn_183_transpose_y_0, x = var_9164_cast_fp16, y = var_9168_cast_fp16)[name = tensor("attn_183_cast_fp16")]; + tensor var_9172 = const()[name = tensor("op_9172"), val = tensor([1, 1280, 1, -1])]; + tensor input_549_cast_fp16 = reshape(shape = var_9172, x = attn_183_cast_fp16)[name = tensor("input_549_cast_fp16")]; + tensor var_9177 = const()[name = tensor("op_9177"), val = tensor([1, 1])]; + tensor var_9179 = const()[name = tensor("op_9179"), val = tensor([1, 1])]; + tensor var_9181_pad_type_0 = const()[name = tensor("op_9181_pad_type_0"), val = tensor("custom")]; + tensor var_9181_pad_0 = const()[name = tensor("op_9181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307725760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308954624))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308954816)))]; + tensor var_9181_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_9179, groups = var_6895, pad = var_9181_pad_0, pad_type = var_9181_pad_type_0, strides = var_9177, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_549_cast_fp16)[name = tensor("op_9181_cast_fp16")]; + tensor inputs_275_cast_fp16 = add(x = var_9181_cast_fp16, y = inputs_273_cast_fp16)[name = tensor("inputs_275_cast_fp16")]; + tensor input_551_axes_0 = const()[name = tensor("input_551_axes_0"), val = tensor([1])]; + tensor input_551_gamma_0_to_fp16 = const()[name = tensor("input_551_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308957440)))]; + tensor input_551_beta_0_to_fp16 = const()[name = tensor("input_551_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308960064)))]; + tensor var_9191_to_fp16 = const()[name = tensor("op_9191_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_551_cast_fp16 = layer_norm(axes = input_551_axes_0, beta = input_551_beta_0_to_fp16, epsilon = var_9191_to_fp16, gamma = input_551_gamma_0_to_fp16, x = inputs_275_cast_fp16)[name = tensor("input_551_cast_fp16")]; + tensor var_9207 = const()[name = tensor("op_9207"), val = tensor([1, 1])]; + tensor var_9209 = const()[name = tensor("op_9209"), val = tensor([1, 1])]; + tensor var_9211_pad_type_0 = const()[name = tensor("op_9211_pad_type_0"), val = tensor("custom")]; + tensor var_9211_pad_0 = const()[name = tensor("op_9211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308962688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318793152))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318793344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318801088))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9211_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9209, groups = var_6895, pad = var_9211_pad_0, pad_type = var_9211_pad_type_0, strides = var_9207, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_551_cast_fp16)[name = tensor("op_9211_cast_fp16")]; + tensor var_9212_split_sizes_0 = const()[name = tensor("op_9212_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9212_axis_0 = const()[name = tensor("op_9212_axis_0"), val = tensor(1)]; + tensor var_9212_cast_fp16_0, tensor var_9212_cast_fp16_1 = split(axis = var_9212_axis_0, split_sizes = var_9212_split_sizes_0, x = var_9211_cast_fp16)[name = tensor("op_9212_cast_fp16")]; + tensor var_9214_mode_0 = const()[name = tensor("op_9214_mode_0"), val = tensor("EXACT")]; + tensor var_9214_cast_fp16 = gelu(mode = var_9214_mode_0, x = var_9212_cast_fp16_1)[name = tensor("op_9214_cast_fp16")]; + tensor input_553_cast_fp16 = mul(x = var_9212_cast_fp16_0, y = var_9214_cast_fp16)[name = tensor("input_553_cast_fp16")]; + tensor var_9218 = const()[name = tensor("op_9218"), val = tensor([1, 1])]; + tensor var_9220 = const()[name = tensor("op_9220"), val = tensor([1, 1])]; + tensor var_9222_pad_type_0 = const()[name = tensor("op_9222_pad_type_0"), val = tensor("custom")]; + tensor var_9222_pad_0 = const()[name = tensor("op_9222_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318801280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323716544))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323716736)))]; + tensor var_9222_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_9220, groups = var_6895, pad = var_9222_pad_0, pad_type = var_9222_pad_type_0, strides = var_9218, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_553_cast_fp16)[name = tensor("op_9222_cast_fp16")]; + tensor inputs_277_cast_fp16 = add(x = var_9222_cast_fp16, y = inputs_275_cast_fp16)[name = tensor("inputs_277_cast_fp16")]; + tensor hidden_states_373_axes_0 = const()[name = tensor("hidden_states_373_axes_0"), val = tensor([1])]; + tensor hidden_states_373_gamma_0_to_fp16 = const()[name = tensor("hidden_states_373_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323719360)))]; + tensor hidden_states_373_beta_0_to_fp16 = const()[name = tensor("hidden_states_373_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323721984)))]; + tensor var_9238_to_fp16 = const()[name = tensor("op_9238_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_373_cast_fp16 = layer_norm(axes = hidden_states_373_axes_0, beta = hidden_states_373_beta_0_to_fp16, epsilon = var_9238_to_fp16, gamma = hidden_states_373_gamma_0_to_fp16, x = inputs_277_cast_fp16)[name = tensor("hidden_states_373_cast_fp16")]; + tensor var_9253 = const()[name = tensor("op_9253"), val = tensor([1, 1])]; + tensor var_9255 = const()[name = tensor("op_9255"), val = tensor([1, 1])]; + tensor q_185_pad_type_0 = const()[name = tensor("q_185_pad_type_0"), val = tensor("custom")]; + tensor q_185_pad_0 = const()[name = tensor("q_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323724608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1324953472))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_185_cast_fp16 = conv(dilations = var_9255, groups = var_6895, pad = q_185_pad_0, pad_type = q_185_pad_type_0, strides = var_9253, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_373_cast_fp16)[name = tensor("q_185_cast_fp16")]; + tensor var_9259 = const()[name = tensor("op_9259"), val = tensor([1, 1])]; + tensor var_9261 = const()[name = tensor("op_9261"), val = tensor([1, 1])]; + tensor k_185_pad_type_0 = const()[name = tensor("k_185_pad_type_0"), val = tensor("custom")]; + tensor k_185_pad_0 = const()[name = tensor("k_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1324953664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1326182528))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_185_cast_fp16 = conv(dilations = var_9261, groups = var_6895, pad = k_185_pad_0, pad_type = k_185_pad_type_0, strides = var_9259, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_373_cast_fp16)[name = tensor("k_185_cast_fp16")]; + tensor var_9265 = const()[name = tensor("op_9265"), val = tensor([1, 1])]; + tensor var_9267 = const()[name = tensor("op_9267"), val = tensor([1, 1])]; + tensor v_185_pad_type_0 = const()[name = tensor("v_185_pad_type_0"), val = tensor("custom")]; + tensor v_185_pad_0 = const()[name = tensor("v_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1326182720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1327411584))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_185_cast_fp16 = conv(dilations = var_9267, groups = var_6895, pad = v_185_pad_0, pad_type = v_185_pad_type_0, strides = var_9265, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_373_cast_fp16)[name = tensor("v_185_cast_fp16")]; + tensor var_9271 = const()[name = tensor("op_9271"), val = tensor([1, 20, 64, -1])]; + tensor var_9272_cast_fp16 = reshape(shape = var_9271, x = q_185_cast_fp16)[name = tensor("op_9272_cast_fp16")]; + tensor var_9273 = const()[name = tensor("op_9273"), val = tensor([1, 20, 64, -1])]; + tensor var_9274_cast_fp16 = reshape(shape = var_9273, x = k_185_cast_fp16)[name = tensor("op_9274_cast_fp16")]; + tensor var_9275 = const()[name = tensor("op_9275"), val = tensor([1, 20, 64, -1])]; + tensor var_9276_cast_fp16 = reshape(shape = var_9275, x = v_185_cast_fp16)[name = tensor("op_9276_cast_fp16")]; + tensor attn_weights_369_transpose_x_0 = const()[name = tensor("attn_weights_369_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_369_transpose_y_0 = const()[name = tensor("attn_weights_369_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_9272_cast_fp16, y = var_9274_cast_fp16)[name = tensor("attn_weights_369_cast_fp16")]; + tensor attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_371_cast_fp16")]; + tensor var_9280_cast_fp16 = softmax(axis = var_6879, x = attn_weights_371_cast_fp16)[name = tensor("op_9280_cast_fp16")]; + tensor attn_185_transpose_x_0 = const()[name = tensor("attn_185_transpose_x_0"), val = tensor(false)]; + tensor attn_185_transpose_y_0 = const()[name = tensor("attn_185_transpose_y_0"), val = tensor(true)]; + tensor attn_185_cast_fp16 = matmul(transpose_x = attn_185_transpose_x_0, transpose_y = attn_185_transpose_y_0, x = var_9276_cast_fp16, y = var_9280_cast_fp16)[name = tensor("attn_185_cast_fp16")]; + tensor var_9284 = const()[name = tensor("op_9284"), val = tensor([1, 1280, 1, -1])]; + tensor input_555_cast_fp16 = reshape(shape = var_9284, x = attn_185_cast_fp16)[name = tensor("input_555_cast_fp16")]; + tensor var_9289 = const()[name = tensor("op_9289"), val = tensor([1, 1])]; + tensor var_9291 = const()[name = tensor("op_9291"), val = tensor([1, 1])]; + tensor var_9293_pad_type_0 = const()[name = tensor("op_9293_pad_type_0"), val = tensor("custom")]; + tensor var_9293_pad_0 = const()[name = tensor("op_9293_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1327411776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328640640))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328640832)))]; + tensor var_9293_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_9291, groups = var_6895, pad = var_9293_pad_0, pad_type = var_9293_pad_type_0, strides = var_9289, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_555_cast_fp16)[name = tensor("op_9293_cast_fp16")]; + tensor inputs_279_cast_fp16 = add(x = var_9293_cast_fp16, y = inputs_277_cast_fp16)[name = tensor("inputs_279_cast_fp16")]; + tensor hidden_states_375_axes_0 = const()[name = tensor("hidden_states_375_axes_0"), val = tensor([1])]; + tensor hidden_states_375_gamma_0_to_fp16 = const()[name = tensor("hidden_states_375_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328643456)))]; + tensor hidden_states_375_beta_0_to_fp16 = const()[name = tensor("hidden_states_375_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328646080)))]; + tensor var_9303_to_fp16 = const()[name = tensor("op_9303_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_375_cast_fp16 = layer_norm(axes = hidden_states_375_axes_0, beta = hidden_states_375_beta_0_to_fp16, epsilon = var_9303_to_fp16, gamma = hidden_states_375_gamma_0_to_fp16, x = inputs_279_cast_fp16)[name = tensor("hidden_states_375_cast_fp16")]; + tensor var_9318 = const()[name = tensor("op_9318"), val = tensor([1, 1])]; + tensor var_9320 = const()[name = tensor("op_9320"), val = tensor([1, 1])]; + tensor q_187_pad_type_0 = const()[name = tensor("q_187_pad_type_0"), val = tensor("custom")]; + tensor q_187_pad_0 = const()[name = tensor("q_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328648704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1329877568))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_187_cast_fp16 = conv(dilations = var_9320, groups = var_6895, pad = q_187_pad_0, pad_type = q_187_pad_type_0, strides = var_9318, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_375_cast_fp16)[name = tensor("q_187_cast_fp16")]; + tensor var_9324 = const()[name = tensor("op_9324"), val = tensor([1, 1])]; + tensor var_9326 = const()[name = tensor("op_9326"), val = tensor([1, 1])]; + tensor k_187_pad_type_0 = const()[name = tensor("k_187_pad_type_0"), val = tensor("custom")]; + tensor k_187_pad_0 = const()[name = tensor("k_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1329877760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1331843904))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_187_cast_fp16 = conv(dilations = var_9326, groups = var_6895, pad = k_187_pad_0, pad_type = k_187_pad_type_0, strides = var_9324, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_187_cast_fp16")]; + tensor var_9330 = const()[name = tensor("op_9330"), val = tensor([1, 1])]; + tensor var_9332 = const()[name = tensor("op_9332"), val = tensor([1, 1])]; + tensor v_187_pad_type_0 = const()[name = tensor("v_187_pad_type_0"), val = tensor("custom")]; + tensor v_187_pad_0 = const()[name = tensor("v_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1331844096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1333810240))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_187_cast_fp16 = conv(dilations = var_9332, groups = var_6895, pad = v_187_pad_0, pad_type = v_187_pad_type_0, strides = var_9330, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_187_cast_fp16")]; + tensor var_9336 = const()[name = tensor("op_9336"), val = tensor([1, 20, 64, -1])]; + tensor var_9337_cast_fp16 = reshape(shape = var_9336, x = q_187_cast_fp16)[name = tensor("op_9337_cast_fp16")]; + tensor var_9338 = const()[name = tensor("op_9338"), val = tensor([1, 20, 64, -1])]; + tensor var_9339_cast_fp16 = reshape(shape = var_9338, x = k_187_cast_fp16)[name = tensor("op_9339_cast_fp16")]; + tensor var_9340 = const()[name = tensor("op_9340"), val = tensor([1, 20, 64, -1])]; + tensor var_9341_cast_fp16 = reshape(shape = var_9340, x = v_187_cast_fp16)[name = tensor("op_9341_cast_fp16")]; + tensor attn_weights_373_transpose_x_0 = const()[name = tensor("attn_weights_373_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_373_transpose_y_0 = const()[name = tensor("attn_weights_373_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_373_cast_fp16 = matmul(transpose_x = attn_weights_373_transpose_x_0, transpose_y = attn_weights_373_transpose_y_0, x = var_9337_cast_fp16, y = var_9339_cast_fp16)[name = tensor("attn_weights_373_cast_fp16")]; + tensor attn_weights_375_cast_fp16 = mul(x = attn_weights_373_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_375_cast_fp16")]; + tensor var_9345_cast_fp16 = softmax(axis = var_6879, x = attn_weights_375_cast_fp16)[name = tensor("op_9345_cast_fp16")]; + tensor attn_187_transpose_x_0 = const()[name = tensor("attn_187_transpose_x_0"), val = tensor(false)]; + tensor attn_187_transpose_y_0 = const()[name = tensor("attn_187_transpose_y_0"), val = tensor(true)]; + tensor attn_187_cast_fp16 = matmul(transpose_x = attn_187_transpose_x_0, transpose_y = attn_187_transpose_y_0, x = var_9341_cast_fp16, y = var_9345_cast_fp16)[name = tensor("attn_187_cast_fp16")]; + tensor var_9349 = const()[name = tensor("op_9349"), val = tensor([1, 1280, 1, -1])]; + tensor input_557_cast_fp16 = reshape(shape = var_9349, x = attn_187_cast_fp16)[name = tensor("input_557_cast_fp16")]; + tensor var_9354 = const()[name = tensor("op_9354"), val = tensor([1, 1])]; + tensor var_9356 = const()[name = tensor("op_9356"), val = tensor([1, 1])]; + tensor var_9358_pad_type_0 = const()[name = tensor("op_9358_pad_type_0"), val = tensor("custom")]; + tensor var_9358_pad_0 = const()[name = tensor("op_9358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1333810432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335039296))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335039488)))]; + tensor var_9358_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_9356, groups = var_6895, pad = var_9358_pad_0, pad_type = var_9358_pad_type_0, strides = var_9354, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_557_cast_fp16)[name = tensor("op_9358_cast_fp16")]; + tensor inputs_281_cast_fp16 = add(x = var_9358_cast_fp16, y = inputs_279_cast_fp16)[name = tensor("inputs_281_cast_fp16")]; + tensor input_559_axes_0 = const()[name = tensor("input_559_axes_0"), val = tensor([1])]; + tensor input_559_gamma_0_to_fp16 = const()[name = tensor("input_559_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335042112)))]; + tensor input_559_beta_0_to_fp16 = const()[name = tensor("input_559_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335044736)))]; + tensor var_9368_to_fp16 = const()[name = tensor("op_9368_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_559_cast_fp16 = layer_norm(axes = input_559_axes_0, beta = input_559_beta_0_to_fp16, epsilon = var_9368_to_fp16, gamma = input_559_gamma_0_to_fp16, x = inputs_281_cast_fp16)[name = tensor("input_559_cast_fp16")]; + tensor var_9384 = const()[name = tensor("op_9384"), val = tensor([1, 1])]; + tensor var_9386 = const()[name = tensor("op_9386"), val = tensor([1, 1])]; + tensor var_9388_pad_type_0 = const()[name = tensor("op_9388_pad_type_0"), val = tensor("custom")]; + tensor var_9388_pad_0 = const()[name = tensor("op_9388_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335047360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344877824))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344878016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344885760))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9388_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9386, groups = var_6895, pad = var_9388_pad_0, pad_type = var_9388_pad_type_0, strides = var_9384, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = tensor("op_9388_cast_fp16")]; + tensor var_9389_split_sizes_0 = const()[name = tensor("op_9389_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9389_axis_0 = const()[name = tensor("op_9389_axis_0"), val = tensor(1)]; + tensor var_9389_cast_fp16_0, tensor var_9389_cast_fp16_1 = split(axis = var_9389_axis_0, split_sizes = var_9389_split_sizes_0, x = var_9388_cast_fp16)[name = tensor("op_9389_cast_fp16")]; + tensor var_9391_mode_0 = const()[name = tensor("op_9391_mode_0"), val = tensor("EXACT")]; + tensor var_9391_cast_fp16 = gelu(mode = var_9391_mode_0, x = var_9389_cast_fp16_1)[name = tensor("op_9391_cast_fp16")]; + tensor input_561_cast_fp16 = mul(x = var_9389_cast_fp16_0, y = var_9391_cast_fp16)[name = tensor("input_561_cast_fp16")]; + tensor var_9395 = const()[name = tensor("op_9395"), val = tensor([1, 1])]; + tensor var_9397 = const()[name = tensor("op_9397"), val = tensor([1, 1])]; + tensor var_9399_pad_type_0 = const()[name = tensor("op_9399_pad_type_0"), val = tensor("custom")]; + tensor var_9399_pad_0 = const()[name = tensor("op_9399_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344885952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349801216))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349801408)))]; + tensor var_9399_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_9397, groups = var_6895, pad = var_9399_pad_0, pad_type = var_9399_pad_type_0, strides = var_9395, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_561_cast_fp16)[name = tensor("op_9399_cast_fp16")]; + tensor inputs_283_cast_fp16 = add(x = var_9399_cast_fp16, y = inputs_281_cast_fp16)[name = tensor("inputs_283_cast_fp16")]; + tensor hidden_states_379_axes_0 = const()[name = tensor("hidden_states_379_axes_0"), val = tensor([1])]; + tensor hidden_states_379_gamma_0_to_fp16 = const()[name = tensor("hidden_states_379_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349804032)))]; + tensor hidden_states_379_beta_0_to_fp16 = const()[name = tensor("hidden_states_379_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349806656)))]; + tensor var_9415_to_fp16 = const()[name = tensor("op_9415_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_379_cast_fp16 = layer_norm(axes = hidden_states_379_axes_0, beta = hidden_states_379_beta_0_to_fp16, epsilon = var_9415_to_fp16, gamma = hidden_states_379_gamma_0_to_fp16, x = inputs_283_cast_fp16)[name = tensor("hidden_states_379_cast_fp16")]; + tensor var_9430 = const()[name = tensor("op_9430"), val = tensor([1, 1])]; + tensor var_9432 = const()[name = tensor("op_9432"), val = tensor([1, 1])]; + tensor q_189_pad_type_0 = const()[name = tensor("q_189_pad_type_0"), val = tensor("custom")]; + tensor q_189_pad_0 = const()[name = tensor("q_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349809280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1351038144))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_189_cast_fp16 = conv(dilations = var_9432, groups = var_6895, pad = q_189_pad_0, pad_type = q_189_pad_type_0, strides = var_9430, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_379_cast_fp16)[name = tensor("q_189_cast_fp16")]; + tensor var_9436 = const()[name = tensor("op_9436"), val = tensor([1, 1])]; + tensor var_9438 = const()[name = tensor("op_9438"), val = tensor([1, 1])]; + tensor k_189_pad_type_0 = const()[name = tensor("k_189_pad_type_0"), val = tensor("custom")]; + tensor k_189_pad_0 = const()[name = tensor("k_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1351038336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1352267200))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_189_cast_fp16 = conv(dilations = var_9438, groups = var_6895, pad = k_189_pad_0, pad_type = k_189_pad_type_0, strides = var_9436, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_379_cast_fp16)[name = tensor("k_189_cast_fp16")]; + tensor var_9442 = const()[name = tensor("op_9442"), val = tensor([1, 1])]; + tensor var_9444 = const()[name = tensor("op_9444"), val = tensor([1, 1])]; + tensor v_189_pad_type_0 = const()[name = tensor("v_189_pad_type_0"), val = tensor("custom")]; + tensor v_189_pad_0 = const()[name = tensor("v_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1352267392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1353496256))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_189_cast_fp16 = conv(dilations = var_9444, groups = var_6895, pad = v_189_pad_0, pad_type = v_189_pad_type_0, strides = var_9442, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_379_cast_fp16)[name = tensor("v_189_cast_fp16")]; + tensor var_9448 = const()[name = tensor("op_9448"), val = tensor([1, 20, 64, -1])]; + tensor var_9449_cast_fp16 = reshape(shape = var_9448, x = q_189_cast_fp16)[name = tensor("op_9449_cast_fp16")]; + tensor var_9450 = const()[name = tensor("op_9450"), val = tensor([1, 20, 64, -1])]; + tensor var_9451_cast_fp16 = reshape(shape = var_9450, x = k_189_cast_fp16)[name = tensor("op_9451_cast_fp16")]; + tensor var_9452 = const()[name = tensor("op_9452"), val = tensor([1, 20, 64, -1])]; + tensor var_9453_cast_fp16 = reshape(shape = var_9452, x = v_189_cast_fp16)[name = tensor("op_9453_cast_fp16")]; + tensor attn_weights_377_transpose_x_0 = const()[name = tensor("attn_weights_377_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_377_transpose_y_0 = const()[name = tensor("attn_weights_377_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_9449_cast_fp16, y = var_9451_cast_fp16)[name = tensor("attn_weights_377_cast_fp16")]; + tensor attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_379_cast_fp16")]; + tensor var_9457_cast_fp16 = softmax(axis = var_6879, x = attn_weights_379_cast_fp16)[name = tensor("op_9457_cast_fp16")]; + tensor attn_189_transpose_x_0 = const()[name = tensor("attn_189_transpose_x_0"), val = tensor(false)]; + tensor attn_189_transpose_y_0 = const()[name = tensor("attn_189_transpose_y_0"), val = tensor(true)]; + tensor attn_189_cast_fp16 = matmul(transpose_x = attn_189_transpose_x_0, transpose_y = attn_189_transpose_y_0, x = var_9453_cast_fp16, y = var_9457_cast_fp16)[name = tensor("attn_189_cast_fp16")]; + tensor var_9461 = const()[name = tensor("op_9461"), val = tensor([1, 1280, 1, -1])]; + tensor input_563_cast_fp16 = reshape(shape = var_9461, x = attn_189_cast_fp16)[name = tensor("input_563_cast_fp16")]; + tensor var_9466 = const()[name = tensor("op_9466"), val = tensor([1, 1])]; + tensor var_9468 = const()[name = tensor("op_9468"), val = tensor([1, 1])]; + tensor var_9470_pad_type_0 = const()[name = tensor("op_9470_pad_type_0"), val = tensor("custom")]; + tensor var_9470_pad_0 = const()[name = tensor("op_9470_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1353496448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354725312))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354725504)))]; + tensor var_9470_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_9468, groups = var_6895, pad = var_9470_pad_0, pad_type = var_9470_pad_type_0, strides = var_9466, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = tensor("op_9470_cast_fp16")]; + tensor inputs_285_cast_fp16 = add(x = var_9470_cast_fp16, y = inputs_283_cast_fp16)[name = tensor("inputs_285_cast_fp16")]; + tensor hidden_states_381_axes_0 = const()[name = tensor("hidden_states_381_axes_0"), val = tensor([1])]; + tensor hidden_states_381_gamma_0_to_fp16 = const()[name = tensor("hidden_states_381_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354728128)))]; + tensor hidden_states_381_beta_0_to_fp16 = const()[name = tensor("hidden_states_381_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354730752)))]; + tensor var_9480_to_fp16 = const()[name = tensor("op_9480_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_381_cast_fp16 = layer_norm(axes = hidden_states_381_axes_0, beta = hidden_states_381_beta_0_to_fp16, epsilon = var_9480_to_fp16, gamma = hidden_states_381_gamma_0_to_fp16, x = inputs_285_cast_fp16)[name = tensor("hidden_states_381_cast_fp16")]; + tensor var_9495 = const()[name = tensor("op_9495"), val = tensor([1, 1])]; + tensor var_9497 = const()[name = tensor("op_9497"), val = tensor([1, 1])]; + tensor q_191_pad_type_0 = const()[name = tensor("q_191_pad_type_0"), val = tensor("custom")]; + tensor q_191_pad_0 = const()[name = tensor("q_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354733376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1355962240))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_191_cast_fp16 = conv(dilations = var_9497, groups = var_6895, pad = q_191_pad_0, pad_type = q_191_pad_type_0, strides = var_9495, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_381_cast_fp16)[name = tensor("q_191_cast_fp16")]; + tensor var_9501 = const()[name = tensor("op_9501"), val = tensor([1, 1])]; + tensor var_9503 = const()[name = tensor("op_9503"), val = tensor([1, 1])]; + tensor k_191_pad_type_0 = const()[name = tensor("k_191_pad_type_0"), val = tensor("custom")]; + tensor k_191_pad_0 = const()[name = tensor("k_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1355962432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1357928576))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_191_cast_fp16 = conv(dilations = var_9503, groups = var_6895, pad = k_191_pad_0, pad_type = k_191_pad_type_0, strides = var_9501, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_191_cast_fp16")]; + tensor var_9507 = const()[name = tensor("op_9507"), val = tensor([1, 1])]; + tensor var_9509 = const()[name = tensor("op_9509"), val = tensor([1, 1])]; + tensor v_191_pad_type_0 = const()[name = tensor("v_191_pad_type_0"), val = tensor("custom")]; + tensor v_191_pad_0 = const()[name = tensor("v_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1357928768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359894912))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_191_cast_fp16 = conv(dilations = var_9509, groups = var_6895, pad = v_191_pad_0, pad_type = v_191_pad_type_0, strides = var_9507, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_191_cast_fp16")]; + tensor var_9513 = const()[name = tensor("op_9513"), val = tensor([1, 20, 64, -1])]; + tensor var_9514_cast_fp16 = reshape(shape = var_9513, x = q_191_cast_fp16)[name = tensor("op_9514_cast_fp16")]; + tensor var_9515 = const()[name = tensor("op_9515"), val = tensor([1, 20, 64, -1])]; + tensor var_9516_cast_fp16 = reshape(shape = var_9515, x = k_191_cast_fp16)[name = tensor("op_9516_cast_fp16")]; + tensor var_9517 = const()[name = tensor("op_9517"), val = tensor([1, 20, 64, -1])]; + tensor var_9518_cast_fp16 = reshape(shape = var_9517, x = v_191_cast_fp16)[name = tensor("op_9518_cast_fp16")]; + tensor attn_weights_381_transpose_x_0 = const()[name = tensor("attn_weights_381_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_381_transpose_y_0 = const()[name = tensor("attn_weights_381_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_381_cast_fp16 = matmul(transpose_x = attn_weights_381_transpose_x_0, transpose_y = attn_weights_381_transpose_y_0, x = var_9514_cast_fp16, y = var_9516_cast_fp16)[name = tensor("attn_weights_381_cast_fp16")]; + tensor attn_weights_383_cast_fp16 = mul(x = attn_weights_381_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_383_cast_fp16")]; + tensor var_9522_cast_fp16 = softmax(axis = var_6879, x = attn_weights_383_cast_fp16)[name = tensor("op_9522_cast_fp16")]; + tensor attn_191_transpose_x_0 = const()[name = tensor("attn_191_transpose_x_0"), val = tensor(false)]; + tensor attn_191_transpose_y_0 = const()[name = tensor("attn_191_transpose_y_0"), val = tensor(true)]; + tensor attn_191_cast_fp16 = matmul(transpose_x = attn_191_transpose_x_0, transpose_y = attn_191_transpose_y_0, x = var_9518_cast_fp16, y = var_9522_cast_fp16)[name = tensor("attn_191_cast_fp16")]; + tensor var_9526 = const()[name = tensor("op_9526"), val = tensor([1, 1280, 1, -1])]; + tensor input_565_cast_fp16 = reshape(shape = var_9526, x = attn_191_cast_fp16)[name = tensor("input_565_cast_fp16")]; + tensor var_9531 = const()[name = tensor("op_9531"), val = tensor([1, 1])]; + tensor var_9533 = const()[name = tensor("op_9533"), val = tensor([1, 1])]; + tensor var_9535_pad_type_0 = const()[name = tensor("op_9535_pad_type_0"), val = tensor("custom")]; + tensor var_9535_pad_0 = const()[name = tensor("op_9535_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359895104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361123968))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361124160)))]; + tensor var_9535_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_9533, groups = var_6895, pad = var_9535_pad_0, pad_type = var_9535_pad_type_0, strides = var_9531, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_565_cast_fp16)[name = tensor("op_9535_cast_fp16")]; + tensor inputs_287_cast_fp16 = add(x = var_9535_cast_fp16, y = inputs_285_cast_fp16)[name = tensor("inputs_287_cast_fp16")]; + tensor input_567_axes_0 = const()[name = tensor("input_567_axes_0"), val = tensor([1])]; + tensor input_567_gamma_0_to_fp16 = const()[name = tensor("input_567_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361126784)))]; + tensor input_567_beta_0_to_fp16 = const()[name = tensor("input_567_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361129408)))]; + tensor var_9545_to_fp16 = const()[name = tensor("op_9545_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_567_cast_fp16 = layer_norm(axes = input_567_axes_0, beta = input_567_beta_0_to_fp16, epsilon = var_9545_to_fp16, gamma = input_567_gamma_0_to_fp16, x = inputs_287_cast_fp16)[name = tensor("input_567_cast_fp16")]; + tensor var_9561 = const()[name = tensor("op_9561"), val = tensor([1, 1])]; + tensor var_9563 = const()[name = tensor("op_9563"), val = tensor([1, 1])]; + tensor var_9565_pad_type_0 = const()[name = tensor("op_9565_pad_type_0"), val = tensor("custom")]; + tensor var_9565_pad_0 = const()[name = tensor("op_9565_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361132032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370962496))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370962688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370970432))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9565_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9563, groups = var_6895, pad = var_9565_pad_0, pad_type = var_9565_pad_type_0, strides = var_9561, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_567_cast_fp16)[name = tensor("op_9565_cast_fp16")]; + tensor var_9566_split_sizes_0 = const()[name = tensor("op_9566_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9566_axis_0 = const()[name = tensor("op_9566_axis_0"), val = tensor(1)]; + tensor var_9566_cast_fp16_0, tensor var_9566_cast_fp16_1 = split(axis = var_9566_axis_0, split_sizes = var_9566_split_sizes_0, x = var_9565_cast_fp16)[name = tensor("op_9566_cast_fp16")]; + tensor var_9568_mode_0 = const()[name = tensor("op_9568_mode_0"), val = tensor("EXACT")]; + tensor var_9568_cast_fp16 = gelu(mode = var_9568_mode_0, x = var_9566_cast_fp16_1)[name = tensor("op_9568_cast_fp16")]; + tensor input_569_cast_fp16 = mul(x = var_9566_cast_fp16_0, y = var_9568_cast_fp16)[name = tensor("input_569_cast_fp16")]; + tensor var_9572 = const()[name = tensor("op_9572"), val = tensor([1, 1])]; + tensor var_9574 = const()[name = tensor("op_9574"), val = tensor([1, 1])]; + tensor var_9576_pad_type_0 = const()[name = tensor("op_9576_pad_type_0"), val = tensor("custom")]; + tensor var_9576_pad_0 = const()[name = tensor("op_9576_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370970624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375885888))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375886080)))]; + tensor var_9576_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_9574, groups = var_6895, pad = var_9576_pad_0, pad_type = var_9576_pad_type_0, strides = var_9572, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_569_cast_fp16)[name = tensor("op_9576_cast_fp16")]; + tensor inputs_289_cast_fp16 = add(x = var_9576_cast_fp16, y = inputs_287_cast_fp16)[name = tensor("inputs_289_cast_fp16")]; + tensor hidden_states_385_axes_0 = const()[name = tensor("hidden_states_385_axes_0"), val = tensor([1])]; + tensor hidden_states_385_gamma_0_to_fp16 = const()[name = tensor("hidden_states_385_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375888704)))]; + tensor hidden_states_385_beta_0_to_fp16 = const()[name = tensor("hidden_states_385_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375891328)))]; + tensor var_9592_to_fp16 = const()[name = tensor("op_9592_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_385_cast_fp16 = layer_norm(axes = hidden_states_385_axes_0, beta = hidden_states_385_beta_0_to_fp16, epsilon = var_9592_to_fp16, gamma = hidden_states_385_gamma_0_to_fp16, x = inputs_289_cast_fp16)[name = tensor("hidden_states_385_cast_fp16")]; + tensor var_9607 = const()[name = tensor("op_9607"), val = tensor([1, 1])]; + tensor var_9609 = const()[name = tensor("op_9609"), val = tensor([1, 1])]; + tensor q_193_pad_type_0 = const()[name = tensor("q_193_pad_type_0"), val = tensor("custom")]; + tensor q_193_pad_0 = const()[name = tensor("q_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375893952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1377122816))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_193_cast_fp16 = conv(dilations = var_9609, groups = var_6895, pad = q_193_pad_0, pad_type = q_193_pad_type_0, strides = var_9607, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_385_cast_fp16)[name = tensor("q_193_cast_fp16")]; + tensor var_9613 = const()[name = tensor("op_9613"), val = tensor([1, 1])]; + tensor var_9615 = const()[name = tensor("op_9615"), val = tensor([1, 1])]; + tensor k_193_pad_type_0 = const()[name = tensor("k_193_pad_type_0"), val = tensor("custom")]; + tensor k_193_pad_0 = const()[name = tensor("k_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1377123008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1378351872))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_193_cast_fp16 = conv(dilations = var_9615, groups = var_6895, pad = k_193_pad_0, pad_type = k_193_pad_type_0, strides = var_9613, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_385_cast_fp16)[name = tensor("k_193_cast_fp16")]; + tensor var_9619 = const()[name = tensor("op_9619"), val = tensor([1, 1])]; + tensor var_9621 = const()[name = tensor("op_9621"), val = tensor([1, 1])]; + tensor v_193_pad_type_0 = const()[name = tensor("v_193_pad_type_0"), val = tensor("custom")]; + tensor v_193_pad_0 = const()[name = tensor("v_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1378352064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379580928))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_193_cast_fp16 = conv(dilations = var_9621, groups = var_6895, pad = v_193_pad_0, pad_type = v_193_pad_type_0, strides = var_9619, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_385_cast_fp16)[name = tensor("v_193_cast_fp16")]; + tensor var_9625 = const()[name = tensor("op_9625"), val = tensor([1, 20, 64, -1])]; + tensor var_9626_cast_fp16 = reshape(shape = var_9625, x = q_193_cast_fp16)[name = tensor("op_9626_cast_fp16")]; + tensor var_9627 = const()[name = tensor("op_9627"), val = tensor([1, 20, 64, -1])]; + tensor var_9628_cast_fp16 = reshape(shape = var_9627, x = k_193_cast_fp16)[name = tensor("op_9628_cast_fp16")]; + tensor var_9629 = const()[name = tensor("op_9629"), val = tensor([1, 20, 64, -1])]; + tensor var_9630_cast_fp16 = reshape(shape = var_9629, x = v_193_cast_fp16)[name = tensor("op_9630_cast_fp16")]; + tensor attn_weights_385_transpose_x_0 = const()[name = tensor("attn_weights_385_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_385_transpose_y_0 = const()[name = tensor("attn_weights_385_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_9626_cast_fp16, y = var_9628_cast_fp16)[name = tensor("attn_weights_385_cast_fp16")]; + tensor attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_387_cast_fp16")]; + tensor var_9634_cast_fp16 = softmax(axis = var_6879, x = attn_weights_387_cast_fp16)[name = tensor("op_9634_cast_fp16")]; + tensor attn_193_transpose_x_0 = const()[name = tensor("attn_193_transpose_x_0"), val = tensor(false)]; + tensor attn_193_transpose_y_0 = const()[name = tensor("attn_193_transpose_y_0"), val = tensor(true)]; + tensor attn_193_cast_fp16 = matmul(transpose_x = attn_193_transpose_x_0, transpose_y = attn_193_transpose_y_0, x = var_9630_cast_fp16, y = var_9634_cast_fp16)[name = tensor("attn_193_cast_fp16")]; + tensor var_9638 = const()[name = tensor("op_9638"), val = tensor([1, 1280, 1, -1])]; + tensor input_571_cast_fp16 = reshape(shape = var_9638, x = attn_193_cast_fp16)[name = tensor("input_571_cast_fp16")]; + tensor var_9643 = const()[name = tensor("op_9643"), val = tensor([1, 1])]; + tensor var_9645 = const()[name = tensor("op_9645"), val = tensor([1, 1])]; + tensor var_9647_pad_type_0 = const()[name = tensor("op_9647_pad_type_0"), val = tensor("custom")]; + tensor var_9647_pad_0 = const()[name = tensor("op_9647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379581120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380809984))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380810176)))]; + tensor var_9647_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_9645, groups = var_6895, pad = var_9647_pad_0, pad_type = var_9647_pad_type_0, strides = var_9643, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_571_cast_fp16)[name = tensor("op_9647_cast_fp16")]; + tensor inputs_291_cast_fp16 = add(x = var_9647_cast_fp16, y = inputs_289_cast_fp16)[name = tensor("inputs_291_cast_fp16")]; + tensor hidden_states_387_axes_0 = const()[name = tensor("hidden_states_387_axes_0"), val = tensor([1])]; + tensor hidden_states_387_gamma_0_to_fp16 = const()[name = tensor("hidden_states_387_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380812800)))]; + tensor hidden_states_387_beta_0_to_fp16 = const()[name = tensor("hidden_states_387_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380815424)))]; + tensor var_9657_to_fp16 = const()[name = tensor("op_9657_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_387_cast_fp16 = layer_norm(axes = hidden_states_387_axes_0, beta = hidden_states_387_beta_0_to_fp16, epsilon = var_9657_to_fp16, gamma = hidden_states_387_gamma_0_to_fp16, x = inputs_291_cast_fp16)[name = tensor("hidden_states_387_cast_fp16")]; + tensor var_9672 = const()[name = tensor("op_9672"), val = tensor([1, 1])]; + tensor var_9674 = const()[name = tensor("op_9674"), val = tensor([1, 1])]; + tensor q_195_pad_type_0 = const()[name = tensor("q_195_pad_type_0"), val = tensor("custom")]; + tensor q_195_pad_0 = const()[name = tensor("q_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380818048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1382046912))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_195_cast_fp16 = conv(dilations = var_9674, groups = var_6895, pad = q_195_pad_0, pad_type = q_195_pad_type_0, strides = var_9672, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_387_cast_fp16)[name = tensor("q_195_cast_fp16")]; + tensor var_9678 = const()[name = tensor("op_9678"), val = tensor([1, 1])]; + tensor var_9680 = const()[name = tensor("op_9680"), val = tensor([1, 1])]; + tensor k_195_pad_type_0 = const()[name = tensor("k_195_pad_type_0"), val = tensor("custom")]; + tensor k_195_pad_0 = const()[name = tensor("k_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1382047104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1384013248))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_195_cast_fp16 = conv(dilations = var_9680, groups = var_6895, pad = k_195_pad_0, pad_type = k_195_pad_type_0, strides = var_9678, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_195_cast_fp16")]; + tensor var_9684 = const()[name = tensor("op_9684"), val = tensor([1, 1])]; + tensor var_9686 = const()[name = tensor("op_9686"), val = tensor([1, 1])]; + tensor v_195_pad_type_0 = const()[name = tensor("v_195_pad_type_0"), val = tensor("custom")]; + tensor v_195_pad_0 = const()[name = tensor("v_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1384013440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1385979584))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_195_cast_fp16 = conv(dilations = var_9686, groups = var_6895, pad = v_195_pad_0, pad_type = v_195_pad_type_0, strides = var_9684, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_195_cast_fp16")]; + tensor var_9690 = const()[name = tensor("op_9690"), val = tensor([1, 20, 64, -1])]; + tensor var_9691_cast_fp16 = reshape(shape = var_9690, x = q_195_cast_fp16)[name = tensor("op_9691_cast_fp16")]; + tensor var_9692 = const()[name = tensor("op_9692"), val = tensor([1, 20, 64, -1])]; + tensor var_9693_cast_fp16 = reshape(shape = var_9692, x = k_195_cast_fp16)[name = tensor("op_9693_cast_fp16")]; + tensor var_9694 = const()[name = tensor("op_9694"), val = tensor([1, 20, 64, -1])]; + tensor var_9695_cast_fp16 = reshape(shape = var_9694, x = v_195_cast_fp16)[name = tensor("op_9695_cast_fp16")]; + tensor attn_weights_389_transpose_x_0 = const()[name = tensor("attn_weights_389_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_389_transpose_y_0 = const()[name = tensor("attn_weights_389_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_389_cast_fp16 = matmul(transpose_x = attn_weights_389_transpose_x_0, transpose_y = attn_weights_389_transpose_y_0, x = var_9691_cast_fp16, y = var_9693_cast_fp16)[name = tensor("attn_weights_389_cast_fp16")]; + tensor attn_weights_391_cast_fp16 = mul(x = attn_weights_389_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_391_cast_fp16")]; + tensor var_9699_cast_fp16 = softmax(axis = var_6879, x = attn_weights_391_cast_fp16)[name = tensor("op_9699_cast_fp16")]; + tensor attn_195_transpose_x_0 = const()[name = tensor("attn_195_transpose_x_0"), val = tensor(false)]; + tensor attn_195_transpose_y_0 = const()[name = tensor("attn_195_transpose_y_0"), val = tensor(true)]; + tensor attn_195_cast_fp16 = matmul(transpose_x = attn_195_transpose_x_0, transpose_y = attn_195_transpose_y_0, x = var_9695_cast_fp16, y = var_9699_cast_fp16)[name = tensor("attn_195_cast_fp16")]; + tensor var_9703 = const()[name = tensor("op_9703"), val = tensor([1, 1280, 1, -1])]; + tensor input_573_cast_fp16 = reshape(shape = var_9703, x = attn_195_cast_fp16)[name = tensor("input_573_cast_fp16")]; + tensor var_9708 = const()[name = tensor("op_9708"), val = tensor([1, 1])]; + tensor var_9710 = const()[name = tensor("op_9710"), val = tensor([1, 1])]; + tensor var_9712_pad_type_0 = const()[name = tensor("op_9712_pad_type_0"), val = tensor("custom")]; + tensor var_9712_pad_0 = const()[name = tensor("op_9712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1385979776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387208640))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387208832)))]; + tensor var_9712_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_9710, groups = var_6895, pad = var_9712_pad_0, pad_type = var_9712_pad_type_0, strides = var_9708, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_573_cast_fp16)[name = tensor("op_9712_cast_fp16")]; + tensor inputs_293_cast_fp16 = add(x = var_9712_cast_fp16, y = inputs_291_cast_fp16)[name = tensor("inputs_293_cast_fp16")]; + tensor input_575_axes_0 = const()[name = tensor("input_575_axes_0"), val = tensor([1])]; + tensor input_575_gamma_0_to_fp16 = const()[name = tensor("input_575_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387211456)))]; + tensor input_575_beta_0_to_fp16 = const()[name = tensor("input_575_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387214080)))]; + tensor var_9722_to_fp16 = const()[name = tensor("op_9722_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_575_cast_fp16 = layer_norm(axes = input_575_axes_0, beta = input_575_beta_0_to_fp16, epsilon = var_9722_to_fp16, gamma = input_575_gamma_0_to_fp16, x = inputs_293_cast_fp16)[name = tensor("input_575_cast_fp16")]; + tensor var_9738 = const()[name = tensor("op_9738"), val = tensor([1, 1])]; + tensor var_9740 = const()[name = tensor("op_9740"), val = tensor([1, 1])]; + tensor var_9742_pad_type_0 = const()[name = tensor("op_9742_pad_type_0"), val = tensor("custom")]; + tensor var_9742_pad_0 = const()[name = tensor("op_9742_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387216704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397047168))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397047360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397055104))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9742_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9740, groups = var_6895, pad = var_9742_pad_0, pad_type = var_9742_pad_type_0, strides = var_9738, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_575_cast_fp16)[name = tensor("op_9742_cast_fp16")]; + tensor var_9743_split_sizes_0 = const()[name = tensor("op_9743_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9743_axis_0 = const()[name = tensor("op_9743_axis_0"), val = tensor(1)]; + tensor var_9743_cast_fp16_0, tensor var_9743_cast_fp16_1 = split(axis = var_9743_axis_0, split_sizes = var_9743_split_sizes_0, x = var_9742_cast_fp16)[name = tensor("op_9743_cast_fp16")]; + tensor var_9745_mode_0 = const()[name = tensor("op_9745_mode_0"), val = tensor("EXACT")]; + tensor var_9745_cast_fp16 = gelu(mode = var_9745_mode_0, x = var_9743_cast_fp16_1)[name = tensor("op_9745_cast_fp16")]; + tensor input_577_cast_fp16 = mul(x = var_9743_cast_fp16_0, y = var_9745_cast_fp16)[name = tensor("input_577_cast_fp16")]; + tensor var_9749 = const()[name = tensor("op_9749"), val = tensor([1, 1])]; + tensor var_9751 = const()[name = tensor("op_9751"), val = tensor([1, 1])]; + tensor var_9753_pad_type_0 = const()[name = tensor("op_9753_pad_type_0"), val = tensor("custom")]; + tensor var_9753_pad_0 = const()[name = tensor("op_9753_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397055296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401970560))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401970752)))]; + tensor var_9753_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_9751, groups = var_6895, pad = var_9753_pad_0, pad_type = var_9753_pad_type_0, strides = var_9749, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_577_cast_fp16)[name = tensor("op_9753_cast_fp16")]; + tensor inputs_295_cast_fp16 = add(x = var_9753_cast_fp16, y = inputs_293_cast_fp16)[name = tensor("inputs_295_cast_fp16")]; + tensor hidden_states_391_axes_0 = const()[name = tensor("hidden_states_391_axes_0"), val = tensor([1])]; + tensor hidden_states_391_gamma_0_to_fp16 = const()[name = tensor("hidden_states_391_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401973376)))]; + tensor hidden_states_391_beta_0_to_fp16 = const()[name = tensor("hidden_states_391_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401976000)))]; + tensor var_9769_to_fp16 = const()[name = tensor("op_9769_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_391_cast_fp16 = layer_norm(axes = hidden_states_391_axes_0, beta = hidden_states_391_beta_0_to_fp16, epsilon = var_9769_to_fp16, gamma = hidden_states_391_gamma_0_to_fp16, x = inputs_295_cast_fp16)[name = tensor("hidden_states_391_cast_fp16")]; + tensor var_9784 = const()[name = tensor("op_9784"), val = tensor([1, 1])]; + tensor var_9786 = const()[name = tensor("op_9786"), val = tensor([1, 1])]; + tensor q_197_pad_type_0 = const()[name = tensor("q_197_pad_type_0"), val = tensor("custom")]; + tensor q_197_pad_0 = const()[name = tensor("q_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401978624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1403207488))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_197_cast_fp16 = conv(dilations = var_9786, groups = var_6895, pad = q_197_pad_0, pad_type = q_197_pad_type_0, strides = var_9784, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_391_cast_fp16)[name = tensor("q_197_cast_fp16")]; + tensor var_9790 = const()[name = tensor("op_9790"), val = tensor([1, 1])]; + tensor var_9792 = const()[name = tensor("op_9792"), val = tensor([1, 1])]; + tensor k_197_pad_type_0 = const()[name = tensor("k_197_pad_type_0"), val = tensor("custom")]; + tensor k_197_pad_0 = const()[name = tensor("k_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1403207680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1404436544))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_197_cast_fp16 = conv(dilations = var_9792, groups = var_6895, pad = k_197_pad_0, pad_type = k_197_pad_type_0, strides = var_9790, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_391_cast_fp16)[name = tensor("k_197_cast_fp16")]; + tensor var_9796 = const()[name = tensor("op_9796"), val = tensor([1, 1])]; + tensor var_9798 = const()[name = tensor("op_9798"), val = tensor([1, 1])]; + tensor v_197_pad_type_0 = const()[name = tensor("v_197_pad_type_0"), val = tensor("custom")]; + tensor v_197_pad_0 = const()[name = tensor("v_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1404436736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1405665600))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_197_cast_fp16 = conv(dilations = var_9798, groups = var_6895, pad = v_197_pad_0, pad_type = v_197_pad_type_0, strides = var_9796, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_391_cast_fp16)[name = tensor("v_197_cast_fp16")]; + tensor var_9802 = const()[name = tensor("op_9802"), val = tensor([1, 20, 64, -1])]; + tensor var_9803_cast_fp16 = reshape(shape = var_9802, x = q_197_cast_fp16)[name = tensor("op_9803_cast_fp16")]; + tensor var_9804 = const()[name = tensor("op_9804"), val = tensor([1, 20, 64, -1])]; + tensor var_9805_cast_fp16 = reshape(shape = var_9804, x = k_197_cast_fp16)[name = tensor("op_9805_cast_fp16")]; + tensor var_9806 = const()[name = tensor("op_9806"), val = tensor([1, 20, 64, -1])]; + tensor var_9807_cast_fp16 = reshape(shape = var_9806, x = v_197_cast_fp16)[name = tensor("op_9807_cast_fp16")]; + tensor attn_weights_393_transpose_x_0 = const()[name = tensor("attn_weights_393_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_393_transpose_y_0 = const()[name = tensor("attn_weights_393_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_9803_cast_fp16, y = var_9805_cast_fp16)[name = tensor("attn_weights_393_cast_fp16")]; + tensor attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_395_cast_fp16")]; + tensor var_9811_cast_fp16 = softmax(axis = var_6879, x = attn_weights_395_cast_fp16)[name = tensor("op_9811_cast_fp16")]; + tensor attn_197_transpose_x_0 = const()[name = tensor("attn_197_transpose_x_0"), val = tensor(false)]; + tensor attn_197_transpose_y_0 = const()[name = tensor("attn_197_transpose_y_0"), val = tensor(true)]; + tensor attn_197_cast_fp16 = matmul(transpose_x = attn_197_transpose_x_0, transpose_y = attn_197_transpose_y_0, x = var_9807_cast_fp16, y = var_9811_cast_fp16)[name = tensor("attn_197_cast_fp16")]; + tensor var_9815 = const()[name = tensor("op_9815"), val = tensor([1, 1280, 1, -1])]; + tensor input_579_cast_fp16 = reshape(shape = var_9815, x = attn_197_cast_fp16)[name = tensor("input_579_cast_fp16")]; + tensor var_9820 = const()[name = tensor("op_9820"), val = tensor([1, 1])]; + tensor var_9822 = const()[name = tensor("op_9822"), val = tensor([1, 1])]; + tensor var_9824_pad_type_0 = const()[name = tensor("op_9824_pad_type_0"), val = tensor("custom")]; + tensor var_9824_pad_0 = const()[name = tensor("op_9824_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1405665792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406894656))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406894848)))]; + tensor var_9824_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_9822, groups = var_6895, pad = var_9824_pad_0, pad_type = var_9824_pad_type_0, strides = var_9820, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_579_cast_fp16)[name = tensor("op_9824_cast_fp16")]; + tensor inputs_297_cast_fp16 = add(x = var_9824_cast_fp16, y = inputs_295_cast_fp16)[name = tensor("inputs_297_cast_fp16")]; + tensor hidden_states_393_axes_0 = const()[name = tensor("hidden_states_393_axes_0"), val = tensor([1])]; + tensor hidden_states_393_gamma_0_to_fp16 = const()[name = tensor("hidden_states_393_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406897472)))]; + tensor hidden_states_393_beta_0_to_fp16 = const()[name = tensor("hidden_states_393_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406900096)))]; + tensor var_9834_to_fp16 = const()[name = tensor("op_9834_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_393_cast_fp16 = layer_norm(axes = hidden_states_393_axes_0, beta = hidden_states_393_beta_0_to_fp16, epsilon = var_9834_to_fp16, gamma = hidden_states_393_gamma_0_to_fp16, x = inputs_297_cast_fp16)[name = tensor("hidden_states_393_cast_fp16")]; + tensor var_9849 = const()[name = tensor("op_9849"), val = tensor([1, 1])]; + tensor var_9851 = const()[name = tensor("op_9851"), val = tensor([1, 1])]; + tensor q_199_pad_type_0 = const()[name = tensor("q_199_pad_type_0"), val = tensor("custom")]; + tensor q_199_pad_0 = const()[name = tensor("q_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406902720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1408131584))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_199_cast_fp16 = conv(dilations = var_9851, groups = var_6895, pad = q_199_pad_0, pad_type = q_199_pad_type_0, strides = var_9849, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_393_cast_fp16)[name = tensor("q_199_cast_fp16")]; + tensor var_9855 = const()[name = tensor("op_9855"), val = tensor([1, 1])]; + tensor var_9857 = const()[name = tensor("op_9857"), val = tensor([1, 1])]; + tensor k_199_pad_type_0 = const()[name = tensor("k_199_pad_type_0"), val = tensor("custom")]; + tensor k_199_pad_0 = const()[name = tensor("k_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1408131776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1410097920))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_199_cast_fp16 = conv(dilations = var_9857, groups = var_6895, pad = k_199_pad_0, pad_type = k_199_pad_type_0, strides = var_9855, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_199_cast_fp16")]; + tensor var_9861 = const()[name = tensor("op_9861"), val = tensor([1, 1])]; + tensor var_9863 = const()[name = tensor("op_9863"), val = tensor([1, 1])]; + tensor v_199_pad_type_0 = const()[name = tensor("v_199_pad_type_0"), val = tensor("custom")]; + tensor v_199_pad_0 = const()[name = tensor("v_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1410098112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1412064256))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_199_cast_fp16 = conv(dilations = var_9863, groups = var_6895, pad = v_199_pad_0, pad_type = v_199_pad_type_0, strides = var_9861, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_199_cast_fp16")]; + tensor var_9867 = const()[name = tensor("op_9867"), val = tensor([1, 20, 64, -1])]; + tensor var_9868_cast_fp16 = reshape(shape = var_9867, x = q_199_cast_fp16)[name = tensor("op_9868_cast_fp16")]; + tensor var_9869 = const()[name = tensor("op_9869"), val = tensor([1, 20, 64, -1])]; + tensor var_9870_cast_fp16 = reshape(shape = var_9869, x = k_199_cast_fp16)[name = tensor("op_9870_cast_fp16")]; + tensor var_9871 = const()[name = tensor("op_9871"), val = tensor([1, 20, 64, -1])]; + tensor var_9872_cast_fp16 = reshape(shape = var_9871, x = v_199_cast_fp16)[name = tensor("op_9872_cast_fp16")]; + tensor attn_weights_397_transpose_x_0 = const()[name = tensor("attn_weights_397_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_397_transpose_y_0 = const()[name = tensor("attn_weights_397_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_397_cast_fp16 = matmul(transpose_x = attn_weights_397_transpose_x_0, transpose_y = attn_weights_397_transpose_y_0, x = var_9868_cast_fp16, y = var_9870_cast_fp16)[name = tensor("attn_weights_397_cast_fp16")]; + tensor attn_weights_399_cast_fp16 = mul(x = attn_weights_397_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_399_cast_fp16")]; + tensor var_9876_cast_fp16 = softmax(axis = var_6879, x = attn_weights_399_cast_fp16)[name = tensor("op_9876_cast_fp16")]; + tensor attn_199_transpose_x_0 = const()[name = tensor("attn_199_transpose_x_0"), val = tensor(false)]; + tensor attn_199_transpose_y_0 = const()[name = tensor("attn_199_transpose_y_0"), val = tensor(true)]; + tensor attn_199_cast_fp16 = matmul(transpose_x = attn_199_transpose_x_0, transpose_y = attn_199_transpose_y_0, x = var_9872_cast_fp16, y = var_9876_cast_fp16)[name = tensor("attn_199_cast_fp16")]; + tensor var_9880 = const()[name = tensor("op_9880"), val = tensor([1, 1280, 1, -1])]; + tensor input_581_cast_fp16 = reshape(shape = var_9880, x = attn_199_cast_fp16)[name = tensor("input_581_cast_fp16")]; + tensor var_9885 = const()[name = tensor("op_9885"), val = tensor([1, 1])]; + tensor var_9887 = const()[name = tensor("op_9887"), val = tensor([1, 1])]; + tensor var_9889_pad_type_0 = const()[name = tensor("op_9889_pad_type_0"), val = tensor("custom")]; + tensor var_9889_pad_0 = const()[name = tensor("op_9889_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1412064448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413293312))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413293504)))]; + tensor var_9889_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_9887, groups = var_6895, pad = var_9889_pad_0, pad_type = var_9889_pad_type_0, strides = var_9885, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_581_cast_fp16)[name = tensor("op_9889_cast_fp16")]; + tensor inputs_299_cast_fp16 = add(x = var_9889_cast_fp16, y = inputs_297_cast_fp16)[name = tensor("inputs_299_cast_fp16")]; + tensor input_583_axes_0 = const()[name = tensor("input_583_axes_0"), val = tensor([1])]; + tensor input_583_gamma_0_to_fp16 = const()[name = tensor("input_583_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413296128)))]; + tensor input_583_beta_0_to_fp16 = const()[name = tensor("input_583_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413298752)))]; + tensor var_9899_to_fp16 = const()[name = tensor("op_9899_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_583_cast_fp16 = layer_norm(axes = input_583_axes_0, beta = input_583_beta_0_to_fp16, epsilon = var_9899_to_fp16, gamma = input_583_gamma_0_to_fp16, x = inputs_299_cast_fp16)[name = tensor("input_583_cast_fp16")]; + tensor var_9915 = const()[name = tensor("op_9915"), val = tensor([1, 1])]; + tensor var_9917 = const()[name = tensor("op_9917"), val = tensor([1, 1])]; + tensor var_9919_pad_type_0 = const()[name = tensor("op_9919_pad_type_0"), val = tensor("custom")]; + tensor var_9919_pad_0 = const()[name = tensor("op_9919_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413301376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423131840))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423132032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423139776))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9919_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9917, groups = var_6895, pad = var_9919_pad_0, pad_type = var_9919_pad_type_0, strides = var_9915, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_583_cast_fp16)[name = tensor("op_9919_cast_fp16")]; + tensor var_9920_split_sizes_0 = const()[name = tensor("op_9920_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9920_axis_0 = const()[name = tensor("op_9920_axis_0"), val = tensor(1)]; + tensor var_9920_cast_fp16_0, tensor var_9920_cast_fp16_1 = split(axis = var_9920_axis_0, split_sizes = var_9920_split_sizes_0, x = var_9919_cast_fp16)[name = tensor("op_9920_cast_fp16")]; + tensor var_9922_mode_0 = const()[name = tensor("op_9922_mode_0"), val = tensor("EXACT")]; + tensor var_9922_cast_fp16 = gelu(mode = var_9922_mode_0, x = var_9920_cast_fp16_1)[name = tensor("op_9922_cast_fp16")]; + tensor input_585_cast_fp16 = mul(x = var_9920_cast_fp16_0, y = var_9922_cast_fp16)[name = tensor("input_585_cast_fp16")]; + tensor var_9926 = const()[name = tensor("op_9926"), val = tensor([1, 1])]; + tensor var_9928 = const()[name = tensor("op_9928"), val = tensor([1, 1])]; + tensor var_9930_pad_type_0 = const()[name = tensor("op_9930_pad_type_0"), val = tensor("custom")]; + tensor var_9930_pad_0 = const()[name = tensor("op_9930_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423139968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428055232))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428055424)))]; + tensor var_9930_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_9928, groups = var_6895, pad = var_9930_pad_0, pad_type = var_9930_pad_type_0, strides = var_9926, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_585_cast_fp16)[name = tensor("op_9930_cast_fp16")]; + tensor inputs_301_cast_fp16 = add(x = var_9930_cast_fp16, y = inputs_299_cast_fp16)[name = tensor("inputs_301_cast_fp16")]; + tensor hidden_states_397_axes_0 = const()[name = tensor("hidden_states_397_axes_0"), val = tensor([1])]; + tensor hidden_states_397_gamma_0_to_fp16 = const()[name = tensor("hidden_states_397_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428058048)))]; + tensor hidden_states_397_beta_0_to_fp16 = const()[name = tensor("hidden_states_397_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428060672)))]; + tensor var_9946_to_fp16 = const()[name = tensor("op_9946_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_397_cast_fp16 = layer_norm(axes = hidden_states_397_axes_0, beta = hidden_states_397_beta_0_to_fp16, epsilon = var_9946_to_fp16, gamma = hidden_states_397_gamma_0_to_fp16, x = inputs_301_cast_fp16)[name = tensor("hidden_states_397_cast_fp16")]; + tensor var_9961 = const()[name = tensor("op_9961"), val = tensor([1, 1])]; + tensor var_9963 = const()[name = tensor("op_9963"), val = tensor([1, 1])]; + tensor q_201_pad_type_0 = const()[name = tensor("q_201_pad_type_0"), val = tensor("custom")]; + tensor q_201_pad_0 = const()[name = tensor("q_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428063296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1429292160))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_201_cast_fp16 = conv(dilations = var_9963, groups = var_6895, pad = q_201_pad_0, pad_type = q_201_pad_type_0, strides = var_9961, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_397_cast_fp16)[name = tensor("q_201_cast_fp16")]; + tensor var_9967 = const()[name = tensor("op_9967"), val = tensor([1, 1])]; + tensor var_9969 = const()[name = tensor("op_9969"), val = tensor([1, 1])]; + tensor k_201_pad_type_0 = const()[name = tensor("k_201_pad_type_0"), val = tensor("custom")]; + tensor k_201_pad_0 = const()[name = tensor("k_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1429292352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1430521216))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_201_cast_fp16 = conv(dilations = var_9969, groups = var_6895, pad = k_201_pad_0, pad_type = k_201_pad_type_0, strides = var_9967, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_397_cast_fp16)[name = tensor("k_201_cast_fp16")]; + tensor var_9973 = const()[name = tensor("op_9973"), val = tensor([1, 1])]; + tensor var_9975 = const()[name = tensor("op_9975"), val = tensor([1, 1])]; + tensor v_201_pad_type_0 = const()[name = tensor("v_201_pad_type_0"), val = tensor("custom")]; + tensor v_201_pad_0 = const()[name = tensor("v_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1430521408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1431750272))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_201_cast_fp16 = conv(dilations = var_9975, groups = var_6895, pad = v_201_pad_0, pad_type = v_201_pad_type_0, strides = var_9973, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_397_cast_fp16)[name = tensor("v_201_cast_fp16")]; + tensor var_9979 = const()[name = tensor("op_9979"), val = tensor([1, 20, 64, -1])]; + tensor var_9980_cast_fp16 = reshape(shape = var_9979, x = q_201_cast_fp16)[name = tensor("op_9980_cast_fp16")]; + tensor var_9981 = const()[name = tensor("op_9981"), val = tensor([1, 20, 64, -1])]; + tensor var_9982_cast_fp16 = reshape(shape = var_9981, x = k_201_cast_fp16)[name = tensor("op_9982_cast_fp16")]; + tensor var_9983 = const()[name = tensor("op_9983"), val = tensor([1, 20, 64, -1])]; + tensor var_9984_cast_fp16 = reshape(shape = var_9983, x = v_201_cast_fp16)[name = tensor("op_9984_cast_fp16")]; + tensor attn_weights_401_transpose_x_0 = const()[name = tensor("attn_weights_401_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_401_transpose_y_0 = const()[name = tensor("attn_weights_401_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9980_cast_fp16, y = var_9982_cast_fp16)[name = tensor("attn_weights_401_cast_fp16")]; + tensor attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_403_cast_fp16")]; + tensor var_9988_cast_fp16 = softmax(axis = var_6879, x = attn_weights_403_cast_fp16)[name = tensor("op_9988_cast_fp16")]; + tensor attn_201_transpose_x_0 = const()[name = tensor("attn_201_transpose_x_0"), val = tensor(false)]; + tensor attn_201_transpose_y_0 = const()[name = tensor("attn_201_transpose_y_0"), val = tensor(true)]; + tensor attn_201_cast_fp16 = matmul(transpose_x = attn_201_transpose_x_0, transpose_y = attn_201_transpose_y_0, x = var_9984_cast_fp16, y = var_9988_cast_fp16)[name = tensor("attn_201_cast_fp16")]; + tensor var_9992 = const()[name = tensor("op_9992"), val = tensor([1, 1280, 1, -1])]; + tensor input_587_cast_fp16 = reshape(shape = var_9992, x = attn_201_cast_fp16)[name = tensor("input_587_cast_fp16")]; + tensor var_9997 = const()[name = tensor("op_9997"), val = tensor([1, 1])]; + tensor var_9999 = const()[name = tensor("op_9999"), val = tensor([1, 1])]; + tensor var_10001_pad_type_0 = const()[name = tensor("op_10001_pad_type_0"), val = tensor("custom")]; + tensor var_10001_pad_0 = const()[name = tensor("op_10001_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1431750464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432979328))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432979520)))]; + tensor var_10001_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_9999, groups = var_6895, pad = var_10001_pad_0, pad_type = var_10001_pad_type_0, strides = var_9997, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_587_cast_fp16)[name = tensor("op_10001_cast_fp16")]; + tensor inputs_303_cast_fp16 = add(x = var_10001_cast_fp16, y = inputs_301_cast_fp16)[name = tensor("inputs_303_cast_fp16")]; + tensor hidden_states_399_axes_0 = const()[name = tensor("hidden_states_399_axes_0"), val = tensor([1])]; + tensor hidden_states_399_gamma_0_to_fp16 = const()[name = tensor("hidden_states_399_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432982144)))]; + tensor hidden_states_399_beta_0_to_fp16 = const()[name = tensor("hidden_states_399_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432984768)))]; + tensor var_10011_to_fp16 = const()[name = tensor("op_10011_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_399_cast_fp16 = layer_norm(axes = hidden_states_399_axes_0, beta = hidden_states_399_beta_0_to_fp16, epsilon = var_10011_to_fp16, gamma = hidden_states_399_gamma_0_to_fp16, x = inputs_303_cast_fp16)[name = tensor("hidden_states_399_cast_fp16")]; + tensor var_10026 = const()[name = tensor("op_10026"), val = tensor([1, 1])]; + tensor var_10028 = const()[name = tensor("op_10028"), val = tensor([1, 1])]; + tensor q_203_pad_type_0 = const()[name = tensor("q_203_pad_type_0"), val = tensor("custom")]; + tensor q_203_pad_0 = const()[name = tensor("q_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432987392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1434216256))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_203_cast_fp16 = conv(dilations = var_10028, groups = var_6895, pad = q_203_pad_0, pad_type = q_203_pad_type_0, strides = var_10026, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_399_cast_fp16)[name = tensor("q_203_cast_fp16")]; + tensor var_10032 = const()[name = tensor("op_10032"), val = tensor([1, 1])]; + tensor var_10034 = const()[name = tensor("op_10034"), val = tensor([1, 1])]; + tensor k_203_pad_type_0 = const()[name = tensor("k_203_pad_type_0"), val = tensor("custom")]; + tensor k_203_pad_0 = const()[name = tensor("k_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1434216448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1436182592))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_203_cast_fp16 = conv(dilations = var_10034, groups = var_6895, pad = k_203_pad_0, pad_type = k_203_pad_type_0, strides = var_10032, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_203_cast_fp16")]; + tensor var_10038 = const()[name = tensor("op_10038"), val = tensor([1, 1])]; + tensor var_10040 = const()[name = tensor("op_10040"), val = tensor([1, 1])]; + tensor v_203_pad_type_0 = const()[name = tensor("v_203_pad_type_0"), val = tensor("custom")]; + tensor v_203_pad_0 = const()[name = tensor("v_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1436182784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1438148928))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_203_cast_fp16 = conv(dilations = var_10040, groups = var_6895, pad = v_203_pad_0, pad_type = v_203_pad_type_0, strides = var_10038, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_203_cast_fp16")]; + tensor var_10044 = const()[name = tensor("op_10044"), val = tensor([1, 20, 64, -1])]; + tensor var_10045_cast_fp16 = reshape(shape = var_10044, x = q_203_cast_fp16)[name = tensor("op_10045_cast_fp16")]; + tensor var_10046 = const()[name = tensor("op_10046"), val = tensor([1, 20, 64, -1])]; + tensor var_10047_cast_fp16 = reshape(shape = var_10046, x = k_203_cast_fp16)[name = tensor("op_10047_cast_fp16")]; + tensor var_10048 = const()[name = tensor("op_10048"), val = tensor([1, 20, 64, -1])]; + tensor var_10049_cast_fp16 = reshape(shape = var_10048, x = v_203_cast_fp16)[name = tensor("op_10049_cast_fp16")]; + tensor attn_weights_405_transpose_x_0 = const()[name = tensor("attn_weights_405_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_405_transpose_y_0 = const()[name = tensor("attn_weights_405_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_405_cast_fp16 = matmul(transpose_x = attn_weights_405_transpose_x_0, transpose_y = attn_weights_405_transpose_y_0, x = var_10045_cast_fp16, y = var_10047_cast_fp16)[name = tensor("attn_weights_405_cast_fp16")]; + tensor attn_weights_407_cast_fp16 = mul(x = attn_weights_405_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_407_cast_fp16")]; + tensor var_10053_cast_fp16 = softmax(axis = var_6879, x = attn_weights_407_cast_fp16)[name = tensor("op_10053_cast_fp16")]; + tensor attn_203_transpose_x_0 = const()[name = tensor("attn_203_transpose_x_0"), val = tensor(false)]; + tensor attn_203_transpose_y_0 = const()[name = tensor("attn_203_transpose_y_0"), val = tensor(true)]; + tensor attn_203_cast_fp16 = matmul(transpose_x = attn_203_transpose_x_0, transpose_y = attn_203_transpose_y_0, x = var_10049_cast_fp16, y = var_10053_cast_fp16)[name = tensor("attn_203_cast_fp16")]; + tensor var_10057 = const()[name = tensor("op_10057"), val = tensor([1, 1280, 1, -1])]; + tensor input_589_cast_fp16 = reshape(shape = var_10057, x = attn_203_cast_fp16)[name = tensor("input_589_cast_fp16")]; + tensor var_10062 = const()[name = tensor("op_10062"), val = tensor([1, 1])]; + tensor var_10064 = const()[name = tensor("op_10064"), val = tensor([1, 1])]; + tensor var_10066_pad_type_0 = const()[name = tensor("op_10066_pad_type_0"), val = tensor("custom")]; + tensor var_10066_pad_0 = const()[name = tensor("op_10066_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1438149120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439377984))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439378176)))]; + tensor var_10066_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_10064, groups = var_6895, pad = var_10066_pad_0, pad_type = var_10066_pad_type_0, strides = var_10062, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_589_cast_fp16)[name = tensor("op_10066_cast_fp16")]; + tensor inputs_305_cast_fp16 = add(x = var_10066_cast_fp16, y = inputs_303_cast_fp16)[name = tensor("inputs_305_cast_fp16")]; + tensor input_591_axes_0 = const()[name = tensor("input_591_axes_0"), val = tensor([1])]; + tensor input_591_gamma_0_to_fp16 = const()[name = tensor("input_591_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439380800)))]; + tensor input_591_beta_0_to_fp16 = const()[name = tensor("input_591_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439383424)))]; + tensor var_10076_to_fp16 = const()[name = tensor("op_10076_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = input_591_beta_0_to_fp16, epsilon = var_10076_to_fp16, gamma = input_591_gamma_0_to_fp16, x = inputs_305_cast_fp16)[name = tensor("input_591_cast_fp16")]; + tensor var_10092 = const()[name = tensor("op_10092"), val = tensor([1, 1])]; + tensor var_10094 = const()[name = tensor("op_10094"), val = tensor([1, 1])]; + tensor var_10096_pad_type_0 = const()[name = tensor("op_10096_pad_type_0"), val = tensor("custom")]; + tensor var_10096_pad_0 = const()[name = tensor("op_10096_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439386048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449216512))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449216704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449224448))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10096_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10094, groups = var_6895, pad = var_10096_pad_0, pad_type = var_10096_pad_type_0, strides = var_10092, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_591_cast_fp16)[name = tensor("op_10096_cast_fp16")]; + tensor var_10097_split_sizes_0 = const()[name = tensor("op_10097_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10097_axis_0 = const()[name = tensor("op_10097_axis_0"), val = tensor(1)]; + tensor var_10097_cast_fp16_0, tensor var_10097_cast_fp16_1 = split(axis = var_10097_axis_0, split_sizes = var_10097_split_sizes_0, x = var_10096_cast_fp16)[name = tensor("op_10097_cast_fp16")]; + tensor var_10099_mode_0 = const()[name = tensor("op_10099_mode_0"), val = tensor("EXACT")]; + tensor var_10099_cast_fp16 = gelu(mode = var_10099_mode_0, x = var_10097_cast_fp16_1)[name = tensor("op_10099_cast_fp16")]; + tensor input_593_cast_fp16 = mul(x = var_10097_cast_fp16_0, y = var_10099_cast_fp16)[name = tensor("input_593_cast_fp16")]; + tensor var_10103 = const()[name = tensor("op_10103"), val = tensor([1, 1])]; + tensor var_10105 = const()[name = tensor("op_10105"), val = tensor([1, 1])]; + tensor var_10107_pad_type_0 = const()[name = tensor("op_10107_pad_type_0"), val = tensor("custom")]; + tensor var_10107_pad_0 = const()[name = tensor("op_10107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449224640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454139904))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454140096)))]; + tensor var_10107_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_10105, groups = var_6895, pad = var_10107_pad_0, pad_type = var_10107_pad_type_0, strides = var_10103, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_593_cast_fp16)[name = tensor("op_10107_cast_fp16")]; + tensor inputs_307_cast_fp16 = add(x = var_10107_cast_fp16, y = inputs_305_cast_fp16)[name = tensor("inputs_307_cast_fp16")]; + tensor hidden_states_403_axes_0 = const()[name = tensor("hidden_states_403_axes_0"), val = tensor([1])]; + tensor hidden_states_403_gamma_0_to_fp16 = const()[name = tensor("hidden_states_403_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454142720)))]; + tensor hidden_states_403_beta_0_to_fp16 = const()[name = tensor("hidden_states_403_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454145344)))]; + tensor var_10123_to_fp16 = const()[name = tensor("op_10123_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_403_cast_fp16 = layer_norm(axes = hidden_states_403_axes_0, beta = hidden_states_403_beta_0_to_fp16, epsilon = var_10123_to_fp16, gamma = hidden_states_403_gamma_0_to_fp16, x = inputs_307_cast_fp16)[name = tensor("hidden_states_403_cast_fp16")]; + tensor var_10138 = const()[name = tensor("op_10138"), val = tensor([1, 1])]; + tensor var_10140 = const()[name = tensor("op_10140"), val = tensor([1, 1])]; + tensor q_205_pad_type_0 = const()[name = tensor("q_205_pad_type_0"), val = tensor("custom")]; + tensor q_205_pad_0 = const()[name = tensor("q_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454147968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1455376832))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_205_cast_fp16 = conv(dilations = var_10140, groups = var_6895, pad = q_205_pad_0, pad_type = q_205_pad_type_0, strides = var_10138, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_403_cast_fp16)[name = tensor("q_205_cast_fp16")]; + tensor var_10144 = const()[name = tensor("op_10144"), val = tensor([1, 1])]; + tensor var_10146 = const()[name = tensor("op_10146"), val = tensor([1, 1])]; + tensor k_205_pad_type_0 = const()[name = tensor("k_205_pad_type_0"), val = tensor("custom")]; + tensor k_205_pad_0 = const()[name = tensor("k_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1455377024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1456605888))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_205_cast_fp16 = conv(dilations = var_10146, groups = var_6895, pad = k_205_pad_0, pad_type = k_205_pad_type_0, strides = var_10144, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_403_cast_fp16)[name = tensor("k_205_cast_fp16")]; + tensor var_10150 = const()[name = tensor("op_10150"), val = tensor([1, 1])]; + tensor var_10152 = const()[name = tensor("op_10152"), val = tensor([1, 1])]; + tensor v_205_pad_type_0 = const()[name = tensor("v_205_pad_type_0"), val = tensor("custom")]; + tensor v_205_pad_0 = const()[name = tensor("v_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1456606080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1457834944))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_205_cast_fp16 = conv(dilations = var_10152, groups = var_6895, pad = v_205_pad_0, pad_type = v_205_pad_type_0, strides = var_10150, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_403_cast_fp16)[name = tensor("v_205_cast_fp16")]; + tensor var_10156 = const()[name = tensor("op_10156"), val = tensor([1, 20, 64, -1])]; + tensor var_10157_cast_fp16 = reshape(shape = var_10156, x = q_205_cast_fp16)[name = tensor("op_10157_cast_fp16")]; + tensor var_10158 = const()[name = tensor("op_10158"), val = tensor([1, 20, 64, -1])]; + tensor var_10159_cast_fp16 = reshape(shape = var_10158, x = k_205_cast_fp16)[name = tensor("op_10159_cast_fp16")]; + tensor var_10160 = const()[name = tensor("op_10160"), val = tensor([1, 20, 64, -1])]; + tensor var_10161_cast_fp16 = reshape(shape = var_10160, x = v_205_cast_fp16)[name = tensor("op_10161_cast_fp16")]; + tensor attn_weights_409_transpose_x_0 = const()[name = tensor("attn_weights_409_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_409_transpose_y_0 = const()[name = tensor("attn_weights_409_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_10157_cast_fp16, y = var_10159_cast_fp16)[name = tensor("attn_weights_409_cast_fp16")]; + tensor attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_411_cast_fp16")]; + tensor var_10165_cast_fp16 = softmax(axis = var_6879, x = attn_weights_411_cast_fp16)[name = tensor("op_10165_cast_fp16")]; + tensor attn_205_transpose_x_0 = const()[name = tensor("attn_205_transpose_x_0"), val = tensor(false)]; + tensor attn_205_transpose_y_0 = const()[name = tensor("attn_205_transpose_y_0"), val = tensor(true)]; + tensor attn_205_cast_fp16 = matmul(transpose_x = attn_205_transpose_x_0, transpose_y = attn_205_transpose_y_0, x = var_10161_cast_fp16, y = var_10165_cast_fp16)[name = tensor("attn_205_cast_fp16")]; + tensor var_10169 = const()[name = tensor("op_10169"), val = tensor([1, 1280, 1, -1])]; + tensor input_595_cast_fp16 = reshape(shape = var_10169, x = attn_205_cast_fp16)[name = tensor("input_595_cast_fp16")]; + tensor var_10174 = const()[name = tensor("op_10174"), val = tensor([1, 1])]; + tensor var_10176 = const()[name = tensor("op_10176"), val = tensor([1, 1])]; + tensor var_10178_pad_type_0 = const()[name = tensor("op_10178_pad_type_0"), val = tensor("custom")]; + tensor var_10178_pad_0 = const()[name = tensor("op_10178_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1457835136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459064000))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459064192)))]; + tensor var_10178_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_10176, groups = var_6895, pad = var_10178_pad_0, pad_type = var_10178_pad_type_0, strides = var_10174, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_595_cast_fp16)[name = tensor("op_10178_cast_fp16")]; + tensor inputs_309_cast_fp16 = add(x = var_10178_cast_fp16, y = inputs_307_cast_fp16)[name = tensor("inputs_309_cast_fp16")]; + tensor hidden_states_405_axes_0 = const()[name = tensor("hidden_states_405_axes_0"), val = tensor([1])]; + tensor hidden_states_405_gamma_0_to_fp16 = const()[name = tensor("hidden_states_405_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459066816)))]; + tensor hidden_states_405_beta_0_to_fp16 = const()[name = tensor("hidden_states_405_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459069440)))]; + tensor var_10188_to_fp16 = const()[name = tensor("op_10188_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_405_cast_fp16 = layer_norm(axes = hidden_states_405_axes_0, beta = hidden_states_405_beta_0_to_fp16, epsilon = var_10188_to_fp16, gamma = hidden_states_405_gamma_0_to_fp16, x = inputs_309_cast_fp16)[name = tensor("hidden_states_405_cast_fp16")]; + tensor var_10203 = const()[name = tensor("op_10203"), val = tensor([1, 1])]; + tensor var_10205 = const()[name = tensor("op_10205"), val = tensor([1, 1])]; + tensor q_207_pad_type_0 = const()[name = tensor("q_207_pad_type_0"), val = tensor("custom")]; + tensor q_207_pad_0 = const()[name = tensor("q_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459072064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1460300928))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_207_cast_fp16 = conv(dilations = var_10205, groups = var_6895, pad = q_207_pad_0, pad_type = q_207_pad_type_0, strides = var_10203, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_405_cast_fp16)[name = tensor("q_207_cast_fp16")]; + tensor var_10209 = const()[name = tensor("op_10209"), val = tensor([1, 1])]; + tensor var_10211 = const()[name = tensor("op_10211"), val = tensor([1, 1])]; + tensor k_207_pad_type_0 = const()[name = tensor("k_207_pad_type_0"), val = tensor("custom")]; + tensor k_207_pad_0 = const()[name = tensor("k_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1460301120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1462267264))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_207_cast_fp16 = conv(dilations = var_10211, groups = var_6895, pad = k_207_pad_0, pad_type = k_207_pad_type_0, strides = var_10209, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_207_cast_fp16")]; + tensor var_10215 = const()[name = tensor("op_10215"), val = tensor([1, 1])]; + tensor var_10217 = const()[name = tensor("op_10217"), val = tensor([1, 1])]; + tensor v_207_pad_type_0 = const()[name = tensor("v_207_pad_type_0"), val = tensor("custom")]; + tensor v_207_pad_0 = const()[name = tensor("v_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1462267456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464233600))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_207_cast_fp16 = conv(dilations = var_10217, groups = var_6895, pad = v_207_pad_0, pad_type = v_207_pad_type_0, strides = var_10215, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_207_cast_fp16")]; + tensor var_10221 = const()[name = tensor("op_10221"), val = tensor([1, 20, 64, -1])]; + tensor var_10222_cast_fp16 = reshape(shape = var_10221, x = q_207_cast_fp16)[name = tensor("op_10222_cast_fp16")]; + tensor var_10223 = const()[name = tensor("op_10223"), val = tensor([1, 20, 64, -1])]; + tensor var_10224_cast_fp16 = reshape(shape = var_10223, x = k_207_cast_fp16)[name = tensor("op_10224_cast_fp16")]; + tensor var_10225 = const()[name = tensor("op_10225"), val = tensor([1, 20, 64, -1])]; + tensor var_10226_cast_fp16 = reshape(shape = var_10225, x = v_207_cast_fp16)[name = tensor("op_10226_cast_fp16")]; + tensor attn_weights_413_transpose_x_0 = const()[name = tensor("attn_weights_413_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_413_transpose_y_0 = const()[name = tensor("attn_weights_413_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_413_cast_fp16 = matmul(transpose_x = attn_weights_413_transpose_x_0, transpose_y = attn_weights_413_transpose_y_0, x = var_10222_cast_fp16, y = var_10224_cast_fp16)[name = tensor("attn_weights_413_cast_fp16")]; + tensor attn_weights_415_cast_fp16 = mul(x = attn_weights_413_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_415_cast_fp16")]; + tensor var_10230_cast_fp16 = softmax(axis = var_6879, x = attn_weights_415_cast_fp16)[name = tensor("op_10230_cast_fp16")]; + tensor attn_207_transpose_x_0 = const()[name = tensor("attn_207_transpose_x_0"), val = tensor(false)]; + tensor attn_207_transpose_y_0 = const()[name = tensor("attn_207_transpose_y_0"), val = tensor(true)]; + tensor attn_207_cast_fp16 = matmul(transpose_x = attn_207_transpose_x_0, transpose_y = attn_207_transpose_y_0, x = var_10226_cast_fp16, y = var_10230_cast_fp16)[name = tensor("attn_207_cast_fp16")]; + tensor var_10234 = const()[name = tensor("op_10234"), val = tensor([1, 1280, 1, -1])]; + tensor input_597_cast_fp16 = reshape(shape = var_10234, x = attn_207_cast_fp16)[name = tensor("input_597_cast_fp16")]; + tensor var_10239 = const()[name = tensor("op_10239"), val = tensor([1, 1])]; + tensor var_10241 = const()[name = tensor("op_10241"), val = tensor([1, 1])]; + tensor var_10243_pad_type_0 = const()[name = tensor("op_10243_pad_type_0"), val = tensor("custom")]; + tensor var_10243_pad_0 = const()[name = tensor("op_10243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464233792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465462656))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465462848)))]; + tensor var_10243_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_10241, groups = var_6895, pad = var_10243_pad_0, pad_type = var_10243_pad_type_0, strides = var_10239, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_597_cast_fp16)[name = tensor("op_10243_cast_fp16")]; + tensor inputs_311_cast_fp16 = add(x = var_10243_cast_fp16, y = inputs_309_cast_fp16)[name = tensor("inputs_311_cast_fp16")]; + tensor input_599_axes_0 = const()[name = tensor("input_599_axes_0"), val = tensor([1])]; + tensor input_599_gamma_0_to_fp16 = const()[name = tensor("input_599_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465465472)))]; + tensor input_599_beta_0_to_fp16 = const()[name = tensor("input_599_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465468096)))]; + tensor var_10253_to_fp16 = const()[name = tensor("op_10253_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = input_599_beta_0_to_fp16, epsilon = var_10253_to_fp16, gamma = input_599_gamma_0_to_fp16, x = inputs_311_cast_fp16)[name = tensor("input_599_cast_fp16")]; + tensor var_10269 = const()[name = tensor("op_10269"), val = tensor([1, 1])]; + tensor var_10271 = const()[name = tensor("op_10271"), val = tensor([1, 1])]; + tensor var_10273_pad_type_0 = const()[name = tensor("op_10273_pad_type_0"), val = tensor("custom")]; + tensor var_10273_pad_0 = const()[name = tensor("op_10273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465470720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475301184))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475301376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475309120))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10273_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10271, groups = var_6895, pad = var_10273_pad_0, pad_type = var_10273_pad_type_0, strides = var_10269, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_599_cast_fp16)[name = tensor("op_10273_cast_fp16")]; + tensor var_10274_split_sizes_0 = const()[name = tensor("op_10274_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10274_axis_0 = const()[name = tensor("op_10274_axis_0"), val = tensor(1)]; + tensor var_10274_cast_fp16_0, tensor var_10274_cast_fp16_1 = split(axis = var_10274_axis_0, split_sizes = var_10274_split_sizes_0, x = var_10273_cast_fp16)[name = tensor("op_10274_cast_fp16")]; + tensor var_10276_mode_0 = const()[name = tensor("op_10276_mode_0"), val = tensor("EXACT")]; + tensor var_10276_cast_fp16 = gelu(mode = var_10276_mode_0, x = var_10274_cast_fp16_1)[name = tensor("op_10276_cast_fp16")]; + tensor input_601_cast_fp16 = mul(x = var_10274_cast_fp16_0, y = var_10276_cast_fp16)[name = tensor("input_601_cast_fp16")]; + tensor var_10280 = const()[name = tensor("op_10280"), val = tensor([1, 1])]; + tensor var_10282 = const()[name = tensor("op_10282"), val = tensor([1, 1])]; + tensor var_10284_pad_type_0 = const()[name = tensor("op_10284_pad_type_0"), val = tensor("custom")]; + tensor var_10284_pad_0 = const()[name = tensor("op_10284_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475309312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480224576))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480224768)))]; + tensor var_10284_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_10282, groups = var_6895, pad = var_10284_pad_0, pad_type = var_10284_pad_type_0, strides = var_10280, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_601_cast_fp16)[name = tensor("op_10284_cast_fp16")]; + tensor inputs_313_cast_fp16 = add(x = var_10284_cast_fp16, y = inputs_311_cast_fp16)[name = tensor("inputs_313_cast_fp16")]; + tensor hidden_states_409_axes_0 = const()[name = tensor("hidden_states_409_axes_0"), val = tensor([1])]; + tensor hidden_states_409_gamma_0_to_fp16 = const()[name = tensor("hidden_states_409_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480227392)))]; + tensor hidden_states_409_beta_0_to_fp16 = const()[name = tensor("hidden_states_409_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480230016)))]; + tensor var_10300_to_fp16 = const()[name = tensor("op_10300_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_409_cast_fp16 = layer_norm(axes = hidden_states_409_axes_0, beta = hidden_states_409_beta_0_to_fp16, epsilon = var_10300_to_fp16, gamma = hidden_states_409_gamma_0_to_fp16, x = inputs_313_cast_fp16)[name = tensor("hidden_states_409_cast_fp16")]; + tensor var_10315 = const()[name = tensor("op_10315"), val = tensor([1, 1])]; + tensor var_10317 = const()[name = tensor("op_10317"), val = tensor([1, 1])]; + tensor q_209_pad_type_0 = const()[name = tensor("q_209_pad_type_0"), val = tensor("custom")]; + tensor q_209_pad_0 = const()[name = tensor("q_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480232640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1481461504))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_209_cast_fp16 = conv(dilations = var_10317, groups = var_6895, pad = q_209_pad_0, pad_type = q_209_pad_type_0, strides = var_10315, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_409_cast_fp16)[name = tensor("q_209_cast_fp16")]; + tensor var_10321 = const()[name = tensor("op_10321"), val = tensor([1, 1])]; + tensor var_10323 = const()[name = tensor("op_10323"), val = tensor([1, 1])]; + tensor k_209_pad_type_0 = const()[name = tensor("k_209_pad_type_0"), val = tensor("custom")]; + tensor k_209_pad_0 = const()[name = tensor("k_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1481461696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1482690560))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_209_cast_fp16 = conv(dilations = var_10323, groups = var_6895, pad = k_209_pad_0, pad_type = k_209_pad_type_0, strides = var_10321, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_409_cast_fp16)[name = tensor("k_209_cast_fp16")]; + tensor var_10327 = const()[name = tensor("op_10327"), val = tensor([1, 1])]; + tensor var_10329 = const()[name = tensor("op_10329"), val = tensor([1, 1])]; + tensor v_209_pad_type_0 = const()[name = tensor("v_209_pad_type_0"), val = tensor("custom")]; + tensor v_209_pad_0 = const()[name = tensor("v_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1482690752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1483919616))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_209_cast_fp16 = conv(dilations = var_10329, groups = var_6895, pad = v_209_pad_0, pad_type = v_209_pad_type_0, strides = var_10327, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_409_cast_fp16)[name = tensor("v_209_cast_fp16")]; + tensor var_10333 = const()[name = tensor("op_10333"), val = tensor([1, 20, 64, -1])]; + tensor var_10334_cast_fp16 = reshape(shape = var_10333, x = q_209_cast_fp16)[name = tensor("op_10334_cast_fp16")]; + tensor var_10335 = const()[name = tensor("op_10335"), val = tensor([1, 20, 64, -1])]; + tensor var_10336_cast_fp16 = reshape(shape = var_10335, x = k_209_cast_fp16)[name = tensor("op_10336_cast_fp16")]; + tensor var_10337 = const()[name = tensor("op_10337"), val = tensor([1, 20, 64, -1])]; + tensor var_10338_cast_fp16 = reshape(shape = var_10337, x = v_209_cast_fp16)[name = tensor("op_10338_cast_fp16")]; + tensor attn_weights_417_transpose_x_0 = const()[name = tensor("attn_weights_417_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_417_transpose_y_0 = const()[name = tensor("attn_weights_417_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_10334_cast_fp16, y = var_10336_cast_fp16)[name = tensor("attn_weights_417_cast_fp16")]; + tensor attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_419_cast_fp16")]; + tensor var_10342_cast_fp16 = softmax(axis = var_6879, x = attn_weights_419_cast_fp16)[name = tensor("op_10342_cast_fp16")]; + tensor attn_209_transpose_x_0 = const()[name = tensor("attn_209_transpose_x_0"), val = tensor(false)]; + tensor attn_209_transpose_y_0 = const()[name = tensor("attn_209_transpose_y_0"), val = tensor(true)]; + tensor attn_209_cast_fp16 = matmul(transpose_x = attn_209_transpose_x_0, transpose_y = attn_209_transpose_y_0, x = var_10338_cast_fp16, y = var_10342_cast_fp16)[name = tensor("attn_209_cast_fp16")]; + tensor var_10346 = const()[name = tensor("op_10346"), val = tensor([1, 1280, 1, -1])]; + tensor input_603_cast_fp16 = reshape(shape = var_10346, x = attn_209_cast_fp16)[name = tensor("input_603_cast_fp16")]; + tensor var_10351 = const()[name = tensor("op_10351"), val = tensor([1, 1])]; + tensor var_10353 = const()[name = tensor("op_10353"), val = tensor([1, 1])]; + tensor var_10355_pad_type_0 = const()[name = tensor("op_10355_pad_type_0"), val = tensor("custom")]; + tensor var_10355_pad_0 = const()[name = tensor("op_10355_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1483919808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485148672))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485148864)))]; + tensor var_10355_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_10353, groups = var_6895, pad = var_10355_pad_0, pad_type = var_10355_pad_type_0, strides = var_10351, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_603_cast_fp16)[name = tensor("op_10355_cast_fp16")]; + tensor inputs_315_cast_fp16 = add(x = var_10355_cast_fp16, y = inputs_313_cast_fp16)[name = tensor("inputs_315_cast_fp16")]; + tensor hidden_states_411_axes_0 = const()[name = tensor("hidden_states_411_axes_0"), val = tensor([1])]; + tensor hidden_states_411_gamma_0_to_fp16 = const()[name = tensor("hidden_states_411_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485151488)))]; + tensor hidden_states_411_beta_0_to_fp16 = const()[name = tensor("hidden_states_411_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485154112)))]; + tensor var_10365_to_fp16 = const()[name = tensor("op_10365_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_411_cast_fp16 = layer_norm(axes = hidden_states_411_axes_0, beta = hidden_states_411_beta_0_to_fp16, epsilon = var_10365_to_fp16, gamma = hidden_states_411_gamma_0_to_fp16, x = inputs_315_cast_fp16)[name = tensor("hidden_states_411_cast_fp16")]; + tensor var_10380 = const()[name = tensor("op_10380"), val = tensor([1, 1])]; + tensor var_10382 = const()[name = tensor("op_10382"), val = tensor([1, 1])]; + tensor q_211_pad_type_0 = const()[name = tensor("q_211_pad_type_0"), val = tensor("custom")]; + tensor q_211_pad_0 = const()[name = tensor("q_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485156736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1486385600))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_211_cast_fp16 = conv(dilations = var_10382, groups = var_6895, pad = q_211_pad_0, pad_type = q_211_pad_type_0, strides = var_10380, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_411_cast_fp16)[name = tensor("q_211_cast_fp16")]; + tensor var_10386 = const()[name = tensor("op_10386"), val = tensor([1, 1])]; + tensor var_10388 = const()[name = tensor("op_10388"), val = tensor([1, 1])]; + tensor k_211_pad_type_0 = const()[name = tensor("k_211_pad_type_0"), val = tensor("custom")]; + tensor k_211_pad_0 = const()[name = tensor("k_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1486385792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1488351936))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_211_cast_fp16 = conv(dilations = var_10388, groups = var_6895, pad = k_211_pad_0, pad_type = k_211_pad_type_0, strides = var_10386, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_211_cast_fp16")]; + tensor var_10392 = const()[name = tensor("op_10392"), val = tensor([1, 1])]; + tensor var_10394 = const()[name = tensor("op_10394"), val = tensor([1, 1])]; + tensor v_211_pad_type_0 = const()[name = tensor("v_211_pad_type_0"), val = tensor("custom")]; + tensor v_211_pad_0 = const()[name = tensor("v_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1488352128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1490318272))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_211_cast_fp16 = conv(dilations = var_10394, groups = var_6895, pad = v_211_pad_0, pad_type = v_211_pad_type_0, strides = var_10392, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_211_cast_fp16")]; + tensor var_10398 = const()[name = tensor("op_10398"), val = tensor([1, 20, 64, -1])]; + tensor var_10399_cast_fp16 = reshape(shape = var_10398, x = q_211_cast_fp16)[name = tensor("op_10399_cast_fp16")]; + tensor var_10400 = const()[name = tensor("op_10400"), val = tensor([1, 20, 64, -1])]; + tensor var_10401_cast_fp16 = reshape(shape = var_10400, x = k_211_cast_fp16)[name = tensor("op_10401_cast_fp16")]; + tensor var_10402 = const()[name = tensor("op_10402"), val = tensor([1, 20, 64, -1])]; + tensor var_10403_cast_fp16 = reshape(shape = var_10402, x = v_211_cast_fp16)[name = tensor("op_10403_cast_fp16")]; + tensor attn_weights_421_transpose_x_0 = const()[name = tensor("attn_weights_421_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_421_transpose_y_0 = const()[name = tensor("attn_weights_421_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_421_cast_fp16 = matmul(transpose_x = attn_weights_421_transpose_x_0, transpose_y = attn_weights_421_transpose_y_0, x = var_10399_cast_fp16, y = var_10401_cast_fp16)[name = tensor("attn_weights_421_cast_fp16")]; + tensor attn_weights_423_cast_fp16 = mul(x = attn_weights_421_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_423_cast_fp16")]; + tensor var_10407_cast_fp16 = softmax(axis = var_6879, x = attn_weights_423_cast_fp16)[name = tensor("op_10407_cast_fp16")]; + tensor attn_211_transpose_x_0 = const()[name = tensor("attn_211_transpose_x_0"), val = tensor(false)]; + tensor attn_211_transpose_y_0 = const()[name = tensor("attn_211_transpose_y_0"), val = tensor(true)]; + tensor attn_211_cast_fp16 = matmul(transpose_x = attn_211_transpose_x_0, transpose_y = attn_211_transpose_y_0, x = var_10403_cast_fp16, y = var_10407_cast_fp16)[name = tensor("attn_211_cast_fp16")]; + tensor var_10411 = const()[name = tensor("op_10411"), val = tensor([1, 1280, 1, -1])]; + tensor input_605_cast_fp16 = reshape(shape = var_10411, x = attn_211_cast_fp16)[name = tensor("input_605_cast_fp16")]; + tensor var_10416 = const()[name = tensor("op_10416"), val = tensor([1, 1])]; + tensor var_10418 = const()[name = tensor("op_10418"), val = tensor([1, 1])]; + tensor var_10420_pad_type_0 = const()[name = tensor("op_10420_pad_type_0"), val = tensor("custom")]; + tensor var_10420_pad_0 = const()[name = tensor("op_10420_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1490318464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491547328))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491547520)))]; + tensor var_10420_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_10418, groups = var_6895, pad = var_10420_pad_0, pad_type = var_10420_pad_type_0, strides = var_10416, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_605_cast_fp16)[name = tensor("op_10420_cast_fp16")]; + tensor inputs_317_cast_fp16 = add(x = var_10420_cast_fp16, y = inputs_315_cast_fp16)[name = tensor("inputs_317_cast_fp16")]; + tensor input_607_axes_0 = const()[name = tensor("input_607_axes_0"), val = tensor([1])]; + tensor input_607_gamma_0_to_fp16 = const()[name = tensor("input_607_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491550144)))]; + tensor input_607_beta_0_to_fp16 = const()[name = tensor("input_607_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491552768)))]; + tensor var_10430_to_fp16 = const()[name = tensor("op_10430_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_607_cast_fp16 = layer_norm(axes = input_607_axes_0, beta = input_607_beta_0_to_fp16, epsilon = var_10430_to_fp16, gamma = input_607_gamma_0_to_fp16, x = inputs_317_cast_fp16)[name = tensor("input_607_cast_fp16")]; + tensor var_10446 = const()[name = tensor("op_10446"), val = tensor([1, 1])]; + tensor var_10448 = const()[name = tensor("op_10448"), val = tensor([1, 1])]; + tensor var_10450_pad_type_0 = const()[name = tensor("op_10450_pad_type_0"), val = tensor("custom")]; + tensor var_10450_pad_0 = const()[name = tensor("op_10450_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491555392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501385856))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501386048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501393792))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10450_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10448, groups = var_6895, pad = var_10450_pad_0, pad_type = var_10450_pad_type_0, strides = var_10446, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_607_cast_fp16)[name = tensor("op_10450_cast_fp16")]; + tensor var_10451_split_sizes_0 = const()[name = tensor("op_10451_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10451_axis_0 = const()[name = tensor("op_10451_axis_0"), val = tensor(1)]; + tensor var_10451_cast_fp16_0, tensor var_10451_cast_fp16_1 = split(axis = var_10451_axis_0, split_sizes = var_10451_split_sizes_0, x = var_10450_cast_fp16)[name = tensor("op_10451_cast_fp16")]; + tensor var_10453_mode_0 = const()[name = tensor("op_10453_mode_0"), val = tensor("EXACT")]; + tensor var_10453_cast_fp16 = gelu(mode = var_10453_mode_0, x = var_10451_cast_fp16_1)[name = tensor("op_10453_cast_fp16")]; + tensor input_609_cast_fp16 = mul(x = var_10451_cast_fp16_0, y = var_10453_cast_fp16)[name = tensor("input_609_cast_fp16")]; + tensor var_10457 = const()[name = tensor("op_10457"), val = tensor([1, 1])]; + tensor var_10459 = const()[name = tensor("op_10459"), val = tensor([1, 1])]; + tensor var_10461_pad_type_0 = const()[name = tensor("op_10461_pad_type_0"), val = tensor("custom")]; + tensor var_10461_pad_0 = const()[name = tensor("op_10461_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501393984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506309248))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506309440)))]; + tensor var_10461_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_10459, groups = var_6895, pad = var_10461_pad_0, pad_type = var_10461_pad_type_0, strides = var_10457, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_609_cast_fp16)[name = tensor("op_10461_cast_fp16")]; + tensor inputs_319_cast_fp16 = add(x = var_10461_cast_fp16, y = inputs_317_cast_fp16)[name = tensor("inputs_319_cast_fp16")]; + tensor hidden_states_415_axes_0 = const()[name = tensor("hidden_states_415_axes_0"), val = tensor([1])]; + tensor hidden_states_415_gamma_0_to_fp16 = const()[name = tensor("hidden_states_415_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506312064)))]; + tensor hidden_states_415_beta_0_to_fp16 = const()[name = tensor("hidden_states_415_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506314688)))]; + tensor var_10477_to_fp16 = const()[name = tensor("op_10477_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_415_cast_fp16 = layer_norm(axes = hidden_states_415_axes_0, beta = hidden_states_415_beta_0_to_fp16, epsilon = var_10477_to_fp16, gamma = hidden_states_415_gamma_0_to_fp16, x = inputs_319_cast_fp16)[name = tensor("hidden_states_415_cast_fp16")]; + tensor var_10492 = const()[name = tensor("op_10492"), val = tensor([1, 1])]; + tensor var_10494 = const()[name = tensor("op_10494"), val = tensor([1, 1])]; + tensor q_213_pad_type_0 = const()[name = tensor("q_213_pad_type_0"), val = tensor("custom")]; + tensor q_213_pad_0 = const()[name = tensor("q_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506317312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1507546176))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_213_cast_fp16 = conv(dilations = var_10494, groups = var_6895, pad = q_213_pad_0, pad_type = q_213_pad_type_0, strides = var_10492, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_415_cast_fp16)[name = tensor("q_213_cast_fp16")]; + tensor var_10498 = const()[name = tensor("op_10498"), val = tensor([1, 1])]; + tensor var_10500 = const()[name = tensor("op_10500"), val = tensor([1, 1])]; + tensor k_213_pad_type_0 = const()[name = tensor("k_213_pad_type_0"), val = tensor("custom")]; + tensor k_213_pad_0 = const()[name = tensor("k_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1507546368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1508775232))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_213_cast_fp16 = conv(dilations = var_10500, groups = var_6895, pad = k_213_pad_0, pad_type = k_213_pad_type_0, strides = var_10498, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_415_cast_fp16)[name = tensor("k_213_cast_fp16")]; + tensor var_10504 = const()[name = tensor("op_10504"), val = tensor([1, 1])]; + tensor var_10506 = const()[name = tensor("op_10506"), val = tensor([1, 1])]; + tensor v_213_pad_type_0 = const()[name = tensor("v_213_pad_type_0"), val = tensor("custom")]; + tensor v_213_pad_0 = const()[name = tensor("v_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1508775424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1510004288))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_213_cast_fp16 = conv(dilations = var_10506, groups = var_6895, pad = v_213_pad_0, pad_type = v_213_pad_type_0, strides = var_10504, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_415_cast_fp16)[name = tensor("v_213_cast_fp16")]; + tensor var_10510 = const()[name = tensor("op_10510"), val = tensor([1, 20, 64, -1])]; + tensor var_10511_cast_fp16 = reshape(shape = var_10510, x = q_213_cast_fp16)[name = tensor("op_10511_cast_fp16")]; + tensor var_10512 = const()[name = tensor("op_10512"), val = tensor([1, 20, 64, -1])]; + tensor var_10513_cast_fp16 = reshape(shape = var_10512, x = k_213_cast_fp16)[name = tensor("op_10513_cast_fp16")]; + tensor var_10514 = const()[name = tensor("op_10514"), val = tensor([1, 20, 64, -1])]; + tensor var_10515_cast_fp16 = reshape(shape = var_10514, x = v_213_cast_fp16)[name = tensor("op_10515_cast_fp16")]; + tensor attn_weights_425_transpose_x_0 = const()[name = tensor("attn_weights_425_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_425_transpose_y_0 = const()[name = tensor("attn_weights_425_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_10511_cast_fp16, y = var_10513_cast_fp16)[name = tensor("attn_weights_425_cast_fp16")]; + tensor attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_427_cast_fp16")]; + tensor var_10519_cast_fp16 = softmax(axis = var_6879, x = attn_weights_427_cast_fp16)[name = tensor("op_10519_cast_fp16")]; + tensor attn_213_transpose_x_0 = const()[name = tensor("attn_213_transpose_x_0"), val = tensor(false)]; + tensor attn_213_transpose_y_0 = const()[name = tensor("attn_213_transpose_y_0"), val = tensor(true)]; + tensor attn_213_cast_fp16 = matmul(transpose_x = attn_213_transpose_x_0, transpose_y = attn_213_transpose_y_0, x = var_10515_cast_fp16, y = var_10519_cast_fp16)[name = tensor("attn_213_cast_fp16")]; + tensor var_10523 = const()[name = tensor("op_10523"), val = tensor([1, 1280, 1, -1])]; + tensor input_611_cast_fp16 = reshape(shape = var_10523, x = attn_213_cast_fp16)[name = tensor("input_611_cast_fp16")]; + tensor var_10528 = const()[name = tensor("op_10528"), val = tensor([1, 1])]; + tensor var_10530 = const()[name = tensor("op_10530"), val = tensor([1, 1])]; + tensor var_10532_pad_type_0 = const()[name = tensor("op_10532_pad_type_0"), val = tensor("custom")]; + tensor var_10532_pad_0 = const()[name = tensor("op_10532_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1510004480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511233344))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511233536)))]; + tensor var_10532_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_10530, groups = var_6895, pad = var_10532_pad_0, pad_type = var_10532_pad_type_0, strides = var_10528, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = tensor("op_10532_cast_fp16")]; + tensor inputs_321_cast_fp16 = add(x = var_10532_cast_fp16, y = inputs_319_cast_fp16)[name = tensor("inputs_321_cast_fp16")]; + tensor hidden_states_417_axes_0 = const()[name = tensor("hidden_states_417_axes_0"), val = tensor([1])]; + tensor hidden_states_417_gamma_0_to_fp16 = const()[name = tensor("hidden_states_417_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511236160)))]; + tensor hidden_states_417_beta_0_to_fp16 = const()[name = tensor("hidden_states_417_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511238784)))]; + tensor var_10542_to_fp16 = const()[name = tensor("op_10542_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_417_cast_fp16 = layer_norm(axes = hidden_states_417_axes_0, beta = hidden_states_417_beta_0_to_fp16, epsilon = var_10542_to_fp16, gamma = hidden_states_417_gamma_0_to_fp16, x = inputs_321_cast_fp16)[name = tensor("hidden_states_417_cast_fp16")]; + tensor var_10557 = const()[name = tensor("op_10557"), val = tensor([1, 1])]; + tensor var_10559 = const()[name = tensor("op_10559"), val = tensor([1, 1])]; + tensor q_215_pad_type_0 = const()[name = tensor("q_215_pad_type_0"), val = tensor("custom")]; + tensor q_215_pad_0 = const()[name = tensor("q_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511241408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1512470272))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_215_cast_fp16 = conv(dilations = var_10559, groups = var_6895, pad = q_215_pad_0, pad_type = q_215_pad_type_0, strides = var_10557, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_417_cast_fp16)[name = tensor("q_215_cast_fp16")]; + tensor var_10563 = const()[name = tensor("op_10563"), val = tensor([1, 1])]; + tensor var_10565 = const()[name = tensor("op_10565"), val = tensor([1, 1])]; + tensor k_215_pad_type_0 = const()[name = tensor("k_215_pad_type_0"), val = tensor("custom")]; + tensor k_215_pad_0 = const()[name = tensor("k_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1512470464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1514436608))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_215_cast_fp16 = conv(dilations = var_10565, groups = var_6895, pad = k_215_pad_0, pad_type = k_215_pad_type_0, strides = var_10563, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_215_cast_fp16")]; + tensor var_10569 = const()[name = tensor("op_10569"), val = tensor([1, 1])]; + tensor var_10571 = const()[name = tensor("op_10571"), val = tensor([1, 1])]; + tensor v_215_pad_type_0 = const()[name = tensor("v_215_pad_type_0"), val = tensor("custom")]; + tensor v_215_pad_0 = const()[name = tensor("v_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1514436800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1516402944))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_215_cast_fp16 = conv(dilations = var_10571, groups = var_6895, pad = v_215_pad_0, pad_type = v_215_pad_type_0, strides = var_10569, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_215_cast_fp16")]; + tensor var_10575 = const()[name = tensor("op_10575"), val = tensor([1, 20, 64, -1])]; + tensor var_10576_cast_fp16 = reshape(shape = var_10575, x = q_215_cast_fp16)[name = tensor("op_10576_cast_fp16")]; + tensor var_10577 = const()[name = tensor("op_10577"), val = tensor([1, 20, 64, -1])]; + tensor var_10578_cast_fp16 = reshape(shape = var_10577, x = k_215_cast_fp16)[name = tensor("op_10578_cast_fp16")]; + tensor var_10579 = const()[name = tensor("op_10579"), val = tensor([1, 20, 64, -1])]; + tensor var_10580_cast_fp16 = reshape(shape = var_10579, x = v_215_cast_fp16)[name = tensor("op_10580_cast_fp16")]; + tensor attn_weights_429_transpose_x_0 = const()[name = tensor("attn_weights_429_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_429_transpose_y_0 = const()[name = tensor("attn_weights_429_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_429_cast_fp16 = matmul(transpose_x = attn_weights_429_transpose_x_0, transpose_y = attn_weights_429_transpose_y_0, x = var_10576_cast_fp16, y = var_10578_cast_fp16)[name = tensor("attn_weights_429_cast_fp16")]; + tensor attn_weights_431_cast_fp16 = mul(x = attn_weights_429_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_431_cast_fp16")]; + tensor var_10584_cast_fp16 = softmax(axis = var_6879, x = attn_weights_431_cast_fp16)[name = tensor("op_10584_cast_fp16")]; + tensor attn_215_transpose_x_0 = const()[name = tensor("attn_215_transpose_x_0"), val = tensor(false)]; + tensor attn_215_transpose_y_0 = const()[name = tensor("attn_215_transpose_y_0"), val = tensor(true)]; + tensor attn_215_cast_fp16 = matmul(transpose_x = attn_215_transpose_x_0, transpose_y = attn_215_transpose_y_0, x = var_10580_cast_fp16, y = var_10584_cast_fp16)[name = tensor("attn_215_cast_fp16")]; + tensor var_10588 = const()[name = tensor("op_10588"), val = tensor([1, 1280, 1, -1])]; + tensor input_613_cast_fp16 = reshape(shape = var_10588, x = attn_215_cast_fp16)[name = tensor("input_613_cast_fp16")]; + tensor var_10593 = const()[name = tensor("op_10593"), val = tensor([1, 1])]; + tensor var_10595 = const()[name = tensor("op_10595"), val = tensor([1, 1])]; + tensor var_10597_pad_type_0 = const()[name = tensor("op_10597_pad_type_0"), val = tensor("custom")]; + tensor var_10597_pad_0 = const()[name = tensor("op_10597_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1516403136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517632000))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517632192)))]; + tensor var_10597_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_10595, groups = var_6895, pad = var_10597_pad_0, pad_type = var_10597_pad_type_0, strides = var_10593, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_613_cast_fp16)[name = tensor("op_10597_cast_fp16")]; + tensor inputs_323_cast_fp16 = add(x = var_10597_cast_fp16, y = inputs_321_cast_fp16)[name = tensor("inputs_323_cast_fp16")]; + tensor input_615_axes_0 = const()[name = tensor("input_615_axes_0"), val = tensor([1])]; + tensor input_615_gamma_0_to_fp16 = const()[name = tensor("input_615_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517634816)))]; + tensor input_615_beta_0_to_fp16 = const()[name = tensor("input_615_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517637440)))]; + tensor var_10607_to_fp16 = const()[name = tensor("op_10607_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_615_cast_fp16 = layer_norm(axes = input_615_axes_0, beta = input_615_beta_0_to_fp16, epsilon = var_10607_to_fp16, gamma = input_615_gamma_0_to_fp16, x = inputs_323_cast_fp16)[name = tensor("input_615_cast_fp16")]; + tensor var_10623 = const()[name = tensor("op_10623"), val = tensor([1, 1])]; + tensor var_10625 = const()[name = tensor("op_10625"), val = tensor([1, 1])]; + tensor var_10627_pad_type_0 = const()[name = tensor("op_10627_pad_type_0"), val = tensor("custom")]; + tensor var_10627_pad_0 = const()[name = tensor("op_10627_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517640064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527470528))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527470720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527478464))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10627_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10625, groups = var_6895, pad = var_10627_pad_0, pad_type = var_10627_pad_type_0, strides = var_10623, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = tensor("op_10627_cast_fp16")]; + tensor var_10628_split_sizes_0 = const()[name = tensor("op_10628_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10628_axis_0 = const()[name = tensor("op_10628_axis_0"), val = tensor(1)]; + tensor var_10628_cast_fp16_0, tensor var_10628_cast_fp16_1 = split(axis = var_10628_axis_0, split_sizes = var_10628_split_sizes_0, x = var_10627_cast_fp16)[name = tensor("op_10628_cast_fp16")]; + tensor var_10630_mode_0 = const()[name = tensor("op_10630_mode_0"), val = tensor("EXACT")]; + tensor var_10630_cast_fp16 = gelu(mode = var_10630_mode_0, x = var_10628_cast_fp16_1)[name = tensor("op_10630_cast_fp16")]; + tensor input_617_cast_fp16 = mul(x = var_10628_cast_fp16_0, y = var_10630_cast_fp16)[name = tensor("input_617_cast_fp16")]; + tensor var_10634 = const()[name = tensor("op_10634"), val = tensor([1, 1])]; + tensor var_10636 = const()[name = tensor("op_10636"), val = tensor([1, 1])]; + tensor var_10638_pad_type_0 = const()[name = tensor("op_10638_pad_type_0"), val = tensor("custom")]; + tensor var_10638_pad_0 = const()[name = tensor("op_10638_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527478656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532393920))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532394112)))]; + tensor var_10638_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_10636, groups = var_6895, pad = var_10638_pad_0, pad_type = var_10638_pad_type_0, strides = var_10634, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_617_cast_fp16)[name = tensor("op_10638_cast_fp16")]; + tensor hidden_states_421_cast_fp16 = add(x = var_10638_cast_fp16, y = inputs_323_cast_fp16)[name = tensor("hidden_states_421_cast_fp16")]; + tensor var_10640 = const()[name = tensor("op_10640"), val = tensor([1, 1280, 32, 32])]; + tensor input_619_cast_fp16 = reshape(shape = var_10640, x = hidden_states_421_cast_fp16)[name = tensor("input_619_cast_fp16")]; + tensor var_10644 = const()[name = tensor("op_10644"), val = tensor([1, 1])]; + tensor var_10646 = const()[name = tensor("op_10646"), val = tensor([1, 1])]; + tensor hidden_states_423_pad_type_0 = const()[name = tensor("hidden_states_423_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_423_pad_0 = const()[name = tensor("hidden_states_423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_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(1532396736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533625600))), name = tensor("up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533625792)))]; + tensor hidden_states_423_cast_fp16 = conv(bias = up_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_10646, groups = var_6895, pad = hidden_states_423_pad_0, pad_type = hidden_states_423_pad_type_0, strides = var_10644, weight = up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized, x = input_619_cast_fp16)[name = tensor("hidden_states_423_cast_fp16")]; + tensor hidden_states_425_cast_fp16 = add(x = hidden_states_423_cast_fp16, y = hidden_states_357_cast_fp16)[name = tensor("hidden_states_425_cast_fp16")]; + tensor input_621_interleave_0 = const()[name = tensor("input_621_interleave_0"), val = tensor(false)]; + tensor input_621_cast_fp16 = concat(axis = var_6895, interleave = input_621_interleave_0, values = (hidden_states_425_cast_fp16, res_hidden_states_5_cast_fp16))[name = tensor("input_621_cast_fp16")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 60, 32, 32])]; + tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = input_621_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; + tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; + tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; + tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; + tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 1920, 32, 32])]; + tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533628416)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533632320)))]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533636224)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533640128)))]; + 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_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; + tensor input_625_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("input_625_cast_fp16")]; + tensor var_10664 = const()[name = tensor("op_10664"), val = tensor([1, 1])]; + tensor var_10666 = const()[name = tensor("op_10666"), val = tensor([1, 1])]; + tensor hidden_states_427_pad_type_0 = const()[name = tensor("hidden_states_427_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_427_pad_0 = const()[name = tensor("hidden_states_427_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(1533644032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550232896))), name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 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(1550233088)))]; + tensor hidden_states_427_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_10666, groups = var_6895, pad = hidden_states_427_pad_0, pad_type = hidden_states_427_pad_type_0, strides = var_10664, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized, x = input_625_cast_fp16)[name = tensor("hidden_states_427_cast_fp16")]; + tensor var_10672 = const()[name = tensor("op_10672"), val = tensor([1, 1])]; + tensor var_10674 = const()[name = tensor("op_10674"), 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_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550235712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551464576))), 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(1551464768)))]; + tensor temb_21_cast_fp16 = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_10674, groups = var_6895, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_10672, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_21_cast_fp16")]; + tensor input_629_cast_fp16 = add(x = hidden_states_427_cast_fp16, y = temb_21_cast_fp16)[name = tensor("input_629_cast_fp16")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_629_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; + tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; + tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; + tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; + tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; + 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(1551467392)))]; + 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(1551470016)))]; + 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_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; + tensor input_633_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_633_cast_fp16")]; + tensor var_10684 = const()[name = tensor("op_10684"), val = tensor([1, 1])]; + tensor var_10686 = const()[name = tensor("op_10686"), val = tensor([1, 1])]; + tensor hidden_states_429_pad_type_0 = const()[name = tensor("hidden_states_429_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_429_pad_0 = const()[name = tensor("hidden_states_429_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(1551472640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1562531904))), 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(1562532096)))]; + tensor hidden_states_429_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_10686, groups = var_6895, pad = hidden_states_429_pad_0, pad_type = hidden_states_429_pad_type_0, strides = var_10684, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized, x = input_633_cast_fp16)[name = tensor("hidden_states_429_cast_fp16")]; + tensor var_10691 = const()[name = tensor("op_10691"), val = tensor([1, 1])]; + tensor var_10693 = const()[name = tensor("op_10693"), 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(1562534720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564377984))), name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 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(1564378176)))]; + tensor x_9_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_10693, groups = var_6895, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_10691, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_621_cast_fp16)[name = tensor("x_9_cast_fp16")]; + tensor hidden_states_431_cast_fp16 = add(x = x_9_cast_fp16, y = hidden_states_429_cast_fp16)[name = tensor("hidden_states_431_cast_fp16")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = hidden_states_431_cast_fp16)[name = tensor("reshape_116_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; + tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; + tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; + tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; + tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564380800)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564383424)))]; + 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_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; + tensor var_10731 = const()[name = tensor("op_10731"), val = tensor([1, 1])]; + tensor var_10733 = const()[name = tensor("op_10733"), val = tensor([1, 1])]; + tensor hidden_states_433_pad_type_0 = const()[name = tensor("hidden_states_433_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_433_pad_0 = const()[name = tensor("hidden_states_433_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564386048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565614912))), name = tensor("up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565615104)))]; + tensor hidden_states_433_cast_fp16 = conv(bias = up_blocks_0_attentions_2_proj_in_bias_to_fp16, dilations = var_10733, groups = var_6895, pad = hidden_states_433_pad_0, pad_type = hidden_states_433_pad_type_0, strides = var_10731, weight = up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized, x = add_59_cast_fp16)[name = tensor("hidden_states_433_cast_fp16")]; + tensor var_10738 = const()[name = tensor("op_10738"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_325_cast_fp16 = reshape(shape = var_10738, x = hidden_states_433_cast_fp16)[name = tensor("inputs_325_cast_fp16")]; + tensor hidden_states_435_axes_0 = const()[name = tensor("hidden_states_435_axes_0"), val = tensor([1])]; + tensor hidden_states_435_gamma_0_to_fp16 = const()[name = tensor("hidden_states_435_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565617728)))]; + tensor hidden_states_435_beta_0_to_fp16 = const()[name = tensor("hidden_states_435_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565620352)))]; + tensor var_10754_to_fp16 = const()[name = tensor("op_10754_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_435_cast_fp16 = layer_norm(axes = hidden_states_435_axes_0, beta = hidden_states_435_beta_0_to_fp16, epsilon = var_10754_to_fp16, gamma = hidden_states_435_gamma_0_to_fp16, x = inputs_325_cast_fp16)[name = tensor("hidden_states_435_cast_fp16")]; + tensor var_10769 = const()[name = tensor("op_10769"), val = tensor([1, 1])]; + tensor var_10771 = const()[name = tensor("op_10771"), val = tensor([1, 1])]; + tensor q_217_pad_type_0 = const()[name = tensor("q_217_pad_type_0"), val = tensor("custom")]; + tensor q_217_pad_0 = const()[name = tensor("q_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1565622976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1566851840))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_217_cast_fp16 = conv(dilations = var_10771, groups = var_6895, pad = q_217_pad_0, pad_type = q_217_pad_type_0, strides = var_10769, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_435_cast_fp16)[name = tensor("q_217_cast_fp16")]; + tensor var_10775 = const()[name = tensor("op_10775"), val = tensor([1, 1])]; + tensor var_10777 = const()[name = tensor("op_10777"), val = tensor([1, 1])]; + tensor k_217_pad_type_0 = const()[name = tensor("k_217_pad_type_0"), val = tensor("custom")]; + tensor k_217_pad_0 = const()[name = tensor("k_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1566852032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1568080896))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_217_cast_fp16 = conv(dilations = var_10777, groups = var_6895, pad = k_217_pad_0, pad_type = k_217_pad_type_0, strides = var_10775, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_435_cast_fp16)[name = tensor("k_217_cast_fp16")]; + tensor var_10781 = const()[name = tensor("op_10781"), val = tensor([1, 1])]; + tensor var_10783 = const()[name = tensor("op_10783"), val = tensor([1, 1])]; + tensor v_217_pad_type_0 = const()[name = tensor("v_217_pad_type_0"), val = tensor("custom")]; + tensor v_217_pad_0 = const()[name = tensor("v_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1568081088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1569309952))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_217_cast_fp16 = conv(dilations = var_10783, groups = var_6895, pad = v_217_pad_0, pad_type = v_217_pad_type_0, strides = var_10781, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_435_cast_fp16)[name = tensor("v_217_cast_fp16")]; + tensor var_10787 = const()[name = tensor("op_10787"), val = tensor([1, 20, 64, -1])]; + tensor var_10788_cast_fp16 = reshape(shape = var_10787, x = q_217_cast_fp16)[name = tensor("op_10788_cast_fp16")]; + tensor var_10789 = const()[name = tensor("op_10789"), val = tensor([1, 20, 64, -1])]; + tensor var_10790_cast_fp16 = reshape(shape = var_10789, x = k_217_cast_fp16)[name = tensor("op_10790_cast_fp16")]; + tensor var_10791 = const()[name = tensor("op_10791"), val = tensor([1, 20, 64, -1])]; + tensor var_10792_cast_fp16 = reshape(shape = var_10791, x = v_217_cast_fp16)[name = tensor("op_10792_cast_fp16")]; + tensor attn_weights_433_transpose_x_0 = const()[name = tensor("attn_weights_433_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_433_transpose_y_0 = const()[name = tensor("attn_weights_433_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_10788_cast_fp16, y = var_10790_cast_fp16)[name = tensor("attn_weights_433_cast_fp16")]; + tensor attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_435_cast_fp16")]; + tensor var_10796_cast_fp16 = softmax(axis = var_6879, x = attn_weights_435_cast_fp16)[name = tensor("op_10796_cast_fp16")]; + tensor attn_217_transpose_x_0 = const()[name = tensor("attn_217_transpose_x_0"), val = tensor(false)]; + tensor attn_217_transpose_y_0 = const()[name = tensor("attn_217_transpose_y_0"), val = tensor(true)]; + tensor attn_217_cast_fp16 = matmul(transpose_x = attn_217_transpose_x_0, transpose_y = attn_217_transpose_y_0, x = var_10792_cast_fp16, y = var_10796_cast_fp16)[name = tensor("attn_217_cast_fp16")]; + tensor var_10800 = const()[name = tensor("op_10800"), val = tensor([1, 1280, 1, -1])]; + tensor input_637_cast_fp16 = reshape(shape = var_10800, x = attn_217_cast_fp16)[name = tensor("input_637_cast_fp16")]; + tensor var_10805 = const()[name = tensor("op_10805"), val = tensor([1, 1])]; + tensor var_10807 = const()[name = tensor("op_10807"), val = tensor([1, 1])]; + tensor var_10809_pad_type_0 = const()[name = tensor("op_10809_pad_type_0"), val = tensor("custom")]; + tensor var_10809_pad_0 = const()[name = tensor("op_10809_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1569310144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570539008))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570539200)))]; + tensor var_10809_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_10807, groups = var_6895, pad = var_10809_pad_0, pad_type = var_10809_pad_type_0, strides = var_10805, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_637_cast_fp16)[name = tensor("op_10809_cast_fp16")]; + tensor inputs_327_cast_fp16 = add(x = var_10809_cast_fp16, y = inputs_325_cast_fp16)[name = tensor("inputs_327_cast_fp16")]; + tensor hidden_states_437_axes_0 = const()[name = tensor("hidden_states_437_axes_0"), val = tensor([1])]; + tensor hidden_states_437_gamma_0_to_fp16 = const()[name = tensor("hidden_states_437_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570541824)))]; + tensor hidden_states_437_beta_0_to_fp16 = const()[name = tensor("hidden_states_437_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570544448)))]; + tensor var_10819_to_fp16 = const()[name = tensor("op_10819_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_437_cast_fp16 = layer_norm(axes = hidden_states_437_axes_0, beta = hidden_states_437_beta_0_to_fp16, epsilon = var_10819_to_fp16, gamma = hidden_states_437_gamma_0_to_fp16, x = inputs_327_cast_fp16)[name = tensor("hidden_states_437_cast_fp16")]; + tensor var_10834 = const()[name = tensor("op_10834"), val = tensor([1, 1])]; + tensor var_10836 = const()[name = tensor("op_10836"), val = tensor([1, 1])]; + tensor q_219_pad_type_0 = const()[name = tensor("q_219_pad_type_0"), val = tensor("custom")]; + tensor q_219_pad_0 = const()[name = tensor("q_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1570547072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1571775936))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_219_cast_fp16 = conv(dilations = var_10836, groups = var_6895, pad = q_219_pad_0, pad_type = q_219_pad_type_0, strides = var_10834, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_437_cast_fp16)[name = tensor("q_219_cast_fp16")]; + tensor var_10840 = const()[name = tensor("op_10840"), val = tensor([1, 1])]; + tensor var_10842 = const()[name = tensor("op_10842"), val = tensor([1, 1])]; + tensor k_219_pad_type_0 = const()[name = tensor("k_219_pad_type_0"), val = tensor("custom")]; + tensor k_219_pad_0 = const()[name = tensor("k_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1571776128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1573742272))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_219_cast_fp16 = conv(dilations = var_10842, groups = var_6895, pad = k_219_pad_0, pad_type = k_219_pad_type_0, strides = var_10840, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_219_cast_fp16")]; + tensor var_10846 = const()[name = tensor("op_10846"), val = tensor([1, 1])]; + tensor var_10848 = const()[name = tensor("op_10848"), val = tensor([1, 1])]; + tensor v_219_pad_type_0 = const()[name = tensor("v_219_pad_type_0"), val = tensor("custom")]; + tensor v_219_pad_0 = const()[name = tensor("v_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1573742464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1575708608))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_219_cast_fp16 = conv(dilations = var_10848, groups = var_6895, pad = v_219_pad_0, pad_type = v_219_pad_type_0, strides = var_10846, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_219_cast_fp16")]; + tensor var_10852 = const()[name = tensor("op_10852"), val = tensor([1, 20, 64, -1])]; + tensor var_10853_cast_fp16 = reshape(shape = var_10852, x = q_219_cast_fp16)[name = tensor("op_10853_cast_fp16")]; + tensor var_10854 = const()[name = tensor("op_10854"), val = tensor([1, 20, 64, -1])]; + tensor var_10855_cast_fp16 = reshape(shape = var_10854, x = k_219_cast_fp16)[name = tensor("op_10855_cast_fp16")]; + tensor var_10856 = const()[name = tensor("op_10856"), val = tensor([1, 20, 64, -1])]; + tensor var_10857_cast_fp16 = reshape(shape = var_10856, x = v_219_cast_fp16)[name = tensor("op_10857_cast_fp16")]; + tensor attn_weights_437_transpose_x_0 = const()[name = tensor("attn_weights_437_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_437_transpose_y_0 = const()[name = tensor("attn_weights_437_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_437_cast_fp16 = matmul(transpose_x = attn_weights_437_transpose_x_0, transpose_y = attn_weights_437_transpose_y_0, x = var_10853_cast_fp16, y = var_10855_cast_fp16)[name = tensor("attn_weights_437_cast_fp16")]; + tensor attn_weights_439_cast_fp16 = mul(x = attn_weights_437_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_439_cast_fp16")]; + tensor var_10861_cast_fp16 = softmax(axis = var_6879, x = attn_weights_439_cast_fp16)[name = tensor("op_10861_cast_fp16")]; + tensor attn_219_transpose_x_0 = const()[name = tensor("attn_219_transpose_x_0"), val = tensor(false)]; + tensor attn_219_transpose_y_0 = const()[name = tensor("attn_219_transpose_y_0"), val = tensor(true)]; + tensor attn_219_cast_fp16 = matmul(transpose_x = attn_219_transpose_x_0, transpose_y = attn_219_transpose_y_0, x = var_10857_cast_fp16, y = var_10861_cast_fp16)[name = tensor("attn_219_cast_fp16")]; + tensor var_10865 = const()[name = tensor("op_10865"), val = tensor([1, 1280, 1, -1])]; + tensor input_639_cast_fp16 = reshape(shape = var_10865, x = attn_219_cast_fp16)[name = tensor("input_639_cast_fp16")]; + tensor var_10870 = const()[name = tensor("op_10870"), val = tensor([1, 1])]; + tensor var_10872 = const()[name = tensor("op_10872"), val = tensor([1, 1])]; + tensor var_10874_pad_type_0 = const()[name = tensor("op_10874_pad_type_0"), val = tensor("custom")]; + tensor var_10874_pad_0 = const()[name = tensor("op_10874_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1575708800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576937664))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576937856)))]; + tensor var_10874_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_10872, groups = var_6895, pad = var_10874_pad_0, pad_type = var_10874_pad_type_0, strides = var_10870, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_639_cast_fp16)[name = tensor("op_10874_cast_fp16")]; + tensor inputs_329_cast_fp16 = add(x = var_10874_cast_fp16, y = inputs_327_cast_fp16)[name = tensor("inputs_329_cast_fp16")]; + tensor input_641_axes_0 = const()[name = tensor("input_641_axes_0"), val = tensor([1])]; + tensor input_641_gamma_0_to_fp16 = const()[name = tensor("input_641_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576940480)))]; + tensor input_641_beta_0_to_fp16 = const()[name = tensor("input_641_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576943104)))]; + tensor var_10884_to_fp16 = const()[name = tensor("op_10884_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_641_cast_fp16 = layer_norm(axes = input_641_axes_0, beta = input_641_beta_0_to_fp16, epsilon = var_10884_to_fp16, gamma = input_641_gamma_0_to_fp16, x = inputs_329_cast_fp16)[name = tensor("input_641_cast_fp16")]; + tensor var_10900 = const()[name = tensor("op_10900"), val = tensor([1, 1])]; + tensor var_10902 = const()[name = tensor("op_10902"), val = tensor([1, 1])]; + tensor var_10904_pad_type_0 = const()[name = tensor("op_10904_pad_type_0"), val = tensor("custom")]; + tensor var_10904_pad_0 = const()[name = tensor("op_10904_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1576945728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586776192))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_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(1586776384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586784128))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10904_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10902, groups = var_6895, pad = var_10904_pad_0, pad_type = var_10904_pad_type_0, strides = var_10900, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_641_cast_fp16)[name = tensor("op_10904_cast_fp16")]; + tensor var_10905_split_sizes_0 = const()[name = tensor("op_10905_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10905_axis_0 = const()[name = tensor("op_10905_axis_0"), val = tensor(1)]; + tensor var_10905_cast_fp16_0, tensor var_10905_cast_fp16_1 = split(axis = var_10905_axis_0, split_sizes = var_10905_split_sizes_0, x = var_10904_cast_fp16)[name = tensor("op_10905_cast_fp16")]; + tensor var_10907_mode_0 = const()[name = tensor("op_10907_mode_0"), val = tensor("EXACT")]; + tensor var_10907_cast_fp16 = gelu(mode = var_10907_mode_0, x = var_10905_cast_fp16_1)[name = tensor("op_10907_cast_fp16")]; + tensor input_643_cast_fp16 = mul(x = var_10905_cast_fp16_0, y = var_10907_cast_fp16)[name = tensor("input_643_cast_fp16")]; + tensor var_10911 = const()[name = tensor("op_10911"), val = tensor([1, 1])]; + tensor var_10913 = const()[name = tensor("op_10913"), val = tensor([1, 1])]; + tensor var_10915_pad_type_0 = const()[name = tensor("op_10915_pad_type_0"), val = tensor("custom")]; + tensor var_10915_pad_0 = const()[name = tensor("op_10915_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_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(1586784320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591699584))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591699776)))]; + tensor var_10915_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_10913, groups = var_6895, pad = var_10915_pad_0, pad_type = var_10915_pad_type_0, strides = var_10911, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_643_cast_fp16)[name = tensor("op_10915_cast_fp16")]; + tensor inputs_331_cast_fp16 = add(x = var_10915_cast_fp16, y = inputs_329_cast_fp16)[name = tensor("inputs_331_cast_fp16")]; + tensor hidden_states_441_axes_0 = const()[name = tensor("hidden_states_441_axes_0"), val = tensor([1])]; + tensor hidden_states_441_gamma_0_to_fp16 = const()[name = tensor("hidden_states_441_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591702400)))]; + tensor hidden_states_441_beta_0_to_fp16 = const()[name = tensor("hidden_states_441_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591705024)))]; + tensor var_10931_to_fp16 = const()[name = tensor("op_10931_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_441_cast_fp16 = layer_norm(axes = hidden_states_441_axes_0, beta = hidden_states_441_beta_0_to_fp16, epsilon = var_10931_to_fp16, gamma = hidden_states_441_gamma_0_to_fp16, x = inputs_331_cast_fp16)[name = tensor("hidden_states_441_cast_fp16")]; + tensor var_10946 = const()[name = tensor("op_10946"), val = tensor([1, 1])]; + tensor var_10948 = const()[name = tensor("op_10948"), val = tensor([1, 1])]; + tensor q_221_pad_type_0 = const()[name = tensor("q_221_pad_type_0"), val = tensor("custom")]; + tensor q_221_pad_0 = const()[name = tensor("q_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591707648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1592936512))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_221_cast_fp16 = conv(dilations = var_10948, groups = var_6895, pad = q_221_pad_0, pad_type = q_221_pad_type_0, strides = var_10946, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_441_cast_fp16)[name = tensor("q_221_cast_fp16")]; + tensor var_10952 = const()[name = tensor("op_10952"), val = tensor([1, 1])]; + tensor var_10954 = const()[name = tensor("op_10954"), val = tensor([1, 1])]; + tensor k_221_pad_type_0 = const()[name = tensor("k_221_pad_type_0"), val = tensor("custom")]; + tensor k_221_pad_0 = const()[name = tensor("k_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1592936704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1594165568))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_221_cast_fp16 = conv(dilations = var_10954, groups = var_6895, pad = k_221_pad_0, pad_type = k_221_pad_type_0, strides = var_10952, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_441_cast_fp16)[name = tensor("k_221_cast_fp16")]; + tensor var_10958 = const()[name = tensor("op_10958"), val = tensor([1, 1])]; + tensor var_10960 = const()[name = tensor("op_10960"), val = tensor([1, 1])]; + tensor v_221_pad_type_0 = const()[name = tensor("v_221_pad_type_0"), val = tensor("custom")]; + tensor v_221_pad_0 = const()[name = tensor("v_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1594165760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1595394624))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_221_cast_fp16 = conv(dilations = var_10960, groups = var_6895, pad = v_221_pad_0, pad_type = v_221_pad_type_0, strides = var_10958, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_441_cast_fp16)[name = tensor("v_221_cast_fp16")]; + tensor var_10964 = const()[name = tensor("op_10964"), val = tensor([1, 20, 64, -1])]; + tensor var_10965_cast_fp16 = reshape(shape = var_10964, x = q_221_cast_fp16)[name = tensor("op_10965_cast_fp16")]; + tensor var_10966 = const()[name = tensor("op_10966"), val = tensor([1, 20, 64, -1])]; + tensor var_10967_cast_fp16 = reshape(shape = var_10966, x = k_221_cast_fp16)[name = tensor("op_10967_cast_fp16")]; + tensor var_10968 = const()[name = tensor("op_10968"), val = tensor([1, 20, 64, -1])]; + tensor var_10969_cast_fp16 = reshape(shape = var_10968, x = v_221_cast_fp16)[name = tensor("op_10969_cast_fp16")]; + tensor attn_weights_441_transpose_x_0 = const()[name = tensor("attn_weights_441_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_441_transpose_y_0 = const()[name = tensor("attn_weights_441_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_10965_cast_fp16, y = var_10967_cast_fp16)[name = tensor("attn_weights_441_cast_fp16")]; + tensor attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_443_cast_fp16")]; + tensor var_10973_cast_fp16 = softmax(axis = var_6879, x = attn_weights_443_cast_fp16)[name = tensor("op_10973_cast_fp16")]; + tensor attn_221_transpose_x_0 = const()[name = tensor("attn_221_transpose_x_0"), val = tensor(false)]; + tensor attn_221_transpose_y_0 = const()[name = tensor("attn_221_transpose_y_0"), val = tensor(true)]; + tensor attn_221_cast_fp16 = matmul(transpose_x = attn_221_transpose_x_0, transpose_y = attn_221_transpose_y_0, x = var_10969_cast_fp16, y = var_10973_cast_fp16)[name = tensor("attn_221_cast_fp16")]; + tensor var_10977 = const()[name = tensor("op_10977"), val = tensor([1, 1280, 1, -1])]; + tensor input_645_cast_fp16 = reshape(shape = var_10977, x = attn_221_cast_fp16)[name = tensor("input_645_cast_fp16")]; + tensor var_10982 = const()[name = tensor("op_10982"), val = tensor([1, 1])]; + tensor var_10984 = const()[name = tensor("op_10984"), val = tensor([1, 1])]; + tensor var_10986_pad_type_0 = const()[name = tensor("op_10986_pad_type_0"), val = tensor("custom")]; + tensor var_10986_pad_0 = const()[name = tensor("op_10986_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1595394816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596623680))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596623872)))]; + tensor var_10986_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_10984, groups = var_6895, pad = var_10986_pad_0, pad_type = var_10986_pad_type_0, strides = var_10982, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_645_cast_fp16)[name = tensor("op_10986_cast_fp16")]; + tensor inputs_333_cast_fp16 = add(x = var_10986_cast_fp16, y = inputs_331_cast_fp16)[name = tensor("inputs_333_cast_fp16")]; + tensor hidden_states_443_axes_0 = const()[name = tensor("hidden_states_443_axes_0"), val = tensor([1])]; + tensor hidden_states_443_gamma_0_to_fp16 = const()[name = tensor("hidden_states_443_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596626496)))]; + tensor hidden_states_443_beta_0_to_fp16 = const()[name = tensor("hidden_states_443_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596629120)))]; + tensor var_10996_to_fp16 = const()[name = tensor("op_10996_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_443_cast_fp16 = layer_norm(axes = hidden_states_443_axes_0, beta = hidden_states_443_beta_0_to_fp16, epsilon = var_10996_to_fp16, gamma = hidden_states_443_gamma_0_to_fp16, x = inputs_333_cast_fp16)[name = tensor("hidden_states_443_cast_fp16")]; + tensor var_11011 = const()[name = tensor("op_11011"), val = tensor([1, 1])]; + tensor var_11013 = const()[name = tensor("op_11013"), val = tensor([1, 1])]; + tensor q_223_pad_type_0 = const()[name = tensor("q_223_pad_type_0"), val = tensor("custom")]; + tensor q_223_pad_0 = const()[name = tensor("q_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596631744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1597860608))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_223_cast_fp16 = conv(dilations = var_11013, groups = var_6895, pad = q_223_pad_0, pad_type = q_223_pad_type_0, strides = var_11011, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_443_cast_fp16)[name = tensor("q_223_cast_fp16")]; + tensor var_11017 = const()[name = tensor("op_11017"), val = tensor([1, 1])]; + tensor var_11019 = const()[name = tensor("op_11019"), val = tensor([1, 1])]; + tensor k_223_pad_type_0 = const()[name = tensor("k_223_pad_type_0"), val = tensor("custom")]; + tensor k_223_pad_0 = const()[name = tensor("k_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1597860800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1599826944))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_223_cast_fp16 = conv(dilations = var_11019, groups = var_6895, pad = k_223_pad_0, pad_type = k_223_pad_type_0, strides = var_11017, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_223_cast_fp16")]; + tensor var_11023 = const()[name = tensor("op_11023"), val = tensor([1, 1])]; + tensor var_11025 = const()[name = tensor("op_11025"), val = tensor([1, 1])]; + tensor v_223_pad_type_0 = const()[name = tensor("v_223_pad_type_0"), val = tensor("custom")]; + tensor v_223_pad_0 = const()[name = tensor("v_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1599827136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601793280))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_223_cast_fp16 = conv(dilations = var_11025, groups = var_6895, pad = v_223_pad_0, pad_type = v_223_pad_type_0, strides = var_11023, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_223_cast_fp16")]; + tensor var_11029 = const()[name = tensor("op_11029"), val = tensor([1, 20, 64, -1])]; + tensor var_11030_cast_fp16 = reshape(shape = var_11029, x = q_223_cast_fp16)[name = tensor("op_11030_cast_fp16")]; + tensor var_11031 = const()[name = tensor("op_11031"), val = tensor([1, 20, 64, -1])]; + tensor var_11032_cast_fp16 = reshape(shape = var_11031, x = k_223_cast_fp16)[name = tensor("op_11032_cast_fp16")]; + tensor var_11033 = const()[name = tensor("op_11033"), val = tensor([1, 20, 64, -1])]; + tensor var_11034_cast_fp16 = reshape(shape = var_11033, x = v_223_cast_fp16)[name = tensor("op_11034_cast_fp16")]; + tensor attn_weights_445_transpose_x_0 = const()[name = tensor("attn_weights_445_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_445_transpose_y_0 = const()[name = tensor("attn_weights_445_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_445_cast_fp16 = matmul(transpose_x = attn_weights_445_transpose_x_0, transpose_y = attn_weights_445_transpose_y_0, x = var_11030_cast_fp16, y = var_11032_cast_fp16)[name = tensor("attn_weights_445_cast_fp16")]; + tensor attn_weights_447_cast_fp16 = mul(x = attn_weights_445_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_447_cast_fp16")]; + tensor var_11038_cast_fp16 = softmax(axis = var_6879, x = attn_weights_447_cast_fp16)[name = tensor("op_11038_cast_fp16")]; + tensor attn_223_transpose_x_0 = const()[name = tensor("attn_223_transpose_x_0"), val = tensor(false)]; + tensor attn_223_transpose_y_0 = const()[name = tensor("attn_223_transpose_y_0"), val = tensor(true)]; + tensor attn_223_cast_fp16 = matmul(transpose_x = attn_223_transpose_x_0, transpose_y = attn_223_transpose_y_0, x = var_11034_cast_fp16, y = var_11038_cast_fp16)[name = tensor("attn_223_cast_fp16")]; + tensor var_11042 = const()[name = tensor("op_11042"), val = tensor([1, 1280, 1, -1])]; + tensor input_647_cast_fp16 = reshape(shape = var_11042, x = attn_223_cast_fp16)[name = tensor("input_647_cast_fp16")]; + tensor var_11047 = const()[name = tensor("op_11047"), val = tensor([1, 1])]; + tensor var_11049 = const()[name = tensor("op_11049"), val = tensor([1, 1])]; + tensor var_11051_pad_type_0 = const()[name = tensor("op_11051_pad_type_0"), val = tensor("custom")]; + tensor var_11051_pad_0 = const()[name = tensor("op_11051_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601793472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603022336))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603022528)))]; + tensor var_11051_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_11049, groups = var_6895, pad = var_11051_pad_0, pad_type = var_11051_pad_type_0, strides = var_11047, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_647_cast_fp16)[name = tensor("op_11051_cast_fp16")]; + tensor inputs_335_cast_fp16 = add(x = var_11051_cast_fp16, y = inputs_333_cast_fp16)[name = tensor("inputs_335_cast_fp16")]; + tensor input_649_axes_0 = const()[name = tensor("input_649_axes_0"), val = tensor([1])]; + tensor input_649_gamma_0_to_fp16 = const()[name = tensor("input_649_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603025152)))]; + tensor input_649_beta_0_to_fp16 = const()[name = tensor("input_649_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603027776)))]; + tensor var_11061_to_fp16 = const()[name = tensor("op_11061_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_649_cast_fp16 = layer_norm(axes = input_649_axes_0, beta = input_649_beta_0_to_fp16, epsilon = var_11061_to_fp16, gamma = input_649_gamma_0_to_fp16, x = inputs_335_cast_fp16)[name = tensor("input_649_cast_fp16")]; + tensor var_11077 = const()[name = tensor("op_11077"), val = tensor([1, 1])]; + tensor var_11079 = const()[name = tensor("op_11079"), val = tensor([1, 1])]; + tensor var_11081_pad_type_0 = const()[name = tensor("op_11081_pad_type_0"), val = tensor("custom")]; + tensor var_11081_pad_0 = const()[name = tensor("op_11081_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603030400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612860864))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612861056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612868800))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11081_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11079, groups = var_6895, pad = var_11081_pad_0, pad_type = var_11081_pad_type_0, strides = var_11077, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_649_cast_fp16)[name = tensor("op_11081_cast_fp16")]; + tensor var_11082_split_sizes_0 = const()[name = tensor("op_11082_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11082_axis_0 = const()[name = tensor("op_11082_axis_0"), val = tensor(1)]; + tensor var_11082_cast_fp16_0, tensor var_11082_cast_fp16_1 = split(axis = var_11082_axis_0, split_sizes = var_11082_split_sizes_0, x = var_11081_cast_fp16)[name = tensor("op_11082_cast_fp16")]; + tensor var_11084_mode_0 = const()[name = tensor("op_11084_mode_0"), val = tensor("EXACT")]; + tensor var_11084_cast_fp16 = gelu(mode = var_11084_mode_0, x = var_11082_cast_fp16_1)[name = tensor("op_11084_cast_fp16")]; + tensor input_651_cast_fp16 = mul(x = var_11082_cast_fp16_0, y = var_11084_cast_fp16)[name = tensor("input_651_cast_fp16")]; + tensor var_11088 = const()[name = tensor("op_11088"), val = tensor([1, 1])]; + tensor var_11090 = const()[name = tensor("op_11090"), val = tensor([1, 1])]; + tensor var_11092_pad_type_0 = const()[name = tensor("op_11092_pad_type_0"), val = tensor("custom")]; + tensor var_11092_pad_0 = const()[name = tensor("op_11092_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612868992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617784256))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617784448)))]; + tensor var_11092_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_11090, groups = var_6895, pad = var_11092_pad_0, pad_type = var_11092_pad_type_0, strides = var_11088, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_651_cast_fp16)[name = tensor("op_11092_cast_fp16")]; + tensor inputs_337_cast_fp16 = add(x = var_11092_cast_fp16, y = inputs_335_cast_fp16)[name = tensor("inputs_337_cast_fp16")]; + tensor hidden_states_447_axes_0 = const()[name = tensor("hidden_states_447_axes_0"), val = tensor([1])]; + tensor hidden_states_447_gamma_0_to_fp16 = const()[name = tensor("hidden_states_447_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617787072)))]; + tensor hidden_states_447_beta_0_to_fp16 = const()[name = tensor("hidden_states_447_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617789696)))]; + tensor var_11108_to_fp16 = const()[name = tensor("op_11108_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_447_cast_fp16 = layer_norm(axes = hidden_states_447_axes_0, beta = hidden_states_447_beta_0_to_fp16, epsilon = var_11108_to_fp16, gamma = hidden_states_447_gamma_0_to_fp16, x = inputs_337_cast_fp16)[name = tensor("hidden_states_447_cast_fp16")]; + tensor var_11123 = const()[name = tensor("op_11123"), val = tensor([1, 1])]; + tensor var_11125 = const()[name = tensor("op_11125"), val = tensor([1, 1])]; + tensor q_225_pad_type_0 = const()[name = tensor("q_225_pad_type_0"), val = tensor("custom")]; + tensor q_225_pad_0 = const()[name = tensor("q_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617792320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1619021184))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_225_cast_fp16 = conv(dilations = var_11125, groups = var_6895, pad = q_225_pad_0, pad_type = q_225_pad_type_0, strides = var_11123, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_447_cast_fp16)[name = tensor("q_225_cast_fp16")]; + tensor var_11129 = const()[name = tensor("op_11129"), val = tensor([1, 1])]; + tensor var_11131 = const()[name = tensor("op_11131"), val = tensor([1, 1])]; + tensor k_225_pad_type_0 = const()[name = tensor("k_225_pad_type_0"), val = tensor("custom")]; + tensor k_225_pad_0 = const()[name = tensor("k_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1619021376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1620250240))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_225_cast_fp16 = conv(dilations = var_11131, groups = var_6895, pad = k_225_pad_0, pad_type = k_225_pad_type_0, strides = var_11129, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_447_cast_fp16)[name = tensor("k_225_cast_fp16")]; + tensor var_11135 = const()[name = tensor("op_11135"), val = tensor([1, 1])]; + tensor var_11137 = const()[name = tensor("op_11137"), val = tensor([1, 1])]; + tensor v_225_pad_type_0 = const()[name = tensor("v_225_pad_type_0"), val = tensor("custom")]; + tensor v_225_pad_0 = const()[name = tensor("v_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1620250432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1621479296))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_225_cast_fp16 = conv(dilations = var_11137, groups = var_6895, pad = v_225_pad_0, pad_type = v_225_pad_type_0, strides = var_11135, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_447_cast_fp16)[name = tensor("v_225_cast_fp16")]; + tensor var_11141 = const()[name = tensor("op_11141"), val = tensor([1, 20, 64, -1])]; + tensor var_11142_cast_fp16 = reshape(shape = var_11141, x = q_225_cast_fp16)[name = tensor("op_11142_cast_fp16")]; + tensor var_11143 = const()[name = tensor("op_11143"), val = tensor([1, 20, 64, -1])]; + tensor var_11144_cast_fp16 = reshape(shape = var_11143, x = k_225_cast_fp16)[name = tensor("op_11144_cast_fp16")]; + tensor var_11145 = const()[name = tensor("op_11145"), val = tensor([1, 20, 64, -1])]; + tensor var_11146_cast_fp16 = reshape(shape = var_11145, x = v_225_cast_fp16)[name = tensor("op_11146_cast_fp16")]; + tensor attn_weights_449_transpose_x_0 = const()[name = tensor("attn_weights_449_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_449_transpose_y_0 = const()[name = tensor("attn_weights_449_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_449_cast_fp16 = matmul(transpose_x = attn_weights_449_transpose_x_0, transpose_y = attn_weights_449_transpose_y_0, x = var_11142_cast_fp16, y = var_11144_cast_fp16)[name = tensor("attn_weights_449_cast_fp16")]; + tensor attn_weights_451_cast_fp16 = mul(x = attn_weights_449_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_451_cast_fp16")]; + tensor var_11150_cast_fp16 = softmax(axis = var_6879, x = attn_weights_451_cast_fp16)[name = tensor("op_11150_cast_fp16")]; + tensor attn_225_transpose_x_0 = const()[name = tensor("attn_225_transpose_x_0"), val = tensor(false)]; + tensor attn_225_transpose_y_0 = const()[name = tensor("attn_225_transpose_y_0"), val = tensor(true)]; + tensor attn_225_cast_fp16 = matmul(transpose_x = attn_225_transpose_x_0, transpose_y = attn_225_transpose_y_0, x = var_11146_cast_fp16, y = var_11150_cast_fp16)[name = tensor("attn_225_cast_fp16")]; + tensor var_11154 = const()[name = tensor("op_11154"), val = tensor([1, 1280, 1, -1])]; + tensor input_653_cast_fp16 = reshape(shape = var_11154, x = attn_225_cast_fp16)[name = tensor("input_653_cast_fp16")]; + tensor var_11159 = const()[name = tensor("op_11159"), val = tensor([1, 1])]; + tensor var_11161 = const()[name = tensor("op_11161"), val = tensor([1, 1])]; + tensor var_11163_pad_type_0 = const()[name = tensor("op_11163_pad_type_0"), val = tensor("custom")]; + tensor var_11163_pad_0 = const()[name = tensor("op_11163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1621479488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622708352))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622708544)))]; + tensor var_11163_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_11161, groups = var_6895, pad = var_11163_pad_0, pad_type = var_11163_pad_type_0, strides = var_11159, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_653_cast_fp16)[name = tensor("op_11163_cast_fp16")]; + tensor inputs_339_cast_fp16 = add(x = var_11163_cast_fp16, y = inputs_337_cast_fp16)[name = tensor("inputs_339_cast_fp16")]; + tensor hidden_states_449_axes_0 = const()[name = tensor("hidden_states_449_axes_0"), val = tensor([1])]; + tensor hidden_states_449_gamma_0_to_fp16 = const()[name = tensor("hidden_states_449_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622711168)))]; + tensor hidden_states_449_beta_0_to_fp16 = const()[name = tensor("hidden_states_449_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622713792)))]; + tensor var_11173_to_fp16 = const()[name = tensor("op_11173_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_449_cast_fp16 = layer_norm(axes = hidden_states_449_axes_0, beta = hidden_states_449_beta_0_to_fp16, epsilon = var_11173_to_fp16, gamma = hidden_states_449_gamma_0_to_fp16, x = inputs_339_cast_fp16)[name = tensor("hidden_states_449_cast_fp16")]; + tensor var_11188 = const()[name = tensor("op_11188"), val = tensor([1, 1])]; + tensor var_11190 = const()[name = tensor("op_11190"), val = tensor([1, 1])]; + tensor q_227_pad_type_0 = const()[name = tensor("q_227_pad_type_0"), val = tensor("custom")]; + tensor q_227_pad_0 = const()[name = tensor("q_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622716416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1623945280))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_227_cast_fp16 = conv(dilations = var_11190, groups = var_6895, pad = q_227_pad_0, pad_type = q_227_pad_type_0, strides = var_11188, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_449_cast_fp16)[name = tensor("q_227_cast_fp16")]; + tensor var_11194 = const()[name = tensor("op_11194"), val = tensor([1, 1])]; + tensor var_11196 = const()[name = tensor("op_11196"), val = tensor([1, 1])]; + tensor k_227_pad_type_0 = const()[name = tensor("k_227_pad_type_0"), val = tensor("custom")]; + tensor k_227_pad_0 = const()[name = tensor("k_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1623945472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1625911616))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_227_cast_fp16 = conv(dilations = var_11196, groups = var_6895, pad = k_227_pad_0, pad_type = k_227_pad_type_0, strides = var_11194, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_227_cast_fp16")]; + tensor var_11200 = const()[name = tensor("op_11200"), val = tensor([1, 1])]; + tensor var_11202 = const()[name = tensor("op_11202"), val = tensor([1, 1])]; + tensor v_227_pad_type_0 = const()[name = tensor("v_227_pad_type_0"), val = tensor("custom")]; + tensor v_227_pad_0 = const()[name = tensor("v_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1625911808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627877952))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_227_cast_fp16 = conv(dilations = var_11202, groups = var_6895, pad = v_227_pad_0, pad_type = v_227_pad_type_0, strides = var_11200, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_227_cast_fp16")]; + tensor var_11206 = const()[name = tensor("op_11206"), val = tensor([1, 20, 64, -1])]; + tensor var_11207_cast_fp16 = reshape(shape = var_11206, x = q_227_cast_fp16)[name = tensor("op_11207_cast_fp16")]; + tensor var_11208 = const()[name = tensor("op_11208"), val = tensor([1, 20, 64, -1])]; + tensor var_11209_cast_fp16 = reshape(shape = var_11208, x = k_227_cast_fp16)[name = tensor("op_11209_cast_fp16")]; + tensor var_11210 = const()[name = tensor("op_11210"), val = tensor([1, 20, 64, -1])]; + tensor var_11211_cast_fp16 = reshape(shape = var_11210, x = v_227_cast_fp16)[name = tensor("op_11211_cast_fp16")]; + tensor attn_weights_453_transpose_x_0 = const()[name = tensor("attn_weights_453_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_453_transpose_y_0 = const()[name = tensor("attn_weights_453_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_453_cast_fp16 = matmul(transpose_x = attn_weights_453_transpose_x_0, transpose_y = attn_weights_453_transpose_y_0, x = var_11207_cast_fp16, y = var_11209_cast_fp16)[name = tensor("attn_weights_453_cast_fp16")]; + tensor attn_weights_455_cast_fp16 = mul(x = attn_weights_453_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_455_cast_fp16")]; + tensor var_11215_cast_fp16 = softmax(axis = var_6879, x = attn_weights_455_cast_fp16)[name = tensor("op_11215_cast_fp16")]; + tensor attn_227_transpose_x_0 = const()[name = tensor("attn_227_transpose_x_0"), val = tensor(false)]; + tensor attn_227_transpose_y_0 = const()[name = tensor("attn_227_transpose_y_0"), val = tensor(true)]; + tensor attn_227_cast_fp16 = matmul(transpose_x = attn_227_transpose_x_0, transpose_y = attn_227_transpose_y_0, x = var_11211_cast_fp16, y = var_11215_cast_fp16)[name = tensor("attn_227_cast_fp16")]; + tensor var_11219 = const()[name = tensor("op_11219"), val = tensor([1, 1280, 1, -1])]; + tensor input_655_cast_fp16 = reshape(shape = var_11219, x = attn_227_cast_fp16)[name = tensor("input_655_cast_fp16")]; + tensor var_11224 = const()[name = tensor("op_11224"), val = tensor([1, 1])]; + tensor var_11226 = const()[name = tensor("op_11226"), val = tensor([1, 1])]; + tensor var_11228_pad_type_0 = const()[name = tensor("op_11228_pad_type_0"), val = tensor("custom")]; + tensor var_11228_pad_0 = const()[name = tensor("op_11228_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627878144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629107008))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629107200)))]; + tensor var_11228_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_11226, groups = var_6895, pad = var_11228_pad_0, pad_type = var_11228_pad_type_0, strides = var_11224, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_655_cast_fp16)[name = tensor("op_11228_cast_fp16")]; + tensor inputs_341_cast_fp16 = add(x = var_11228_cast_fp16, y = inputs_339_cast_fp16)[name = tensor("inputs_341_cast_fp16")]; + tensor input_657_axes_0 = const()[name = tensor("input_657_axes_0"), val = tensor([1])]; + tensor input_657_gamma_0_to_fp16 = const()[name = tensor("input_657_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629109824)))]; + tensor input_657_beta_0_to_fp16 = const()[name = tensor("input_657_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629112448)))]; + tensor var_11238_to_fp16 = const()[name = tensor("op_11238_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_657_cast_fp16 = layer_norm(axes = input_657_axes_0, beta = input_657_beta_0_to_fp16, epsilon = var_11238_to_fp16, gamma = input_657_gamma_0_to_fp16, x = inputs_341_cast_fp16)[name = tensor("input_657_cast_fp16")]; + tensor var_11254 = const()[name = tensor("op_11254"), val = tensor([1, 1])]; + tensor var_11256 = const()[name = tensor("op_11256"), val = tensor([1, 1])]; + tensor var_11258_pad_type_0 = const()[name = tensor("op_11258_pad_type_0"), val = tensor("custom")]; + tensor var_11258_pad_0 = const()[name = tensor("op_11258_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629115072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638945536))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638945728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638953472))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11258_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11256, groups = var_6895, pad = var_11258_pad_0, pad_type = var_11258_pad_type_0, strides = var_11254, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_657_cast_fp16)[name = tensor("op_11258_cast_fp16")]; + tensor var_11259_split_sizes_0 = const()[name = tensor("op_11259_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11259_axis_0 = const()[name = tensor("op_11259_axis_0"), val = tensor(1)]; + tensor var_11259_cast_fp16_0, tensor var_11259_cast_fp16_1 = split(axis = var_11259_axis_0, split_sizes = var_11259_split_sizes_0, x = var_11258_cast_fp16)[name = tensor("op_11259_cast_fp16")]; + tensor var_11261_mode_0 = const()[name = tensor("op_11261_mode_0"), val = tensor("EXACT")]; + tensor var_11261_cast_fp16 = gelu(mode = var_11261_mode_0, x = var_11259_cast_fp16_1)[name = tensor("op_11261_cast_fp16")]; + tensor input_659_cast_fp16 = mul(x = var_11259_cast_fp16_0, y = var_11261_cast_fp16)[name = tensor("input_659_cast_fp16")]; + tensor var_11265 = const()[name = tensor("op_11265"), val = tensor([1, 1])]; + tensor var_11267 = const()[name = tensor("op_11267"), val = tensor([1, 1])]; + tensor var_11269_pad_type_0 = const()[name = tensor("op_11269_pad_type_0"), val = tensor("custom")]; + tensor var_11269_pad_0 = const()[name = tensor("op_11269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638953664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643868928))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643869120)))]; + tensor var_11269_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_11267, groups = var_6895, pad = var_11269_pad_0, pad_type = var_11269_pad_type_0, strides = var_11265, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_659_cast_fp16)[name = tensor("op_11269_cast_fp16")]; + tensor inputs_343_cast_fp16 = add(x = var_11269_cast_fp16, y = inputs_341_cast_fp16)[name = tensor("inputs_343_cast_fp16")]; + tensor hidden_states_453_axes_0 = const()[name = tensor("hidden_states_453_axes_0"), val = tensor([1])]; + tensor hidden_states_453_gamma_0_to_fp16 = const()[name = tensor("hidden_states_453_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643871744)))]; + tensor hidden_states_453_beta_0_to_fp16 = const()[name = tensor("hidden_states_453_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643874368)))]; + tensor var_11285_to_fp16 = const()[name = tensor("op_11285_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_453_cast_fp16 = layer_norm(axes = hidden_states_453_axes_0, beta = hidden_states_453_beta_0_to_fp16, epsilon = var_11285_to_fp16, gamma = hidden_states_453_gamma_0_to_fp16, x = inputs_343_cast_fp16)[name = tensor("hidden_states_453_cast_fp16")]; + tensor var_11300 = const()[name = tensor("op_11300"), val = tensor([1, 1])]; + tensor var_11302 = const()[name = tensor("op_11302"), val = tensor([1, 1])]; + tensor q_229_pad_type_0 = const()[name = tensor("q_229_pad_type_0"), val = tensor("custom")]; + tensor q_229_pad_0 = const()[name = tensor("q_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643876992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645105856))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_229_cast_fp16 = conv(dilations = var_11302, groups = var_6895, pad = q_229_pad_0, pad_type = q_229_pad_type_0, strides = var_11300, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_453_cast_fp16)[name = tensor("q_229_cast_fp16")]; + tensor var_11306 = const()[name = tensor("op_11306"), val = tensor([1, 1])]; + tensor var_11308 = const()[name = tensor("op_11308"), val = tensor([1, 1])]; + tensor k_229_pad_type_0 = const()[name = tensor("k_229_pad_type_0"), val = tensor("custom")]; + tensor k_229_pad_0 = const()[name = tensor("k_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645106048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1646334912))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_229_cast_fp16 = conv(dilations = var_11308, groups = var_6895, pad = k_229_pad_0, pad_type = k_229_pad_type_0, strides = var_11306, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_453_cast_fp16)[name = tensor("k_229_cast_fp16")]; + tensor var_11312 = const()[name = tensor("op_11312"), val = tensor([1, 1])]; + tensor var_11314 = const()[name = tensor("op_11314"), val = tensor([1, 1])]; + tensor v_229_pad_type_0 = const()[name = tensor("v_229_pad_type_0"), val = tensor("custom")]; + tensor v_229_pad_0 = const()[name = tensor("v_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1646335104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647563968))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_229_cast_fp16 = conv(dilations = var_11314, groups = var_6895, pad = v_229_pad_0, pad_type = v_229_pad_type_0, strides = var_11312, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_453_cast_fp16)[name = tensor("v_229_cast_fp16")]; + tensor var_11318 = const()[name = tensor("op_11318"), val = tensor([1, 20, 64, -1])]; + tensor var_11319_cast_fp16 = reshape(shape = var_11318, x = q_229_cast_fp16)[name = tensor("op_11319_cast_fp16")]; + tensor var_11320 = const()[name = tensor("op_11320"), val = tensor([1, 20, 64, -1])]; + tensor var_11321_cast_fp16 = reshape(shape = var_11320, x = k_229_cast_fp16)[name = tensor("op_11321_cast_fp16")]; + tensor var_11322 = const()[name = tensor("op_11322"), val = tensor([1, 20, 64, -1])]; + tensor var_11323_cast_fp16 = reshape(shape = var_11322, x = v_229_cast_fp16)[name = tensor("op_11323_cast_fp16")]; + tensor attn_weights_457_transpose_x_0 = const()[name = tensor("attn_weights_457_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_457_transpose_y_0 = const()[name = tensor("attn_weights_457_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_457_cast_fp16 = matmul(transpose_x = attn_weights_457_transpose_x_0, transpose_y = attn_weights_457_transpose_y_0, x = var_11319_cast_fp16, y = var_11321_cast_fp16)[name = tensor("attn_weights_457_cast_fp16")]; + tensor attn_weights_459_cast_fp16 = mul(x = attn_weights_457_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_459_cast_fp16")]; + tensor var_11327_cast_fp16 = softmax(axis = var_6879, x = attn_weights_459_cast_fp16)[name = tensor("op_11327_cast_fp16")]; + tensor attn_229_transpose_x_0 = const()[name = tensor("attn_229_transpose_x_0"), val = tensor(false)]; + tensor attn_229_transpose_y_0 = const()[name = tensor("attn_229_transpose_y_0"), val = tensor(true)]; + tensor attn_229_cast_fp16 = matmul(transpose_x = attn_229_transpose_x_0, transpose_y = attn_229_transpose_y_0, x = var_11323_cast_fp16, y = var_11327_cast_fp16)[name = tensor("attn_229_cast_fp16")]; + tensor var_11331 = const()[name = tensor("op_11331"), val = tensor([1, 1280, 1, -1])]; + tensor input_661_cast_fp16 = reshape(shape = var_11331, x = attn_229_cast_fp16)[name = tensor("input_661_cast_fp16")]; + tensor var_11336 = const()[name = tensor("op_11336"), val = tensor([1, 1])]; + tensor var_11338 = const()[name = tensor("op_11338"), val = tensor([1, 1])]; + tensor var_11340_pad_type_0 = const()[name = tensor("op_11340_pad_type_0"), val = tensor("custom")]; + tensor var_11340_pad_0 = const()[name = tensor("op_11340_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647564160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648793024))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648793216)))]; + tensor var_11340_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_11338, groups = var_6895, pad = var_11340_pad_0, pad_type = var_11340_pad_type_0, strides = var_11336, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_661_cast_fp16)[name = tensor("op_11340_cast_fp16")]; + tensor inputs_345_cast_fp16 = add(x = var_11340_cast_fp16, y = inputs_343_cast_fp16)[name = tensor("inputs_345_cast_fp16")]; + tensor hidden_states_455_axes_0 = const()[name = tensor("hidden_states_455_axes_0"), val = tensor([1])]; + tensor hidden_states_455_gamma_0_to_fp16 = const()[name = tensor("hidden_states_455_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648795840)))]; + tensor hidden_states_455_beta_0_to_fp16 = const()[name = tensor("hidden_states_455_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648798464)))]; + tensor var_11350_to_fp16 = const()[name = tensor("op_11350_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_455_cast_fp16 = layer_norm(axes = hidden_states_455_axes_0, beta = hidden_states_455_beta_0_to_fp16, epsilon = var_11350_to_fp16, gamma = hidden_states_455_gamma_0_to_fp16, x = inputs_345_cast_fp16)[name = tensor("hidden_states_455_cast_fp16")]; + tensor var_11365 = const()[name = tensor("op_11365"), val = tensor([1, 1])]; + tensor var_11367 = const()[name = tensor("op_11367"), val = tensor([1, 1])]; + tensor q_231_pad_type_0 = const()[name = tensor("q_231_pad_type_0"), val = tensor("custom")]; + tensor q_231_pad_0 = const()[name = tensor("q_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648801088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1650029952))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_231_cast_fp16 = conv(dilations = var_11367, groups = var_6895, pad = q_231_pad_0, pad_type = q_231_pad_type_0, strides = var_11365, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_455_cast_fp16)[name = tensor("q_231_cast_fp16")]; + tensor var_11371 = const()[name = tensor("op_11371"), val = tensor([1, 1])]; + tensor var_11373 = const()[name = tensor("op_11373"), val = tensor([1, 1])]; + tensor k_231_pad_type_0 = const()[name = tensor("k_231_pad_type_0"), val = tensor("custom")]; + tensor k_231_pad_0 = const()[name = tensor("k_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1650030144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1651996288))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_231_cast_fp16 = conv(dilations = var_11373, groups = var_6895, pad = k_231_pad_0, pad_type = k_231_pad_type_0, strides = var_11371, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_231_cast_fp16")]; + tensor var_11377 = const()[name = tensor("op_11377"), val = tensor([1, 1])]; + tensor var_11379 = const()[name = tensor("op_11379"), val = tensor([1, 1])]; + tensor v_231_pad_type_0 = const()[name = tensor("v_231_pad_type_0"), val = tensor("custom")]; + tensor v_231_pad_0 = const()[name = tensor("v_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1651996480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1653962624))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_231_cast_fp16 = conv(dilations = var_11379, groups = var_6895, pad = v_231_pad_0, pad_type = v_231_pad_type_0, strides = var_11377, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_231_cast_fp16")]; + tensor var_11383 = const()[name = tensor("op_11383"), val = tensor([1, 20, 64, -1])]; + tensor var_11384_cast_fp16 = reshape(shape = var_11383, x = q_231_cast_fp16)[name = tensor("op_11384_cast_fp16")]; + tensor var_11385 = const()[name = tensor("op_11385"), val = tensor([1, 20, 64, -1])]; + tensor var_11386_cast_fp16 = reshape(shape = var_11385, x = k_231_cast_fp16)[name = tensor("op_11386_cast_fp16")]; + tensor var_11387 = const()[name = tensor("op_11387"), val = tensor([1, 20, 64, -1])]; + tensor var_11388_cast_fp16 = reshape(shape = var_11387, x = v_231_cast_fp16)[name = tensor("op_11388_cast_fp16")]; + tensor attn_weights_461_transpose_x_0 = const()[name = tensor("attn_weights_461_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_461_transpose_y_0 = const()[name = tensor("attn_weights_461_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_461_cast_fp16 = matmul(transpose_x = attn_weights_461_transpose_x_0, transpose_y = attn_weights_461_transpose_y_0, x = var_11384_cast_fp16, y = var_11386_cast_fp16)[name = tensor("attn_weights_461_cast_fp16")]; + tensor attn_weights_463_cast_fp16 = mul(x = attn_weights_461_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_463_cast_fp16")]; + tensor var_11392_cast_fp16 = softmax(axis = var_6879, x = attn_weights_463_cast_fp16)[name = tensor("op_11392_cast_fp16")]; + tensor attn_231_transpose_x_0 = const()[name = tensor("attn_231_transpose_x_0"), val = tensor(false)]; + tensor attn_231_transpose_y_0 = const()[name = tensor("attn_231_transpose_y_0"), val = tensor(true)]; + tensor attn_231_cast_fp16 = matmul(transpose_x = attn_231_transpose_x_0, transpose_y = attn_231_transpose_y_0, x = var_11388_cast_fp16, y = var_11392_cast_fp16)[name = tensor("attn_231_cast_fp16")]; + tensor var_11396 = const()[name = tensor("op_11396"), val = tensor([1, 1280, 1, -1])]; + tensor input_663_cast_fp16 = reshape(shape = var_11396, x = attn_231_cast_fp16)[name = tensor("input_663_cast_fp16")]; + tensor var_11401 = const()[name = tensor("op_11401"), val = tensor([1, 1])]; + tensor var_11403 = const()[name = tensor("op_11403"), val = tensor([1, 1])]; + tensor var_11405_pad_type_0 = const()[name = tensor("op_11405_pad_type_0"), val = tensor("custom")]; + tensor var_11405_pad_0 = const()[name = tensor("op_11405_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1653962816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655191680))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655191872)))]; + tensor var_11405_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_11403, groups = var_6895, pad = var_11405_pad_0, pad_type = var_11405_pad_type_0, strides = var_11401, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = tensor("op_11405_cast_fp16")]; + tensor inputs_347_cast_fp16 = add(x = var_11405_cast_fp16, y = inputs_345_cast_fp16)[name = tensor("inputs_347_cast_fp16")]; + tensor input_665_axes_0 = const()[name = tensor("input_665_axes_0"), val = tensor([1])]; + tensor input_665_gamma_0_to_fp16 = const()[name = tensor("input_665_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655194496)))]; + tensor input_665_beta_0_to_fp16 = const()[name = tensor("input_665_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655197120)))]; + tensor var_11415_to_fp16 = const()[name = tensor("op_11415_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_665_cast_fp16 = layer_norm(axes = input_665_axes_0, beta = input_665_beta_0_to_fp16, epsilon = var_11415_to_fp16, gamma = input_665_gamma_0_to_fp16, x = inputs_347_cast_fp16)[name = tensor("input_665_cast_fp16")]; + tensor var_11431 = const()[name = tensor("op_11431"), val = tensor([1, 1])]; + tensor var_11433 = const()[name = tensor("op_11433"), val = tensor([1, 1])]; + tensor var_11435_pad_type_0 = const()[name = tensor("op_11435_pad_type_0"), val = tensor("custom")]; + tensor var_11435_pad_0 = const()[name = tensor("op_11435_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655199744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665030208))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665030400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665038144))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11435_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11433, groups = var_6895, pad = var_11435_pad_0, pad_type = var_11435_pad_type_0, strides = var_11431, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_665_cast_fp16)[name = tensor("op_11435_cast_fp16")]; + tensor var_11436_split_sizes_0 = const()[name = tensor("op_11436_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11436_axis_0 = const()[name = tensor("op_11436_axis_0"), val = tensor(1)]; + tensor var_11436_cast_fp16_0, tensor var_11436_cast_fp16_1 = split(axis = var_11436_axis_0, split_sizes = var_11436_split_sizes_0, x = var_11435_cast_fp16)[name = tensor("op_11436_cast_fp16")]; + tensor var_11438_mode_0 = const()[name = tensor("op_11438_mode_0"), val = tensor("EXACT")]; + tensor var_11438_cast_fp16 = gelu(mode = var_11438_mode_0, x = var_11436_cast_fp16_1)[name = tensor("op_11438_cast_fp16")]; + tensor input_667_cast_fp16 = mul(x = var_11436_cast_fp16_0, y = var_11438_cast_fp16)[name = tensor("input_667_cast_fp16")]; + tensor var_11442 = const()[name = tensor("op_11442"), val = tensor([1, 1])]; + tensor var_11444 = const()[name = tensor("op_11444"), val = tensor([1, 1])]; + tensor var_11446_pad_type_0 = const()[name = tensor("op_11446_pad_type_0"), val = tensor("custom")]; + tensor var_11446_pad_0 = const()[name = tensor("op_11446_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665038336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669953600))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669953792)))]; + tensor var_11446_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_11444, groups = var_6895, pad = var_11446_pad_0, pad_type = var_11446_pad_type_0, strides = var_11442, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = tensor("op_11446_cast_fp16")]; + tensor inputs_349_cast_fp16 = add(x = var_11446_cast_fp16, y = inputs_347_cast_fp16)[name = tensor("inputs_349_cast_fp16")]; + tensor hidden_states_459_axes_0 = const()[name = tensor("hidden_states_459_axes_0"), val = tensor([1])]; + tensor hidden_states_459_gamma_0_to_fp16 = const()[name = tensor("hidden_states_459_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669956416)))]; + tensor hidden_states_459_beta_0_to_fp16 = const()[name = tensor("hidden_states_459_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669959040)))]; + tensor var_11462_to_fp16 = const()[name = tensor("op_11462_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_459_cast_fp16 = layer_norm(axes = hidden_states_459_axes_0, beta = hidden_states_459_beta_0_to_fp16, epsilon = var_11462_to_fp16, gamma = hidden_states_459_gamma_0_to_fp16, x = inputs_349_cast_fp16)[name = tensor("hidden_states_459_cast_fp16")]; + tensor var_11477 = const()[name = tensor("op_11477"), val = tensor([1, 1])]; + tensor var_11479 = const()[name = tensor("op_11479"), val = tensor([1, 1])]; + tensor q_233_pad_type_0 = const()[name = tensor("q_233_pad_type_0"), val = tensor("custom")]; + tensor q_233_pad_0 = const()[name = tensor("q_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669961664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1671190528))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_233_cast_fp16 = conv(dilations = var_11479, groups = var_6895, pad = q_233_pad_0, pad_type = q_233_pad_type_0, strides = var_11477, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_459_cast_fp16)[name = tensor("q_233_cast_fp16")]; + tensor var_11483 = const()[name = tensor("op_11483"), val = tensor([1, 1])]; + tensor var_11485 = const()[name = tensor("op_11485"), val = tensor([1, 1])]; + tensor k_233_pad_type_0 = const()[name = tensor("k_233_pad_type_0"), val = tensor("custom")]; + tensor k_233_pad_0 = const()[name = tensor("k_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1671190720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1672419584))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_233_cast_fp16 = conv(dilations = var_11485, groups = var_6895, pad = k_233_pad_0, pad_type = k_233_pad_type_0, strides = var_11483, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_459_cast_fp16)[name = tensor("k_233_cast_fp16")]; + tensor var_11489 = const()[name = tensor("op_11489"), val = tensor([1, 1])]; + tensor var_11491 = const()[name = tensor("op_11491"), val = tensor([1, 1])]; + tensor v_233_pad_type_0 = const()[name = tensor("v_233_pad_type_0"), val = tensor("custom")]; + tensor v_233_pad_0 = const()[name = tensor("v_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1672419776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1673648640))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_233_cast_fp16 = conv(dilations = var_11491, groups = var_6895, pad = v_233_pad_0, pad_type = v_233_pad_type_0, strides = var_11489, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_459_cast_fp16)[name = tensor("v_233_cast_fp16")]; + tensor var_11495 = const()[name = tensor("op_11495"), val = tensor([1, 20, 64, -1])]; + tensor var_11496_cast_fp16 = reshape(shape = var_11495, x = q_233_cast_fp16)[name = tensor("op_11496_cast_fp16")]; + tensor var_11497 = const()[name = tensor("op_11497"), val = tensor([1, 20, 64, -1])]; + tensor var_11498_cast_fp16 = reshape(shape = var_11497, x = k_233_cast_fp16)[name = tensor("op_11498_cast_fp16")]; + tensor var_11499 = const()[name = tensor("op_11499"), val = tensor([1, 20, 64, -1])]; + tensor var_11500_cast_fp16 = reshape(shape = var_11499, x = v_233_cast_fp16)[name = tensor("op_11500_cast_fp16")]; + tensor attn_weights_465_transpose_x_0 = const()[name = tensor("attn_weights_465_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_465_transpose_y_0 = const()[name = tensor("attn_weights_465_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_465_cast_fp16 = matmul(transpose_x = attn_weights_465_transpose_x_0, transpose_y = attn_weights_465_transpose_y_0, x = var_11496_cast_fp16, y = var_11498_cast_fp16)[name = tensor("attn_weights_465_cast_fp16")]; + tensor attn_weights_467_cast_fp16 = mul(x = attn_weights_465_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_467_cast_fp16")]; + tensor var_11504_cast_fp16 = softmax(axis = var_6879, x = attn_weights_467_cast_fp16)[name = tensor("op_11504_cast_fp16")]; + tensor attn_233_transpose_x_0 = const()[name = tensor("attn_233_transpose_x_0"), val = tensor(false)]; + tensor attn_233_transpose_y_0 = const()[name = tensor("attn_233_transpose_y_0"), val = tensor(true)]; + tensor attn_233_cast_fp16 = matmul(transpose_x = attn_233_transpose_x_0, transpose_y = attn_233_transpose_y_0, x = var_11500_cast_fp16, y = var_11504_cast_fp16)[name = tensor("attn_233_cast_fp16")]; + tensor var_11508 = const()[name = tensor("op_11508"), val = tensor([1, 1280, 1, -1])]; + tensor input_669_cast_fp16 = reshape(shape = var_11508, x = attn_233_cast_fp16)[name = tensor("input_669_cast_fp16")]; + tensor var_11513 = const()[name = tensor("op_11513"), val = tensor([1, 1])]; + tensor var_11515 = const()[name = tensor("op_11515"), val = tensor([1, 1])]; + tensor var_11517_pad_type_0 = const()[name = tensor("op_11517_pad_type_0"), val = tensor("custom")]; + tensor var_11517_pad_0 = const()[name = tensor("op_11517_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1673648832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674877696))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674877888)))]; + tensor var_11517_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_11515, groups = var_6895, pad = var_11517_pad_0, pad_type = var_11517_pad_type_0, strides = var_11513, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_669_cast_fp16)[name = tensor("op_11517_cast_fp16")]; + tensor inputs_351_cast_fp16 = add(x = var_11517_cast_fp16, y = inputs_349_cast_fp16)[name = tensor("inputs_351_cast_fp16")]; + tensor hidden_states_461_axes_0 = const()[name = tensor("hidden_states_461_axes_0"), val = tensor([1])]; + tensor hidden_states_461_gamma_0_to_fp16 = const()[name = tensor("hidden_states_461_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674880512)))]; + tensor hidden_states_461_beta_0_to_fp16 = const()[name = tensor("hidden_states_461_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674883136)))]; + tensor var_11527_to_fp16 = const()[name = tensor("op_11527_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_461_cast_fp16 = layer_norm(axes = hidden_states_461_axes_0, beta = hidden_states_461_beta_0_to_fp16, epsilon = var_11527_to_fp16, gamma = hidden_states_461_gamma_0_to_fp16, x = inputs_351_cast_fp16)[name = tensor("hidden_states_461_cast_fp16")]; + tensor var_11542 = const()[name = tensor("op_11542"), val = tensor([1, 1])]; + tensor var_11544 = const()[name = tensor("op_11544"), val = tensor([1, 1])]; + tensor q_235_pad_type_0 = const()[name = tensor("q_235_pad_type_0"), val = tensor("custom")]; + tensor q_235_pad_0 = const()[name = tensor("q_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674885760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1676114624))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_235_cast_fp16 = conv(dilations = var_11544, groups = var_6895, pad = q_235_pad_0, pad_type = q_235_pad_type_0, strides = var_11542, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_461_cast_fp16)[name = tensor("q_235_cast_fp16")]; + tensor var_11548 = const()[name = tensor("op_11548"), val = tensor([1, 1])]; + tensor var_11550 = const()[name = tensor("op_11550"), val = tensor([1, 1])]; + tensor k_235_pad_type_0 = const()[name = tensor("k_235_pad_type_0"), val = tensor("custom")]; + tensor k_235_pad_0 = const()[name = tensor("k_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1676114816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1678080960))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_235_cast_fp16 = conv(dilations = var_11550, groups = var_6895, pad = k_235_pad_0, pad_type = k_235_pad_type_0, strides = var_11548, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_235_cast_fp16")]; + tensor var_11554 = const()[name = tensor("op_11554"), val = tensor([1, 1])]; + tensor var_11556 = const()[name = tensor("op_11556"), val = tensor([1, 1])]; + tensor v_235_pad_type_0 = const()[name = tensor("v_235_pad_type_0"), val = tensor("custom")]; + tensor v_235_pad_0 = const()[name = tensor("v_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1678081152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1680047296))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_235_cast_fp16 = conv(dilations = var_11556, groups = var_6895, pad = v_235_pad_0, pad_type = v_235_pad_type_0, strides = var_11554, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_235_cast_fp16")]; + tensor var_11560 = const()[name = tensor("op_11560"), val = tensor([1, 20, 64, -1])]; + tensor var_11561_cast_fp16 = reshape(shape = var_11560, x = q_235_cast_fp16)[name = tensor("op_11561_cast_fp16")]; + tensor var_11562 = const()[name = tensor("op_11562"), val = tensor([1, 20, 64, -1])]; + tensor var_11563_cast_fp16 = reshape(shape = var_11562, x = k_235_cast_fp16)[name = tensor("op_11563_cast_fp16")]; + tensor var_11564 = const()[name = tensor("op_11564"), val = tensor([1, 20, 64, -1])]; + tensor var_11565_cast_fp16 = reshape(shape = var_11564, x = v_235_cast_fp16)[name = tensor("op_11565_cast_fp16")]; + tensor attn_weights_469_transpose_x_0 = const()[name = tensor("attn_weights_469_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_469_transpose_y_0 = const()[name = tensor("attn_weights_469_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_469_cast_fp16 = matmul(transpose_x = attn_weights_469_transpose_x_0, transpose_y = attn_weights_469_transpose_y_0, x = var_11561_cast_fp16, y = var_11563_cast_fp16)[name = tensor("attn_weights_469_cast_fp16")]; + tensor attn_weights_471_cast_fp16 = mul(x = attn_weights_469_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_471_cast_fp16")]; + tensor var_11569_cast_fp16 = softmax(axis = var_6879, x = attn_weights_471_cast_fp16)[name = tensor("op_11569_cast_fp16")]; + tensor attn_235_transpose_x_0 = const()[name = tensor("attn_235_transpose_x_0"), val = tensor(false)]; + tensor attn_235_transpose_y_0 = const()[name = tensor("attn_235_transpose_y_0"), val = tensor(true)]; + tensor attn_235_cast_fp16 = matmul(transpose_x = attn_235_transpose_x_0, transpose_y = attn_235_transpose_y_0, x = var_11565_cast_fp16, y = var_11569_cast_fp16)[name = tensor("attn_235_cast_fp16")]; + tensor var_11573 = const()[name = tensor("op_11573"), val = tensor([1, 1280, 1, -1])]; + tensor input_671_cast_fp16 = reshape(shape = var_11573, x = attn_235_cast_fp16)[name = tensor("input_671_cast_fp16")]; + tensor var_11578 = const()[name = tensor("op_11578"), val = tensor([1, 1])]; + tensor var_11580 = const()[name = tensor("op_11580"), val = tensor([1, 1])]; + tensor var_11582_pad_type_0 = const()[name = tensor("op_11582_pad_type_0"), val = tensor("custom")]; + tensor var_11582_pad_0 = const()[name = tensor("op_11582_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1680047488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681276352))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681276544)))]; + tensor var_11582_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_11580, groups = var_6895, pad = var_11582_pad_0, pad_type = var_11582_pad_type_0, strides = var_11578, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_671_cast_fp16)[name = tensor("op_11582_cast_fp16")]; + tensor inputs_353_cast_fp16 = add(x = var_11582_cast_fp16, y = inputs_351_cast_fp16)[name = tensor("inputs_353_cast_fp16")]; + tensor input_673_axes_0 = const()[name = tensor("input_673_axes_0"), val = tensor([1])]; + tensor input_673_gamma_0_to_fp16 = const()[name = tensor("input_673_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681279168)))]; + tensor input_673_beta_0_to_fp16 = const()[name = tensor("input_673_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681281792)))]; + tensor var_11592_to_fp16 = const()[name = tensor("op_11592_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_673_cast_fp16 = layer_norm(axes = input_673_axes_0, beta = input_673_beta_0_to_fp16, epsilon = var_11592_to_fp16, gamma = input_673_gamma_0_to_fp16, x = inputs_353_cast_fp16)[name = tensor("input_673_cast_fp16")]; + tensor var_11608 = const()[name = tensor("op_11608"), val = tensor([1, 1])]; + tensor var_11610 = const()[name = tensor("op_11610"), val = tensor([1, 1])]; + tensor var_11612_pad_type_0 = const()[name = tensor("op_11612_pad_type_0"), val = tensor("custom")]; + tensor var_11612_pad_0 = const()[name = tensor("op_11612_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681284416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691114880))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691115072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691122816))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11612_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11610, groups = var_6895, pad = var_11612_pad_0, pad_type = var_11612_pad_type_0, strides = var_11608, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_673_cast_fp16)[name = tensor("op_11612_cast_fp16")]; + tensor var_11613_split_sizes_0 = const()[name = tensor("op_11613_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11613_axis_0 = const()[name = tensor("op_11613_axis_0"), val = tensor(1)]; + tensor var_11613_cast_fp16_0, tensor var_11613_cast_fp16_1 = split(axis = var_11613_axis_0, split_sizes = var_11613_split_sizes_0, x = var_11612_cast_fp16)[name = tensor("op_11613_cast_fp16")]; + tensor var_11615_mode_0 = const()[name = tensor("op_11615_mode_0"), val = tensor("EXACT")]; + tensor var_11615_cast_fp16 = gelu(mode = var_11615_mode_0, x = var_11613_cast_fp16_1)[name = tensor("op_11615_cast_fp16")]; + tensor input_675_cast_fp16 = mul(x = var_11613_cast_fp16_0, y = var_11615_cast_fp16)[name = tensor("input_675_cast_fp16")]; + tensor var_11619 = const()[name = tensor("op_11619"), val = tensor([1, 1])]; + tensor var_11621 = const()[name = tensor("op_11621"), val = tensor([1, 1])]; + tensor var_11623_pad_type_0 = const()[name = tensor("op_11623_pad_type_0"), val = tensor("custom")]; + tensor var_11623_pad_0 = const()[name = tensor("op_11623_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691123008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696038272))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696038464)))]; + tensor var_11623_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_11621, groups = var_6895, pad = var_11623_pad_0, pad_type = var_11623_pad_type_0, strides = var_11619, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_675_cast_fp16)[name = tensor("op_11623_cast_fp16")]; + tensor inputs_355_cast_fp16 = add(x = var_11623_cast_fp16, y = inputs_353_cast_fp16)[name = tensor("inputs_355_cast_fp16")]; + tensor hidden_states_465_axes_0 = const()[name = tensor("hidden_states_465_axes_0"), val = tensor([1])]; + tensor hidden_states_465_gamma_0_to_fp16 = const()[name = tensor("hidden_states_465_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696041088)))]; + tensor hidden_states_465_beta_0_to_fp16 = const()[name = tensor("hidden_states_465_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696043712)))]; + tensor var_11639_to_fp16 = const()[name = tensor("op_11639_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_465_cast_fp16 = layer_norm(axes = hidden_states_465_axes_0, beta = hidden_states_465_beta_0_to_fp16, epsilon = var_11639_to_fp16, gamma = hidden_states_465_gamma_0_to_fp16, x = inputs_355_cast_fp16)[name = tensor("hidden_states_465_cast_fp16")]; + tensor var_11654 = const()[name = tensor("op_11654"), val = tensor([1, 1])]; + tensor var_11656 = const()[name = tensor("op_11656"), val = tensor([1, 1])]; + tensor q_237_pad_type_0 = const()[name = tensor("q_237_pad_type_0"), val = tensor("custom")]; + tensor q_237_pad_0 = const()[name = tensor("q_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696046336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1697275200))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_237_cast_fp16 = conv(dilations = var_11656, groups = var_6895, pad = q_237_pad_0, pad_type = q_237_pad_type_0, strides = var_11654, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_465_cast_fp16)[name = tensor("q_237_cast_fp16")]; + tensor var_11660 = const()[name = tensor("op_11660"), val = tensor([1, 1])]; + tensor var_11662 = const()[name = tensor("op_11662"), val = tensor([1, 1])]; + tensor k_237_pad_type_0 = const()[name = tensor("k_237_pad_type_0"), val = tensor("custom")]; + tensor k_237_pad_0 = const()[name = tensor("k_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1697275392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698504256))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_237_cast_fp16 = conv(dilations = var_11662, groups = var_6895, pad = k_237_pad_0, pad_type = k_237_pad_type_0, strides = var_11660, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_465_cast_fp16)[name = tensor("k_237_cast_fp16")]; + tensor var_11666 = const()[name = tensor("op_11666"), val = tensor([1, 1])]; + tensor var_11668 = const()[name = tensor("op_11668"), val = tensor([1, 1])]; + tensor v_237_pad_type_0 = const()[name = tensor("v_237_pad_type_0"), val = tensor("custom")]; + tensor v_237_pad_0 = const()[name = tensor("v_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698504448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699733312))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_237_cast_fp16 = conv(dilations = var_11668, groups = var_6895, pad = v_237_pad_0, pad_type = v_237_pad_type_0, strides = var_11666, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_465_cast_fp16)[name = tensor("v_237_cast_fp16")]; + tensor var_11672 = const()[name = tensor("op_11672"), val = tensor([1, 20, 64, -1])]; + tensor var_11673_cast_fp16 = reshape(shape = var_11672, x = q_237_cast_fp16)[name = tensor("op_11673_cast_fp16")]; + tensor var_11674 = const()[name = tensor("op_11674"), val = tensor([1, 20, 64, -1])]; + tensor var_11675_cast_fp16 = reshape(shape = var_11674, x = k_237_cast_fp16)[name = tensor("op_11675_cast_fp16")]; + tensor var_11676 = const()[name = tensor("op_11676"), val = tensor([1, 20, 64, -1])]; + tensor var_11677_cast_fp16 = reshape(shape = var_11676, x = v_237_cast_fp16)[name = tensor("op_11677_cast_fp16")]; + tensor attn_weights_473_transpose_x_0 = const()[name = tensor("attn_weights_473_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_473_transpose_y_0 = const()[name = tensor("attn_weights_473_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_473_cast_fp16 = matmul(transpose_x = attn_weights_473_transpose_x_0, transpose_y = attn_weights_473_transpose_y_0, x = var_11673_cast_fp16, y = var_11675_cast_fp16)[name = tensor("attn_weights_473_cast_fp16")]; + tensor attn_weights_475_cast_fp16 = mul(x = attn_weights_473_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_475_cast_fp16")]; + tensor var_11681_cast_fp16 = softmax(axis = var_6879, x = attn_weights_475_cast_fp16)[name = tensor("op_11681_cast_fp16")]; + tensor attn_237_transpose_x_0 = const()[name = tensor("attn_237_transpose_x_0"), val = tensor(false)]; + tensor attn_237_transpose_y_0 = const()[name = tensor("attn_237_transpose_y_0"), val = tensor(true)]; + tensor attn_237_cast_fp16 = matmul(transpose_x = attn_237_transpose_x_0, transpose_y = attn_237_transpose_y_0, x = var_11677_cast_fp16, y = var_11681_cast_fp16)[name = tensor("attn_237_cast_fp16")]; + tensor var_11685 = const()[name = tensor("op_11685"), val = tensor([1, 1280, 1, -1])]; + tensor input_677_cast_fp16 = reshape(shape = var_11685, x = attn_237_cast_fp16)[name = tensor("input_677_cast_fp16")]; + tensor var_11690 = const()[name = tensor("op_11690"), val = tensor([1, 1])]; + tensor var_11692 = const()[name = tensor("op_11692"), val = tensor([1, 1])]; + tensor var_11694_pad_type_0 = const()[name = tensor("op_11694_pad_type_0"), val = tensor("custom")]; + tensor var_11694_pad_0 = const()[name = tensor("op_11694_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699733504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700962368))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700962560)))]; + tensor var_11694_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_11692, groups = var_6895, pad = var_11694_pad_0, pad_type = var_11694_pad_type_0, strides = var_11690, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_677_cast_fp16)[name = tensor("op_11694_cast_fp16")]; + tensor inputs_357_cast_fp16 = add(x = var_11694_cast_fp16, y = inputs_355_cast_fp16)[name = tensor("inputs_357_cast_fp16")]; + tensor hidden_states_467_axes_0 = const()[name = tensor("hidden_states_467_axes_0"), val = tensor([1])]; + tensor hidden_states_467_gamma_0_to_fp16 = const()[name = tensor("hidden_states_467_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700965184)))]; + tensor hidden_states_467_beta_0_to_fp16 = const()[name = tensor("hidden_states_467_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700967808)))]; + tensor var_11704_to_fp16 = const()[name = tensor("op_11704_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_467_cast_fp16 = layer_norm(axes = hidden_states_467_axes_0, beta = hidden_states_467_beta_0_to_fp16, epsilon = var_11704_to_fp16, gamma = hidden_states_467_gamma_0_to_fp16, x = inputs_357_cast_fp16)[name = tensor("hidden_states_467_cast_fp16")]; + tensor var_11719 = const()[name = tensor("op_11719"), val = tensor([1, 1])]; + tensor var_11721 = const()[name = tensor("op_11721"), val = tensor([1, 1])]; + tensor q_239_pad_type_0 = const()[name = tensor("q_239_pad_type_0"), val = tensor("custom")]; + tensor q_239_pad_0 = const()[name = tensor("q_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700970432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702199296))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_239_cast_fp16 = conv(dilations = var_11721, groups = var_6895, pad = q_239_pad_0, pad_type = q_239_pad_type_0, strides = var_11719, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_467_cast_fp16)[name = tensor("q_239_cast_fp16")]; + tensor var_11725 = const()[name = tensor("op_11725"), val = tensor([1, 1])]; + tensor var_11727 = const()[name = tensor("op_11727"), val = tensor([1, 1])]; + tensor k_239_pad_type_0 = const()[name = tensor("k_239_pad_type_0"), val = tensor("custom")]; + tensor k_239_pad_0 = const()[name = tensor("k_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702199488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704165632))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_239_cast_fp16 = conv(dilations = var_11727, groups = var_6895, pad = k_239_pad_0, pad_type = k_239_pad_type_0, strides = var_11725, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_239_cast_fp16")]; + tensor var_11731 = const()[name = tensor("op_11731"), val = tensor([1, 1])]; + tensor var_11733 = const()[name = tensor("op_11733"), val = tensor([1, 1])]; + tensor v_239_pad_type_0 = const()[name = tensor("v_239_pad_type_0"), val = tensor("custom")]; + tensor v_239_pad_0 = const()[name = tensor("v_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704165824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1706131968))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_239_cast_fp16 = conv(dilations = var_11733, groups = var_6895, pad = v_239_pad_0, pad_type = v_239_pad_type_0, strides = var_11731, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_239_cast_fp16")]; + tensor var_11737 = const()[name = tensor("op_11737"), val = tensor([1, 20, 64, -1])]; + tensor var_11738_cast_fp16 = reshape(shape = var_11737, x = q_239_cast_fp16)[name = tensor("op_11738_cast_fp16")]; + tensor var_11739 = const()[name = tensor("op_11739"), val = tensor([1, 20, 64, -1])]; + tensor var_11740_cast_fp16 = reshape(shape = var_11739, x = k_239_cast_fp16)[name = tensor("op_11740_cast_fp16")]; + tensor var_11741 = const()[name = tensor("op_11741"), val = tensor([1, 20, 64, -1])]; + tensor var_11742_cast_fp16 = reshape(shape = var_11741, x = v_239_cast_fp16)[name = tensor("op_11742_cast_fp16")]; + tensor attn_weights_477_transpose_x_0 = const()[name = tensor("attn_weights_477_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_477_transpose_y_0 = const()[name = tensor("attn_weights_477_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_477_cast_fp16 = matmul(transpose_x = attn_weights_477_transpose_x_0, transpose_y = attn_weights_477_transpose_y_0, x = var_11738_cast_fp16, y = var_11740_cast_fp16)[name = tensor("attn_weights_477_cast_fp16")]; + tensor attn_weights_479_cast_fp16 = mul(x = attn_weights_477_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_479_cast_fp16")]; + tensor var_11746_cast_fp16 = softmax(axis = var_6879, x = attn_weights_479_cast_fp16)[name = tensor("op_11746_cast_fp16")]; + tensor attn_239_transpose_x_0 = const()[name = tensor("attn_239_transpose_x_0"), val = tensor(false)]; + tensor attn_239_transpose_y_0 = const()[name = tensor("attn_239_transpose_y_0"), val = tensor(true)]; + tensor attn_239_cast_fp16 = matmul(transpose_x = attn_239_transpose_x_0, transpose_y = attn_239_transpose_y_0, x = var_11742_cast_fp16, y = var_11746_cast_fp16)[name = tensor("attn_239_cast_fp16")]; + tensor var_11750 = const()[name = tensor("op_11750"), val = tensor([1, 1280, 1, -1])]; + tensor input_679_cast_fp16 = reshape(shape = var_11750, x = attn_239_cast_fp16)[name = tensor("input_679_cast_fp16")]; + tensor var_11755 = const()[name = tensor("op_11755"), val = tensor([1, 1])]; + tensor var_11757 = const()[name = tensor("op_11757"), val = tensor([1, 1])]; + tensor var_11759_pad_type_0 = const()[name = tensor("op_11759_pad_type_0"), val = tensor("custom")]; + tensor var_11759_pad_0 = const()[name = tensor("op_11759_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1706132160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707361024))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707361216)))]; + tensor var_11759_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_11757, groups = var_6895, pad = var_11759_pad_0, pad_type = var_11759_pad_type_0, strides = var_11755, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_679_cast_fp16)[name = tensor("op_11759_cast_fp16")]; + tensor inputs_359_cast_fp16 = add(x = var_11759_cast_fp16, y = inputs_357_cast_fp16)[name = tensor("inputs_359_cast_fp16")]; + tensor input_681_axes_0 = const()[name = tensor("input_681_axes_0"), val = tensor([1])]; + tensor input_681_gamma_0_to_fp16 = const()[name = tensor("input_681_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707363840)))]; + tensor input_681_beta_0_to_fp16 = const()[name = tensor("input_681_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707366464)))]; + tensor var_11769_to_fp16 = const()[name = tensor("op_11769_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_681_cast_fp16 = layer_norm(axes = input_681_axes_0, beta = input_681_beta_0_to_fp16, epsilon = var_11769_to_fp16, gamma = input_681_gamma_0_to_fp16, x = inputs_359_cast_fp16)[name = tensor("input_681_cast_fp16")]; + tensor var_11785 = const()[name = tensor("op_11785"), val = tensor([1, 1])]; + tensor var_11787 = const()[name = tensor("op_11787"), val = tensor([1, 1])]; + tensor var_11789_pad_type_0 = const()[name = tensor("op_11789_pad_type_0"), val = tensor("custom")]; + tensor var_11789_pad_0 = const()[name = tensor("op_11789_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707369088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717199552))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717199744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717207488))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11789_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11787, groups = var_6895, pad = var_11789_pad_0, pad_type = var_11789_pad_type_0, strides = var_11785, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_681_cast_fp16)[name = tensor("op_11789_cast_fp16")]; + tensor var_11790_split_sizes_0 = const()[name = tensor("op_11790_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11790_axis_0 = const()[name = tensor("op_11790_axis_0"), val = tensor(1)]; + tensor var_11790_cast_fp16_0, tensor var_11790_cast_fp16_1 = split(axis = var_11790_axis_0, split_sizes = var_11790_split_sizes_0, x = var_11789_cast_fp16)[name = tensor("op_11790_cast_fp16")]; + tensor var_11792_mode_0 = const()[name = tensor("op_11792_mode_0"), val = tensor("EXACT")]; + tensor var_11792_cast_fp16 = gelu(mode = var_11792_mode_0, x = var_11790_cast_fp16_1)[name = tensor("op_11792_cast_fp16")]; + tensor input_683_cast_fp16 = mul(x = var_11790_cast_fp16_0, y = var_11792_cast_fp16)[name = tensor("input_683_cast_fp16")]; + tensor var_11796 = const()[name = tensor("op_11796"), val = tensor([1, 1])]; + tensor var_11798 = const()[name = tensor("op_11798"), val = tensor([1, 1])]; + tensor var_11800_pad_type_0 = const()[name = tensor("op_11800_pad_type_0"), val = tensor("custom")]; + tensor var_11800_pad_0 = const()[name = tensor("op_11800_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717207680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722122944))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722123136)))]; + tensor var_11800_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_11798, groups = var_6895, pad = var_11800_pad_0, pad_type = var_11800_pad_type_0, strides = var_11796, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_683_cast_fp16)[name = tensor("op_11800_cast_fp16")]; + tensor inputs_361_cast_fp16 = add(x = var_11800_cast_fp16, y = inputs_359_cast_fp16)[name = tensor("inputs_361_cast_fp16")]; + tensor hidden_states_471_axes_0 = const()[name = tensor("hidden_states_471_axes_0"), val = tensor([1])]; + tensor hidden_states_471_gamma_0_to_fp16 = const()[name = tensor("hidden_states_471_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722125760)))]; + tensor hidden_states_471_beta_0_to_fp16 = const()[name = tensor("hidden_states_471_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722128384)))]; + tensor var_11816_to_fp16 = const()[name = tensor("op_11816_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_471_cast_fp16 = layer_norm(axes = hidden_states_471_axes_0, beta = hidden_states_471_beta_0_to_fp16, epsilon = var_11816_to_fp16, gamma = hidden_states_471_gamma_0_to_fp16, x = inputs_361_cast_fp16)[name = tensor("hidden_states_471_cast_fp16")]; + tensor var_11831 = const()[name = tensor("op_11831"), val = tensor([1, 1])]; + tensor var_11833 = const()[name = tensor("op_11833"), val = tensor([1, 1])]; + tensor q_241_pad_type_0 = const()[name = tensor("q_241_pad_type_0"), val = tensor("custom")]; + tensor q_241_pad_0 = const()[name = tensor("q_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722131008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1723359872))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_241_cast_fp16 = conv(dilations = var_11833, groups = var_6895, pad = q_241_pad_0, pad_type = q_241_pad_type_0, strides = var_11831, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_471_cast_fp16)[name = tensor("q_241_cast_fp16")]; + tensor var_11837 = const()[name = tensor("op_11837"), val = tensor([1, 1])]; + tensor var_11839 = const()[name = tensor("op_11839"), val = tensor([1, 1])]; + tensor k_241_pad_type_0 = const()[name = tensor("k_241_pad_type_0"), val = tensor("custom")]; + tensor k_241_pad_0 = const()[name = tensor("k_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1723360064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724588928))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_241_cast_fp16 = conv(dilations = var_11839, groups = var_6895, pad = k_241_pad_0, pad_type = k_241_pad_type_0, strides = var_11837, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_471_cast_fp16)[name = tensor("k_241_cast_fp16")]; + tensor var_11843 = const()[name = tensor("op_11843"), val = tensor([1, 1])]; + tensor var_11845 = const()[name = tensor("op_11845"), val = tensor([1, 1])]; + tensor v_241_pad_type_0 = const()[name = tensor("v_241_pad_type_0"), val = tensor("custom")]; + tensor v_241_pad_0 = const()[name = tensor("v_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724589120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1725817984))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_241_cast_fp16 = conv(dilations = var_11845, groups = var_6895, pad = v_241_pad_0, pad_type = v_241_pad_type_0, strides = var_11843, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_471_cast_fp16)[name = tensor("v_241_cast_fp16")]; + tensor var_11849 = const()[name = tensor("op_11849"), val = tensor([1, 20, 64, -1])]; + tensor var_11850_cast_fp16 = reshape(shape = var_11849, x = q_241_cast_fp16)[name = tensor("op_11850_cast_fp16")]; + tensor var_11851 = const()[name = tensor("op_11851"), val = tensor([1, 20, 64, -1])]; + tensor var_11852_cast_fp16 = reshape(shape = var_11851, x = k_241_cast_fp16)[name = tensor("op_11852_cast_fp16")]; + tensor var_11853 = const()[name = tensor("op_11853"), val = tensor([1, 20, 64, -1])]; + tensor var_11854_cast_fp16 = reshape(shape = var_11853, x = v_241_cast_fp16)[name = tensor("op_11854_cast_fp16")]; + tensor attn_weights_481_transpose_x_0 = const()[name = tensor("attn_weights_481_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_481_transpose_y_0 = const()[name = tensor("attn_weights_481_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_481_cast_fp16 = matmul(transpose_x = attn_weights_481_transpose_x_0, transpose_y = attn_weights_481_transpose_y_0, x = var_11850_cast_fp16, y = var_11852_cast_fp16)[name = tensor("attn_weights_481_cast_fp16")]; + tensor attn_weights_483_cast_fp16 = mul(x = attn_weights_481_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_483_cast_fp16")]; + tensor var_11858_cast_fp16 = softmax(axis = var_6879, x = attn_weights_483_cast_fp16)[name = tensor("op_11858_cast_fp16")]; + tensor attn_241_transpose_x_0 = const()[name = tensor("attn_241_transpose_x_0"), val = tensor(false)]; + tensor attn_241_transpose_y_0 = const()[name = tensor("attn_241_transpose_y_0"), val = tensor(true)]; + tensor attn_241_cast_fp16 = matmul(transpose_x = attn_241_transpose_x_0, transpose_y = attn_241_transpose_y_0, x = var_11854_cast_fp16, y = var_11858_cast_fp16)[name = tensor("attn_241_cast_fp16")]; + tensor var_11862 = const()[name = tensor("op_11862"), val = tensor([1, 1280, 1, -1])]; + tensor input_685_cast_fp16 = reshape(shape = var_11862, x = attn_241_cast_fp16)[name = tensor("input_685_cast_fp16")]; + tensor var_11867 = const()[name = tensor("op_11867"), val = tensor([1, 1])]; + tensor var_11869 = const()[name = tensor("op_11869"), val = tensor([1, 1])]; + tensor var_11871_pad_type_0 = const()[name = tensor("op_11871_pad_type_0"), val = tensor("custom")]; + tensor var_11871_pad_0 = const()[name = tensor("op_11871_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1725818176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727047040))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727047232)))]; + tensor var_11871_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_11869, groups = var_6895, pad = var_11871_pad_0, pad_type = var_11871_pad_type_0, strides = var_11867, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_685_cast_fp16)[name = tensor("op_11871_cast_fp16")]; + tensor inputs_363_cast_fp16 = add(x = var_11871_cast_fp16, y = inputs_361_cast_fp16)[name = tensor("inputs_363_cast_fp16")]; + tensor hidden_states_473_axes_0 = const()[name = tensor("hidden_states_473_axes_0"), val = tensor([1])]; + tensor hidden_states_473_gamma_0_to_fp16 = const()[name = tensor("hidden_states_473_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727049856)))]; + tensor hidden_states_473_beta_0_to_fp16 = const()[name = tensor("hidden_states_473_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727052480)))]; + tensor var_11881_to_fp16 = const()[name = tensor("op_11881_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_473_cast_fp16 = layer_norm(axes = hidden_states_473_axes_0, beta = hidden_states_473_beta_0_to_fp16, epsilon = var_11881_to_fp16, gamma = hidden_states_473_gamma_0_to_fp16, x = inputs_363_cast_fp16)[name = tensor("hidden_states_473_cast_fp16")]; + tensor var_11896 = const()[name = tensor("op_11896"), val = tensor([1, 1])]; + tensor var_11898 = const()[name = tensor("op_11898"), val = tensor([1, 1])]; + tensor q_243_pad_type_0 = const()[name = tensor("q_243_pad_type_0"), val = tensor("custom")]; + tensor q_243_pad_0 = const()[name = tensor("q_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727055104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728283968))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_243_cast_fp16 = conv(dilations = var_11898, groups = var_6895, pad = q_243_pad_0, pad_type = q_243_pad_type_0, strides = var_11896, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_473_cast_fp16)[name = tensor("q_243_cast_fp16")]; + tensor var_11902 = const()[name = tensor("op_11902"), val = tensor([1, 1])]; + tensor var_11904 = const()[name = tensor("op_11904"), val = tensor([1, 1])]; + tensor k_243_pad_type_0 = const()[name = tensor("k_243_pad_type_0"), val = tensor("custom")]; + tensor k_243_pad_0 = const()[name = tensor("k_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728284160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1730250304))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_243_cast_fp16 = conv(dilations = var_11904, groups = var_6895, pad = k_243_pad_0, pad_type = k_243_pad_type_0, strides = var_11902, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_243_cast_fp16")]; + tensor var_11908 = const()[name = tensor("op_11908"), val = tensor([1, 1])]; + tensor var_11910 = const()[name = tensor("op_11910"), val = tensor([1, 1])]; + tensor v_243_pad_type_0 = const()[name = tensor("v_243_pad_type_0"), val = tensor("custom")]; + tensor v_243_pad_0 = const()[name = tensor("v_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1730250496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1732216640))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_243_cast_fp16 = conv(dilations = var_11910, groups = var_6895, pad = v_243_pad_0, pad_type = v_243_pad_type_0, strides = var_11908, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_243_cast_fp16")]; + tensor var_11914 = const()[name = tensor("op_11914"), val = tensor([1, 20, 64, -1])]; + tensor var_11915_cast_fp16 = reshape(shape = var_11914, x = q_243_cast_fp16)[name = tensor("op_11915_cast_fp16")]; + tensor var_11916 = const()[name = tensor("op_11916"), val = tensor([1, 20, 64, -1])]; + tensor var_11917_cast_fp16 = reshape(shape = var_11916, x = k_243_cast_fp16)[name = tensor("op_11917_cast_fp16")]; + tensor var_11918 = const()[name = tensor("op_11918"), val = tensor([1, 20, 64, -1])]; + tensor var_11919_cast_fp16 = reshape(shape = var_11918, x = v_243_cast_fp16)[name = tensor("op_11919_cast_fp16")]; + tensor attn_weights_485_transpose_x_0 = const()[name = tensor("attn_weights_485_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_485_transpose_y_0 = const()[name = tensor("attn_weights_485_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_485_cast_fp16 = matmul(transpose_x = attn_weights_485_transpose_x_0, transpose_y = attn_weights_485_transpose_y_0, x = var_11915_cast_fp16, y = var_11917_cast_fp16)[name = tensor("attn_weights_485_cast_fp16")]; + tensor attn_weights_487_cast_fp16 = mul(x = attn_weights_485_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_487_cast_fp16")]; + tensor var_11923_cast_fp16 = softmax(axis = var_6879, x = attn_weights_487_cast_fp16)[name = tensor("op_11923_cast_fp16")]; + tensor attn_243_transpose_x_0 = const()[name = tensor("attn_243_transpose_x_0"), val = tensor(false)]; + tensor attn_243_transpose_y_0 = const()[name = tensor("attn_243_transpose_y_0"), val = tensor(true)]; + tensor attn_243_cast_fp16 = matmul(transpose_x = attn_243_transpose_x_0, transpose_y = attn_243_transpose_y_0, x = var_11919_cast_fp16, y = var_11923_cast_fp16)[name = tensor("attn_243_cast_fp16")]; + tensor var_11927 = const()[name = tensor("op_11927"), val = tensor([1, 1280, 1, -1])]; + tensor input_687_cast_fp16 = reshape(shape = var_11927, x = attn_243_cast_fp16)[name = tensor("input_687_cast_fp16")]; + tensor var_11932 = const()[name = tensor("op_11932"), val = tensor([1, 1])]; + tensor var_11934 = const()[name = tensor("op_11934"), val = tensor([1, 1])]; + tensor var_11936_pad_type_0 = const()[name = tensor("op_11936_pad_type_0"), val = tensor("custom")]; + tensor var_11936_pad_0 = const()[name = tensor("op_11936_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1732216832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733445696))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733445888)))]; + tensor var_11936_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_11934, groups = var_6895, pad = var_11936_pad_0, pad_type = var_11936_pad_type_0, strides = var_11932, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_687_cast_fp16)[name = tensor("op_11936_cast_fp16")]; + tensor inputs_365_cast_fp16 = add(x = var_11936_cast_fp16, y = inputs_363_cast_fp16)[name = tensor("inputs_365_cast_fp16")]; + tensor input_689_axes_0 = const()[name = tensor("input_689_axes_0"), val = tensor([1])]; + tensor input_689_gamma_0_to_fp16 = const()[name = tensor("input_689_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733448512)))]; + tensor input_689_beta_0_to_fp16 = const()[name = tensor("input_689_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733451136)))]; + tensor var_11946_to_fp16 = const()[name = tensor("op_11946_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_689_cast_fp16 = layer_norm(axes = input_689_axes_0, beta = input_689_beta_0_to_fp16, epsilon = var_11946_to_fp16, gamma = input_689_gamma_0_to_fp16, x = inputs_365_cast_fp16)[name = tensor("input_689_cast_fp16")]; + tensor var_11962 = const()[name = tensor("op_11962"), val = tensor([1, 1])]; + tensor var_11964 = const()[name = tensor("op_11964"), val = tensor([1, 1])]; + tensor var_11966_pad_type_0 = const()[name = tensor("op_11966_pad_type_0"), val = tensor("custom")]; + tensor var_11966_pad_0 = const()[name = tensor("op_11966_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733453760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743284224))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743284416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743292160))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11966_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11964, groups = var_6895, pad = var_11966_pad_0, pad_type = var_11966_pad_type_0, strides = var_11962, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_689_cast_fp16)[name = tensor("op_11966_cast_fp16")]; + tensor var_11967_split_sizes_0 = const()[name = tensor("op_11967_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11967_axis_0 = const()[name = tensor("op_11967_axis_0"), val = tensor(1)]; + tensor var_11967_cast_fp16_0, tensor var_11967_cast_fp16_1 = split(axis = var_11967_axis_0, split_sizes = var_11967_split_sizes_0, x = var_11966_cast_fp16)[name = tensor("op_11967_cast_fp16")]; + tensor var_11969_mode_0 = const()[name = tensor("op_11969_mode_0"), val = tensor("EXACT")]; + tensor var_11969_cast_fp16 = gelu(mode = var_11969_mode_0, x = var_11967_cast_fp16_1)[name = tensor("op_11969_cast_fp16")]; + tensor input_691_cast_fp16 = mul(x = var_11967_cast_fp16_0, y = var_11969_cast_fp16)[name = tensor("input_691_cast_fp16")]; + tensor var_11973 = const()[name = tensor("op_11973"), val = tensor([1, 1])]; + tensor var_11975 = const()[name = tensor("op_11975"), val = tensor([1, 1])]; + tensor var_11977_pad_type_0 = const()[name = tensor("op_11977_pad_type_0"), val = tensor("custom")]; + tensor var_11977_pad_0 = const()[name = tensor("op_11977_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743292352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748207616))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748207808)))]; + tensor var_11977_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_11975, groups = var_6895, pad = var_11977_pad_0, pad_type = var_11977_pad_type_0, strides = var_11973, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_691_cast_fp16)[name = tensor("op_11977_cast_fp16")]; + tensor inputs_367_cast_fp16 = add(x = var_11977_cast_fp16, y = inputs_365_cast_fp16)[name = tensor("inputs_367_cast_fp16")]; + tensor hidden_states_477_axes_0 = const()[name = tensor("hidden_states_477_axes_0"), val = tensor([1])]; + tensor hidden_states_477_gamma_0_to_fp16 = const()[name = tensor("hidden_states_477_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748210432)))]; + tensor hidden_states_477_beta_0_to_fp16 = const()[name = tensor("hidden_states_477_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748213056)))]; + tensor var_11993_to_fp16 = const()[name = tensor("op_11993_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_477_cast_fp16 = layer_norm(axes = hidden_states_477_axes_0, beta = hidden_states_477_beta_0_to_fp16, epsilon = var_11993_to_fp16, gamma = hidden_states_477_gamma_0_to_fp16, x = inputs_367_cast_fp16)[name = tensor("hidden_states_477_cast_fp16")]; + tensor var_12008 = const()[name = tensor("op_12008"), val = tensor([1, 1])]; + tensor var_12010 = const()[name = tensor("op_12010"), val = tensor([1, 1])]; + tensor q_245_pad_type_0 = const()[name = tensor("q_245_pad_type_0"), val = tensor("custom")]; + tensor q_245_pad_0 = const()[name = tensor("q_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748215680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1749444544))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_245_cast_fp16 = conv(dilations = var_12010, groups = var_6895, pad = q_245_pad_0, pad_type = q_245_pad_type_0, strides = var_12008, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_477_cast_fp16)[name = tensor("q_245_cast_fp16")]; + tensor var_12014 = const()[name = tensor("op_12014"), val = tensor([1, 1])]; + tensor var_12016 = const()[name = tensor("op_12016"), val = tensor([1, 1])]; + tensor k_245_pad_type_0 = const()[name = tensor("k_245_pad_type_0"), val = tensor("custom")]; + tensor k_245_pad_0 = const()[name = tensor("k_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1749444736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1750673600))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_245_cast_fp16 = conv(dilations = var_12016, groups = var_6895, pad = k_245_pad_0, pad_type = k_245_pad_type_0, strides = var_12014, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_477_cast_fp16)[name = tensor("k_245_cast_fp16")]; + tensor var_12020 = const()[name = tensor("op_12020"), val = tensor([1, 1])]; + tensor var_12022 = const()[name = tensor("op_12022"), val = tensor([1, 1])]; + tensor v_245_pad_type_0 = const()[name = tensor("v_245_pad_type_0"), val = tensor("custom")]; + tensor v_245_pad_0 = const()[name = tensor("v_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1750673792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1751902656))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_245_cast_fp16 = conv(dilations = var_12022, groups = var_6895, pad = v_245_pad_0, pad_type = v_245_pad_type_0, strides = var_12020, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_477_cast_fp16)[name = tensor("v_245_cast_fp16")]; + tensor var_12026 = const()[name = tensor("op_12026"), val = tensor([1, 20, 64, -1])]; + tensor var_12027_cast_fp16 = reshape(shape = var_12026, x = q_245_cast_fp16)[name = tensor("op_12027_cast_fp16")]; + tensor var_12028 = const()[name = tensor("op_12028"), val = tensor([1, 20, 64, -1])]; + tensor var_12029_cast_fp16 = reshape(shape = var_12028, x = k_245_cast_fp16)[name = tensor("op_12029_cast_fp16")]; + tensor var_12030 = const()[name = tensor("op_12030"), val = tensor([1, 20, 64, -1])]; + tensor var_12031_cast_fp16 = reshape(shape = var_12030, x = v_245_cast_fp16)[name = tensor("op_12031_cast_fp16")]; + tensor attn_weights_489_transpose_x_0 = const()[name = tensor("attn_weights_489_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_489_transpose_y_0 = const()[name = tensor("attn_weights_489_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_489_cast_fp16 = matmul(transpose_x = attn_weights_489_transpose_x_0, transpose_y = attn_weights_489_transpose_y_0, x = var_12027_cast_fp16, y = var_12029_cast_fp16)[name = tensor("attn_weights_489_cast_fp16")]; + tensor attn_weights_491_cast_fp16 = mul(x = attn_weights_489_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_491_cast_fp16")]; + tensor var_12035_cast_fp16 = softmax(axis = var_6879, x = attn_weights_491_cast_fp16)[name = tensor("op_12035_cast_fp16")]; + tensor attn_245_transpose_x_0 = const()[name = tensor("attn_245_transpose_x_0"), val = tensor(false)]; + tensor attn_245_transpose_y_0 = const()[name = tensor("attn_245_transpose_y_0"), val = tensor(true)]; + tensor attn_245_cast_fp16 = matmul(transpose_x = attn_245_transpose_x_0, transpose_y = attn_245_transpose_y_0, x = var_12031_cast_fp16, y = var_12035_cast_fp16)[name = tensor("attn_245_cast_fp16")]; + tensor var_12039 = const()[name = tensor("op_12039"), val = tensor([1, 1280, 1, -1])]; + tensor input_693_cast_fp16 = reshape(shape = var_12039, x = attn_245_cast_fp16)[name = tensor("input_693_cast_fp16")]; + tensor var_12044 = const()[name = tensor("op_12044"), val = tensor([1, 1])]; + tensor var_12046 = const()[name = tensor("op_12046"), val = tensor([1, 1])]; + tensor var_12048_pad_type_0 = const()[name = tensor("op_12048_pad_type_0"), val = tensor("custom")]; + tensor var_12048_pad_0 = const()[name = tensor("op_12048_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1751902848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753131712))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753131904)))]; + tensor var_12048_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_12046, groups = var_6895, pad = var_12048_pad_0, pad_type = var_12048_pad_type_0, strides = var_12044, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_693_cast_fp16)[name = tensor("op_12048_cast_fp16")]; + tensor inputs_369_cast_fp16 = add(x = var_12048_cast_fp16, y = inputs_367_cast_fp16)[name = tensor("inputs_369_cast_fp16")]; + tensor hidden_states_479_axes_0 = const()[name = tensor("hidden_states_479_axes_0"), val = tensor([1])]; + tensor hidden_states_479_gamma_0_to_fp16 = const()[name = tensor("hidden_states_479_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753134528)))]; + tensor hidden_states_479_beta_0_to_fp16 = const()[name = tensor("hidden_states_479_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753137152)))]; + tensor var_12058_to_fp16 = const()[name = tensor("op_12058_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_479_cast_fp16 = layer_norm(axes = hidden_states_479_axes_0, beta = hidden_states_479_beta_0_to_fp16, epsilon = var_12058_to_fp16, gamma = hidden_states_479_gamma_0_to_fp16, x = inputs_369_cast_fp16)[name = tensor("hidden_states_479_cast_fp16")]; + tensor var_12073 = const()[name = tensor("op_12073"), val = tensor([1, 1])]; + tensor var_12075 = const()[name = tensor("op_12075"), val = tensor([1, 1])]; + tensor q_247_pad_type_0 = const()[name = tensor("q_247_pad_type_0"), val = tensor("custom")]; + tensor q_247_pad_0 = const()[name = tensor("q_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753139776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1754368640))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_247_cast_fp16 = conv(dilations = var_12075, groups = var_6895, pad = q_247_pad_0, pad_type = q_247_pad_type_0, strides = var_12073, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_479_cast_fp16)[name = tensor("q_247_cast_fp16")]; + tensor var_12079 = const()[name = tensor("op_12079"), val = tensor([1, 1])]; + tensor var_12081 = const()[name = tensor("op_12081"), val = tensor([1, 1])]; + tensor k_247_pad_type_0 = const()[name = tensor("k_247_pad_type_0"), val = tensor("custom")]; + tensor k_247_pad_0 = const()[name = tensor("k_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1754368832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756334976))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_247_cast_fp16 = conv(dilations = var_12081, groups = var_6895, pad = k_247_pad_0, pad_type = k_247_pad_type_0, strides = var_12079, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_247_cast_fp16")]; + tensor var_12085 = const()[name = tensor("op_12085"), val = tensor([1, 1])]; + tensor var_12087 = const()[name = tensor("op_12087"), val = tensor([1, 1])]; + tensor v_247_pad_type_0 = const()[name = tensor("v_247_pad_type_0"), val = tensor("custom")]; + tensor v_247_pad_0 = const()[name = tensor("v_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756335168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1758301312))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_247_cast_fp16 = conv(dilations = var_12087, groups = var_6895, pad = v_247_pad_0, pad_type = v_247_pad_type_0, strides = var_12085, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_247_cast_fp16")]; + tensor var_12091 = const()[name = tensor("op_12091"), val = tensor([1, 20, 64, -1])]; + tensor var_12092_cast_fp16 = reshape(shape = var_12091, x = q_247_cast_fp16)[name = tensor("op_12092_cast_fp16")]; + tensor var_12093 = const()[name = tensor("op_12093"), val = tensor([1, 20, 64, -1])]; + tensor var_12094_cast_fp16 = reshape(shape = var_12093, x = k_247_cast_fp16)[name = tensor("op_12094_cast_fp16")]; + tensor var_12095 = const()[name = tensor("op_12095"), val = tensor([1, 20, 64, -1])]; + tensor var_12096_cast_fp16 = reshape(shape = var_12095, x = v_247_cast_fp16)[name = tensor("op_12096_cast_fp16")]; + tensor attn_weights_493_transpose_x_0 = const()[name = tensor("attn_weights_493_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_493_transpose_y_0 = const()[name = tensor("attn_weights_493_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_493_cast_fp16 = matmul(transpose_x = attn_weights_493_transpose_x_0, transpose_y = attn_weights_493_transpose_y_0, x = var_12092_cast_fp16, y = var_12094_cast_fp16)[name = tensor("attn_weights_493_cast_fp16")]; + tensor attn_weights_495_cast_fp16 = mul(x = attn_weights_493_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_495_cast_fp16")]; + tensor var_12100_cast_fp16 = softmax(axis = var_6879, x = attn_weights_495_cast_fp16)[name = tensor("op_12100_cast_fp16")]; + tensor attn_247_transpose_x_0 = const()[name = tensor("attn_247_transpose_x_0"), val = tensor(false)]; + tensor attn_247_transpose_y_0 = const()[name = tensor("attn_247_transpose_y_0"), val = tensor(true)]; + tensor attn_247_cast_fp16 = matmul(transpose_x = attn_247_transpose_x_0, transpose_y = attn_247_transpose_y_0, x = var_12096_cast_fp16, y = var_12100_cast_fp16)[name = tensor("attn_247_cast_fp16")]; + tensor var_12104 = const()[name = tensor("op_12104"), val = tensor([1, 1280, 1, -1])]; + tensor input_695_cast_fp16 = reshape(shape = var_12104, x = attn_247_cast_fp16)[name = tensor("input_695_cast_fp16")]; + tensor var_12109 = const()[name = tensor("op_12109"), val = tensor([1, 1])]; + tensor var_12111 = const()[name = tensor("op_12111"), val = tensor([1, 1])]; + tensor var_12113_pad_type_0 = const()[name = tensor("op_12113_pad_type_0"), val = tensor("custom")]; + tensor var_12113_pad_0 = const()[name = tensor("op_12113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1758301504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759530368))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759530560)))]; + tensor var_12113_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_12111, groups = var_6895, pad = var_12113_pad_0, pad_type = var_12113_pad_type_0, strides = var_12109, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_695_cast_fp16)[name = tensor("op_12113_cast_fp16")]; + tensor inputs_371_cast_fp16 = add(x = var_12113_cast_fp16, y = inputs_369_cast_fp16)[name = tensor("inputs_371_cast_fp16")]; + tensor input_697_axes_0 = const()[name = tensor("input_697_axes_0"), val = tensor([1])]; + tensor input_697_gamma_0_to_fp16 = const()[name = tensor("input_697_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759533184)))]; + tensor input_697_beta_0_to_fp16 = const()[name = tensor("input_697_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759535808)))]; + tensor var_12123_to_fp16 = const()[name = tensor("op_12123_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = input_697_beta_0_to_fp16, epsilon = var_12123_to_fp16, gamma = input_697_gamma_0_to_fp16, x = inputs_371_cast_fp16)[name = tensor("input_697_cast_fp16")]; + tensor var_12139 = const()[name = tensor("op_12139"), val = tensor([1, 1])]; + tensor var_12141 = const()[name = tensor("op_12141"), val = tensor([1, 1])]; + tensor var_12143_pad_type_0 = const()[name = tensor("op_12143_pad_type_0"), val = tensor("custom")]; + tensor var_12143_pad_0 = const()[name = tensor("op_12143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759538432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769368896))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769369088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769376832))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_12143_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12141, groups = var_6895, pad = var_12143_pad_0, pad_type = var_12143_pad_type_0, strides = var_12139, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_697_cast_fp16)[name = tensor("op_12143_cast_fp16")]; + tensor var_12144_split_sizes_0 = const()[name = tensor("op_12144_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12144_axis_0 = const()[name = tensor("op_12144_axis_0"), val = tensor(1)]; + tensor var_12144_cast_fp16_0, tensor var_12144_cast_fp16_1 = split(axis = var_12144_axis_0, split_sizes = var_12144_split_sizes_0, x = var_12143_cast_fp16)[name = tensor("op_12144_cast_fp16")]; + tensor var_12146_mode_0 = const()[name = tensor("op_12146_mode_0"), val = tensor("EXACT")]; + tensor var_12146_cast_fp16 = gelu(mode = var_12146_mode_0, x = var_12144_cast_fp16_1)[name = tensor("op_12146_cast_fp16")]; + tensor input_699_cast_fp16 = mul(x = var_12144_cast_fp16_0, y = var_12146_cast_fp16)[name = tensor("input_699_cast_fp16")]; + tensor var_12150 = const()[name = tensor("op_12150"), val = tensor([1, 1])]; + tensor var_12152 = const()[name = tensor("op_12152"), val = tensor([1, 1])]; + tensor var_12154_pad_type_0 = const()[name = tensor("op_12154_pad_type_0"), val = tensor("custom")]; + tensor var_12154_pad_0 = const()[name = tensor("op_12154_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769377024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774292288))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774292480)))]; + tensor var_12154_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_12152, groups = var_6895, pad = var_12154_pad_0, pad_type = var_12154_pad_type_0, strides = var_12150, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_699_cast_fp16)[name = tensor("op_12154_cast_fp16")]; + tensor inputs_373_cast_fp16 = add(x = var_12154_cast_fp16, y = inputs_371_cast_fp16)[name = tensor("inputs_373_cast_fp16")]; + tensor hidden_states_483_axes_0 = const()[name = tensor("hidden_states_483_axes_0"), val = tensor([1])]; + tensor hidden_states_483_gamma_0_to_fp16 = const()[name = tensor("hidden_states_483_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774295104)))]; + tensor hidden_states_483_beta_0_to_fp16 = const()[name = tensor("hidden_states_483_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774297728)))]; + tensor var_12170_to_fp16 = const()[name = tensor("op_12170_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_483_cast_fp16 = layer_norm(axes = hidden_states_483_axes_0, beta = hidden_states_483_beta_0_to_fp16, epsilon = var_12170_to_fp16, gamma = hidden_states_483_gamma_0_to_fp16, x = inputs_373_cast_fp16)[name = tensor("hidden_states_483_cast_fp16")]; + tensor var_12185 = const()[name = tensor("op_12185"), val = tensor([1, 1])]; + tensor var_12187 = const()[name = tensor("op_12187"), val = tensor([1, 1])]; + tensor q_249_pad_type_0 = const()[name = tensor("q_249_pad_type_0"), val = tensor("custom")]; + tensor q_249_pad_0 = const()[name = tensor("q_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774300352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1775529216))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_249_cast_fp16 = conv(dilations = var_12187, groups = var_6895, pad = q_249_pad_0, pad_type = q_249_pad_type_0, strides = var_12185, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_483_cast_fp16)[name = tensor("q_249_cast_fp16")]; + tensor var_12191 = const()[name = tensor("op_12191"), val = tensor([1, 1])]; + tensor var_12193 = const()[name = tensor("op_12193"), val = tensor([1, 1])]; + tensor k_249_pad_type_0 = const()[name = tensor("k_249_pad_type_0"), val = tensor("custom")]; + tensor k_249_pad_0 = const()[name = tensor("k_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1775529408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1776758272))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_249_cast_fp16 = conv(dilations = var_12193, groups = var_6895, pad = k_249_pad_0, pad_type = k_249_pad_type_0, strides = var_12191, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_483_cast_fp16)[name = tensor("k_249_cast_fp16")]; + tensor var_12197 = const()[name = tensor("op_12197"), val = tensor([1, 1])]; + tensor var_12199 = const()[name = tensor("op_12199"), val = tensor([1, 1])]; + tensor v_249_pad_type_0 = const()[name = tensor("v_249_pad_type_0"), val = tensor("custom")]; + tensor v_249_pad_0 = const()[name = tensor("v_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1776758464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1777987328))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_249_cast_fp16 = conv(dilations = var_12199, groups = var_6895, pad = v_249_pad_0, pad_type = v_249_pad_type_0, strides = var_12197, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_483_cast_fp16)[name = tensor("v_249_cast_fp16")]; + tensor var_12203 = const()[name = tensor("op_12203"), val = tensor([1, 20, 64, -1])]; + tensor var_12204_cast_fp16 = reshape(shape = var_12203, x = q_249_cast_fp16)[name = tensor("op_12204_cast_fp16")]; + tensor var_12205 = const()[name = tensor("op_12205"), val = tensor([1, 20, 64, -1])]; + tensor var_12206_cast_fp16 = reshape(shape = var_12205, x = k_249_cast_fp16)[name = tensor("op_12206_cast_fp16")]; + tensor var_12207 = const()[name = tensor("op_12207"), val = tensor([1, 20, 64, -1])]; + tensor var_12208_cast_fp16 = reshape(shape = var_12207, x = v_249_cast_fp16)[name = tensor("op_12208_cast_fp16")]; + tensor attn_weights_497_transpose_x_0 = const()[name = tensor("attn_weights_497_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_497_transpose_y_0 = const()[name = tensor("attn_weights_497_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_497_cast_fp16 = matmul(transpose_x = attn_weights_497_transpose_x_0, transpose_y = attn_weights_497_transpose_y_0, x = var_12204_cast_fp16, y = var_12206_cast_fp16)[name = tensor("attn_weights_497_cast_fp16")]; + tensor attn_weights_499_cast_fp16 = mul(x = attn_weights_497_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_499_cast_fp16")]; + tensor var_12212_cast_fp16 = softmax(axis = var_6879, x = attn_weights_499_cast_fp16)[name = tensor("op_12212_cast_fp16")]; + tensor attn_249_transpose_x_0 = const()[name = tensor("attn_249_transpose_x_0"), val = tensor(false)]; + tensor attn_249_transpose_y_0 = const()[name = tensor("attn_249_transpose_y_0"), val = tensor(true)]; + tensor attn_249_cast_fp16 = matmul(transpose_x = attn_249_transpose_x_0, transpose_y = attn_249_transpose_y_0, x = var_12208_cast_fp16, y = var_12212_cast_fp16)[name = tensor("attn_249_cast_fp16")]; + tensor var_12216 = const()[name = tensor("op_12216"), val = tensor([1, 1280, 1, -1])]; + tensor input_701_cast_fp16 = reshape(shape = var_12216, x = attn_249_cast_fp16)[name = tensor("input_701_cast_fp16")]; + tensor var_12221 = const()[name = tensor("op_12221"), val = tensor([1, 1])]; + tensor var_12223 = const()[name = tensor("op_12223"), val = tensor([1, 1])]; + tensor var_12225_pad_type_0 = const()[name = tensor("op_12225_pad_type_0"), val = tensor("custom")]; + tensor var_12225_pad_0 = const()[name = tensor("op_12225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1777987520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779216384))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779216576)))]; + tensor var_12225_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_12223, groups = var_6895, pad = var_12225_pad_0, pad_type = var_12225_pad_type_0, strides = var_12221, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_701_cast_fp16)[name = tensor("op_12225_cast_fp16")]; + tensor inputs_375_cast_fp16 = add(x = var_12225_cast_fp16, y = inputs_373_cast_fp16)[name = tensor("inputs_375_cast_fp16")]; + tensor hidden_states_485_axes_0 = const()[name = tensor("hidden_states_485_axes_0"), val = tensor([1])]; + tensor hidden_states_485_gamma_0_to_fp16 = const()[name = tensor("hidden_states_485_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779219200)))]; + tensor hidden_states_485_beta_0_to_fp16 = const()[name = tensor("hidden_states_485_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779221824)))]; + tensor var_12235_to_fp16 = const()[name = tensor("op_12235_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_485_cast_fp16 = layer_norm(axes = hidden_states_485_axes_0, beta = hidden_states_485_beta_0_to_fp16, epsilon = var_12235_to_fp16, gamma = hidden_states_485_gamma_0_to_fp16, x = inputs_375_cast_fp16)[name = tensor("hidden_states_485_cast_fp16")]; + tensor var_12250 = const()[name = tensor("op_12250"), val = tensor([1, 1])]; + tensor var_12252 = const()[name = tensor("op_12252"), val = tensor([1, 1])]; + tensor q_251_pad_type_0 = const()[name = tensor("q_251_pad_type_0"), val = tensor("custom")]; + tensor q_251_pad_0 = const()[name = tensor("q_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779224448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1780453312))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_251_cast_fp16 = conv(dilations = var_12252, groups = var_6895, pad = q_251_pad_0, pad_type = q_251_pad_type_0, strides = var_12250, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_485_cast_fp16)[name = tensor("q_251_cast_fp16")]; + tensor var_12256 = const()[name = tensor("op_12256"), val = tensor([1, 1])]; + tensor var_12258 = const()[name = tensor("op_12258"), val = tensor([1, 1])]; + tensor k_251_pad_type_0 = const()[name = tensor("k_251_pad_type_0"), val = tensor("custom")]; + tensor k_251_pad_0 = const()[name = tensor("k_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1780453504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782419648))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_251_cast_fp16 = conv(dilations = var_12258, groups = var_6895, pad = k_251_pad_0, pad_type = k_251_pad_type_0, strides = var_12256, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_251_cast_fp16")]; + tensor var_12262 = const()[name = tensor("op_12262"), val = tensor([1, 1])]; + tensor var_12264 = const()[name = tensor("op_12264"), val = tensor([1, 1])]; + tensor v_251_pad_type_0 = const()[name = tensor("v_251_pad_type_0"), val = tensor("custom")]; + tensor v_251_pad_0 = const()[name = tensor("v_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782419840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1784385984))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_251_cast_fp16 = conv(dilations = var_12264, groups = var_6895, pad = v_251_pad_0, pad_type = v_251_pad_type_0, strides = var_12262, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_251_cast_fp16")]; + tensor var_12268 = const()[name = tensor("op_12268"), val = tensor([1, 20, 64, -1])]; + tensor var_12269_cast_fp16 = reshape(shape = var_12268, x = q_251_cast_fp16)[name = tensor("op_12269_cast_fp16")]; + tensor var_12270 = const()[name = tensor("op_12270"), val = tensor([1, 20, 64, -1])]; + tensor var_12271_cast_fp16 = reshape(shape = var_12270, x = k_251_cast_fp16)[name = tensor("op_12271_cast_fp16")]; + tensor var_12272 = const()[name = tensor("op_12272"), val = tensor([1, 20, 64, -1])]; + tensor var_12273_cast_fp16 = reshape(shape = var_12272, x = v_251_cast_fp16)[name = tensor("op_12273_cast_fp16")]; + tensor attn_weights_501_transpose_x_0 = const()[name = tensor("attn_weights_501_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_501_transpose_y_0 = const()[name = tensor("attn_weights_501_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_501_cast_fp16 = matmul(transpose_x = attn_weights_501_transpose_x_0, transpose_y = attn_weights_501_transpose_y_0, x = var_12269_cast_fp16, y = var_12271_cast_fp16)[name = tensor("attn_weights_501_cast_fp16")]; + tensor attn_weights_503_cast_fp16 = mul(x = attn_weights_501_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_503_cast_fp16")]; + tensor var_12277_cast_fp16 = softmax(axis = var_6879, x = attn_weights_503_cast_fp16)[name = tensor("op_12277_cast_fp16")]; + tensor attn_251_transpose_x_0 = const()[name = tensor("attn_251_transpose_x_0"), val = tensor(false)]; + tensor attn_251_transpose_y_0 = const()[name = tensor("attn_251_transpose_y_0"), val = tensor(true)]; + tensor attn_251_cast_fp16 = matmul(transpose_x = attn_251_transpose_x_0, transpose_y = attn_251_transpose_y_0, x = var_12273_cast_fp16, y = var_12277_cast_fp16)[name = tensor("attn_251_cast_fp16")]; + tensor var_12281 = const()[name = tensor("op_12281"), val = tensor([1, 1280, 1, -1])]; + tensor input_703_cast_fp16 = reshape(shape = var_12281, x = attn_251_cast_fp16)[name = tensor("input_703_cast_fp16")]; + tensor var_12286 = const()[name = tensor("op_12286"), val = tensor([1, 1])]; + tensor var_12288 = const()[name = tensor("op_12288"), val = tensor([1, 1])]; + tensor var_12290_pad_type_0 = const()[name = tensor("op_12290_pad_type_0"), val = tensor("custom")]; + tensor var_12290_pad_0 = const()[name = tensor("op_12290_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1784386176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785615040))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785615232)))]; + tensor var_12290_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_12288, groups = var_6895, pad = var_12290_pad_0, pad_type = var_12290_pad_type_0, strides = var_12286, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_703_cast_fp16)[name = tensor("op_12290_cast_fp16")]; + tensor inputs_377_cast_fp16 = add(x = var_12290_cast_fp16, y = inputs_375_cast_fp16)[name = tensor("inputs_377_cast_fp16")]; + tensor input_705_axes_0 = const()[name = tensor("input_705_axes_0"), val = tensor([1])]; + tensor input_705_gamma_0_to_fp16 = const()[name = tensor("input_705_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785617856)))]; + tensor input_705_beta_0_to_fp16 = const()[name = tensor("input_705_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785620480)))]; + tensor var_12300_to_fp16 = const()[name = tensor("op_12300_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_705_cast_fp16 = layer_norm(axes = input_705_axes_0, beta = input_705_beta_0_to_fp16, epsilon = var_12300_to_fp16, gamma = input_705_gamma_0_to_fp16, x = inputs_377_cast_fp16)[name = tensor("input_705_cast_fp16")]; + tensor var_12316 = const()[name = tensor("op_12316"), val = tensor([1, 1])]; + tensor var_12318 = const()[name = tensor("op_12318"), val = tensor([1, 1])]; + tensor var_12320_pad_type_0 = const()[name = tensor("op_12320_pad_type_0"), val = tensor("custom")]; + tensor var_12320_pad_0 = const()[name = tensor("op_12320_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785623104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795453568))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795453760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795461504))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_12320_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12318, groups = var_6895, pad = var_12320_pad_0, pad_type = var_12320_pad_type_0, strides = var_12316, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_705_cast_fp16)[name = tensor("op_12320_cast_fp16")]; + tensor var_12321_split_sizes_0 = const()[name = tensor("op_12321_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12321_axis_0 = const()[name = tensor("op_12321_axis_0"), val = tensor(1)]; + tensor var_12321_cast_fp16_0, tensor var_12321_cast_fp16_1 = split(axis = var_12321_axis_0, split_sizes = var_12321_split_sizes_0, x = var_12320_cast_fp16)[name = tensor("op_12321_cast_fp16")]; + tensor var_12323_mode_0 = const()[name = tensor("op_12323_mode_0"), val = tensor("EXACT")]; + tensor var_12323_cast_fp16 = gelu(mode = var_12323_mode_0, x = var_12321_cast_fp16_1)[name = tensor("op_12323_cast_fp16")]; + tensor input_707_cast_fp16 = mul(x = var_12321_cast_fp16_0, y = var_12323_cast_fp16)[name = tensor("input_707_cast_fp16")]; + tensor var_12327 = const()[name = tensor("op_12327"), val = tensor([1, 1])]; + tensor var_12329 = const()[name = tensor("op_12329"), val = tensor([1, 1])]; + tensor var_12331_pad_type_0 = const()[name = tensor("op_12331_pad_type_0"), val = tensor("custom")]; + tensor var_12331_pad_0 = const()[name = tensor("op_12331_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795461696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800376960))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800377152)))]; + tensor var_12331_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_12329, groups = var_6895, pad = var_12331_pad_0, pad_type = var_12331_pad_type_0, strides = var_12327, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_707_cast_fp16)[name = tensor("op_12331_cast_fp16")]; + tensor inputs_379_cast_fp16 = add(x = var_12331_cast_fp16, y = inputs_377_cast_fp16)[name = tensor("inputs_379_cast_fp16")]; + tensor hidden_states_489_axes_0 = const()[name = tensor("hidden_states_489_axes_0"), val = tensor([1])]; + tensor hidden_states_489_gamma_0_to_fp16 = const()[name = tensor("hidden_states_489_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800379776)))]; + tensor hidden_states_489_beta_0_to_fp16 = const()[name = tensor("hidden_states_489_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800382400)))]; + tensor var_12347_to_fp16 = const()[name = tensor("op_12347_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_489_cast_fp16 = layer_norm(axes = hidden_states_489_axes_0, beta = hidden_states_489_beta_0_to_fp16, epsilon = var_12347_to_fp16, gamma = hidden_states_489_gamma_0_to_fp16, x = inputs_379_cast_fp16)[name = tensor("hidden_states_489_cast_fp16")]; + tensor var_12362 = const()[name = tensor("op_12362"), val = tensor([1, 1])]; + tensor var_12364 = const()[name = tensor("op_12364"), val = tensor([1, 1])]; + tensor q_253_pad_type_0 = const()[name = tensor("q_253_pad_type_0"), val = tensor("custom")]; + tensor q_253_pad_0 = const()[name = tensor("q_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800385024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1801613888))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_253_cast_fp16 = conv(dilations = var_12364, groups = var_6895, pad = q_253_pad_0, pad_type = q_253_pad_type_0, strides = var_12362, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_489_cast_fp16)[name = tensor("q_253_cast_fp16")]; + tensor var_12368 = const()[name = tensor("op_12368"), val = tensor([1, 1])]; + tensor var_12370 = const()[name = tensor("op_12370"), val = tensor([1, 1])]; + tensor k_253_pad_type_0 = const()[name = tensor("k_253_pad_type_0"), val = tensor("custom")]; + tensor k_253_pad_0 = const()[name = tensor("k_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1801614080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1802842944))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_253_cast_fp16 = conv(dilations = var_12370, groups = var_6895, pad = k_253_pad_0, pad_type = k_253_pad_type_0, strides = var_12368, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_489_cast_fp16)[name = tensor("k_253_cast_fp16")]; + tensor var_12374 = const()[name = tensor("op_12374"), val = tensor([1, 1])]; + tensor var_12376 = const()[name = tensor("op_12376"), val = tensor([1, 1])]; + tensor v_253_pad_type_0 = const()[name = tensor("v_253_pad_type_0"), val = tensor("custom")]; + tensor v_253_pad_0 = const()[name = tensor("v_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1802843136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1804072000))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_253_cast_fp16 = conv(dilations = var_12376, groups = var_6895, pad = v_253_pad_0, pad_type = v_253_pad_type_0, strides = var_12374, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_489_cast_fp16)[name = tensor("v_253_cast_fp16")]; + tensor var_12380 = const()[name = tensor("op_12380"), val = tensor([1, 20, 64, -1])]; + tensor var_12381_cast_fp16 = reshape(shape = var_12380, x = q_253_cast_fp16)[name = tensor("op_12381_cast_fp16")]; + tensor var_12382 = const()[name = tensor("op_12382"), val = tensor([1, 20, 64, -1])]; + tensor var_12383_cast_fp16 = reshape(shape = var_12382, x = k_253_cast_fp16)[name = tensor("op_12383_cast_fp16")]; + tensor var_12384 = const()[name = tensor("op_12384"), val = tensor([1, 20, 64, -1])]; + tensor var_12385_cast_fp16 = reshape(shape = var_12384, x = v_253_cast_fp16)[name = tensor("op_12385_cast_fp16")]; + tensor attn_weights_505_transpose_x_0 = const()[name = tensor("attn_weights_505_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_505_transpose_y_0 = const()[name = tensor("attn_weights_505_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_505_cast_fp16 = matmul(transpose_x = attn_weights_505_transpose_x_0, transpose_y = attn_weights_505_transpose_y_0, x = var_12381_cast_fp16, y = var_12383_cast_fp16)[name = tensor("attn_weights_505_cast_fp16")]; + tensor attn_weights_507_cast_fp16 = mul(x = attn_weights_505_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_507_cast_fp16")]; + tensor var_12389_cast_fp16 = softmax(axis = var_6879, x = attn_weights_507_cast_fp16)[name = tensor("op_12389_cast_fp16")]; + tensor attn_253_transpose_x_0 = const()[name = tensor("attn_253_transpose_x_0"), val = tensor(false)]; + tensor attn_253_transpose_y_0 = const()[name = tensor("attn_253_transpose_y_0"), val = tensor(true)]; + tensor attn_253_cast_fp16 = matmul(transpose_x = attn_253_transpose_x_0, transpose_y = attn_253_transpose_y_0, x = var_12385_cast_fp16, y = var_12389_cast_fp16)[name = tensor("attn_253_cast_fp16")]; + tensor var_12393 = const()[name = tensor("op_12393"), val = tensor([1, 1280, 1, -1])]; + tensor input_709_cast_fp16 = reshape(shape = var_12393, x = attn_253_cast_fp16)[name = tensor("input_709_cast_fp16")]; + tensor var_12398 = const()[name = tensor("op_12398"), val = tensor([1, 1])]; + tensor var_12400 = const()[name = tensor("op_12400"), val = tensor([1, 1])]; + tensor var_12402_pad_type_0 = const()[name = tensor("op_12402_pad_type_0"), val = tensor("custom")]; + tensor var_12402_pad_0 = const()[name = tensor("op_12402_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1804072192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805301056))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805301248)))]; + tensor var_12402_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_12400, groups = var_6895, pad = var_12402_pad_0, pad_type = var_12402_pad_type_0, strides = var_12398, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_709_cast_fp16)[name = tensor("op_12402_cast_fp16")]; + tensor inputs_381_cast_fp16 = add(x = var_12402_cast_fp16, y = inputs_379_cast_fp16)[name = tensor("inputs_381_cast_fp16")]; + tensor hidden_states_491_axes_0 = const()[name = tensor("hidden_states_491_axes_0"), val = tensor([1])]; + tensor hidden_states_491_gamma_0_to_fp16 = const()[name = tensor("hidden_states_491_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805303872)))]; + tensor hidden_states_491_beta_0_to_fp16 = const()[name = tensor("hidden_states_491_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805306496)))]; + tensor var_12412_to_fp16 = const()[name = tensor("op_12412_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_491_cast_fp16 = layer_norm(axes = hidden_states_491_axes_0, beta = hidden_states_491_beta_0_to_fp16, epsilon = var_12412_to_fp16, gamma = hidden_states_491_gamma_0_to_fp16, x = inputs_381_cast_fp16)[name = tensor("hidden_states_491_cast_fp16")]; + tensor var_12427 = const()[name = tensor("op_12427"), val = tensor([1, 1])]; + tensor var_12429 = const()[name = tensor("op_12429"), val = tensor([1, 1])]; + tensor q_255_pad_type_0 = const()[name = tensor("q_255_pad_type_0"), val = tensor("custom")]; + tensor q_255_pad_0 = const()[name = tensor("q_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805309120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1806537984))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_255_cast_fp16 = conv(dilations = var_12429, groups = var_6895, pad = q_255_pad_0, pad_type = q_255_pad_type_0, strides = var_12427, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_491_cast_fp16)[name = tensor("q_255_cast_fp16")]; + tensor var_12433 = const()[name = tensor("op_12433"), val = tensor([1, 1])]; + tensor var_12435 = const()[name = tensor("op_12435"), val = tensor([1, 1])]; + tensor k_255_pad_type_0 = const()[name = tensor("k_255_pad_type_0"), val = tensor("custom")]; + tensor k_255_pad_0 = const()[name = tensor("k_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1806538176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808504320))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_255_cast_fp16 = conv(dilations = var_12435, groups = var_6895, pad = k_255_pad_0, pad_type = k_255_pad_type_0, strides = var_12433, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_255_cast_fp16")]; + tensor var_12439 = const()[name = tensor("op_12439"), val = tensor([1, 1])]; + tensor var_12441 = const()[name = tensor("op_12441"), val = tensor([1, 1])]; + tensor v_255_pad_type_0 = const()[name = tensor("v_255_pad_type_0"), val = tensor("custom")]; + tensor v_255_pad_0 = const()[name = tensor("v_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808504512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810470656))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_255_cast_fp16 = conv(dilations = var_12441, groups = var_6895, pad = v_255_pad_0, pad_type = v_255_pad_type_0, strides = var_12439, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_255_cast_fp16")]; + tensor var_12445 = const()[name = tensor("op_12445"), val = tensor([1, 20, 64, -1])]; + tensor var_12446_cast_fp16 = reshape(shape = var_12445, x = q_255_cast_fp16)[name = tensor("op_12446_cast_fp16")]; + tensor var_12447 = const()[name = tensor("op_12447"), val = tensor([1, 20, 64, -1])]; + tensor var_12448_cast_fp16 = reshape(shape = var_12447, x = k_255_cast_fp16)[name = tensor("op_12448_cast_fp16")]; + tensor var_12449 = const()[name = tensor("op_12449"), val = tensor([1, 20, 64, -1])]; + tensor var_12450_cast_fp16 = reshape(shape = var_12449, x = v_255_cast_fp16)[name = tensor("op_12450_cast_fp16")]; + tensor attn_weights_509_transpose_x_0 = const()[name = tensor("attn_weights_509_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_509_transpose_y_0 = const()[name = tensor("attn_weights_509_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_509_cast_fp16 = matmul(transpose_x = attn_weights_509_transpose_x_0, transpose_y = attn_weights_509_transpose_y_0, x = var_12446_cast_fp16, y = var_12448_cast_fp16)[name = tensor("attn_weights_509_cast_fp16")]; + tensor attn_weights_511_cast_fp16 = mul(x = attn_weights_509_cast_fp16, y = var_6886_to_fp16)[name = tensor("attn_weights_511_cast_fp16")]; + tensor var_12454_cast_fp16 = softmax(axis = var_6879, x = attn_weights_511_cast_fp16)[name = tensor("op_12454_cast_fp16")]; + tensor attn_255_transpose_x_0 = const()[name = tensor("attn_255_transpose_x_0"), val = tensor(false)]; + tensor attn_255_transpose_y_0 = const()[name = tensor("attn_255_transpose_y_0"), val = tensor(true)]; + tensor attn_255_cast_fp16 = matmul(transpose_x = attn_255_transpose_x_0, transpose_y = attn_255_transpose_y_0, x = var_12450_cast_fp16, y = var_12454_cast_fp16)[name = tensor("attn_255_cast_fp16")]; + tensor var_12458 = const()[name = tensor("op_12458"), val = tensor([1, 1280, 1, -1])]; + tensor input_711_cast_fp16 = reshape(shape = var_12458, x = attn_255_cast_fp16)[name = tensor("input_711_cast_fp16")]; + tensor var_12463 = const()[name = tensor("op_12463"), val = tensor([1, 1])]; + tensor var_12465 = const()[name = tensor("op_12465"), val = tensor([1, 1])]; + tensor var_12467_pad_type_0 = const()[name = tensor("op_12467_pad_type_0"), val = tensor("custom")]; + tensor var_12467_pad_0 = const()[name = tensor("op_12467_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810470848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811699712))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811699904)))]; + tensor var_12467_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_12465, groups = var_6895, pad = var_12467_pad_0, pad_type = var_12467_pad_type_0, strides = var_12463, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_711_cast_fp16)[name = tensor("op_12467_cast_fp16")]; + tensor inputs_383_cast_fp16 = add(x = var_12467_cast_fp16, y = inputs_381_cast_fp16)[name = tensor("inputs_383_cast_fp16")]; + tensor input_713_axes_0 = const()[name = tensor("input_713_axes_0"), val = tensor([1])]; + tensor input_713_gamma_0_to_fp16 = const()[name = tensor("input_713_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811702528)))]; + tensor input_713_beta_0_to_fp16 = const()[name = tensor("input_713_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811705152)))]; + tensor var_12477_to_fp16 = const()[name = tensor("op_12477_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_713_cast_fp16 = layer_norm(axes = input_713_axes_0, beta = input_713_beta_0_to_fp16, epsilon = var_12477_to_fp16, gamma = input_713_gamma_0_to_fp16, x = inputs_383_cast_fp16)[name = tensor("input_713_cast_fp16")]; + tensor var_12493 = const()[name = tensor("op_12493"), val = tensor([1, 1])]; + tensor var_12495 = const()[name = tensor("op_12495"), val = tensor([1, 1])]; + tensor var_12497_pad_type_0 = const()[name = tensor("op_12497_pad_type_0"), val = tensor("custom")]; + tensor var_12497_pad_0 = const()[name = tensor("op_12497_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811707776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821538240))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821538432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821546176))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_12497_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12495, groups = var_6895, pad = var_12497_pad_0, pad_type = var_12497_pad_type_0, strides = var_12493, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_713_cast_fp16)[name = tensor("op_12497_cast_fp16")]; + tensor var_12498_split_sizes_0 = const()[name = tensor("op_12498_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12498_axis_0 = const()[name = tensor("op_12498_axis_0"), val = tensor(1)]; + tensor var_12498_cast_fp16_0, tensor var_12498_cast_fp16_1 = split(axis = var_12498_axis_0, split_sizes = var_12498_split_sizes_0, x = var_12497_cast_fp16)[name = tensor("op_12498_cast_fp16")]; + tensor var_12500_mode_0 = const()[name = tensor("op_12500_mode_0"), val = tensor("EXACT")]; + tensor var_12500_cast_fp16 = gelu(mode = var_12500_mode_0, x = var_12498_cast_fp16_1)[name = tensor("op_12500_cast_fp16")]; + tensor input_715_cast_fp16 = mul(x = var_12498_cast_fp16_0, y = var_12500_cast_fp16)[name = tensor("input_715_cast_fp16")]; + tensor var_12504 = const()[name = tensor("op_12504"), val = tensor([1, 1])]; + tensor var_12506 = const()[name = tensor("op_12506"), val = tensor([1, 1])]; + tensor var_12508_pad_type_0 = const()[name = tensor("op_12508_pad_type_0"), val = tensor("custom")]; + tensor var_12508_pad_0 = const()[name = tensor("op_12508_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821546368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1826461632))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1826461824)))]; + tensor var_12508_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_12506, groups = var_6895, pad = var_12508_pad_0, pad_type = var_12508_pad_type_0, strides = var_12504, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = tensor("op_12508_cast_fp16")]; + tensor hidden_states_495_cast_fp16 = add(x = var_12508_cast_fp16, y = inputs_383_cast_fp16)[name = tensor("hidden_states_495_cast_fp16")]; + tensor var_12510 = const()[name = tensor("op_12510"), val = tensor([1, 1280, 32, 32])]; + tensor input_717_cast_fp16 = reshape(shape = var_12510, x = hidden_states_495_cast_fp16)[name = tensor("input_717_cast_fp16")]; + tensor var_12514 = const()[name = tensor("op_12514"), val = tensor([1, 1])]; + tensor var_12516 = const()[name = tensor("op_12516"), val = tensor([1, 1])]; + tensor hidden_states_497_pad_type_0 = const()[name = tensor("hidden_states_497_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_497_pad_0 = const()[name = tensor("hidden_states_497_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1826464448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1827693312))), name = tensor("up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1827693504)))]; + tensor hidden_states_497_cast_fp16 = conv(bias = up_blocks_0_attentions_2_proj_out_bias_to_fp16, dilations = var_12516, groups = var_6895, pad = hidden_states_497_pad_0, pad_type = hidden_states_497_pad_type_0, strides = var_12514, weight = up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized, x = input_717_cast_fp16)[name = tensor("hidden_states_497_cast_fp16")]; + tensor input_719_cast_fp16 = add(x = hidden_states_497_cast_fp16, y = hidden_states_431_cast_fp16)[name = tensor("input_719_cast_fp16")]; + tensor input_721_scale_factor_height_0 = const()[name = tensor("input_721_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_721_scale_factor_width_0 = const()[name = tensor("input_721_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_721_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_721_scale_factor_height_0, scale_factor_width = input_721_scale_factor_width_0, x = input_719_cast_fp16)[name = tensor("input_721_cast_fp16")]; + tensor var_12525 = const()[name = tensor("op_12525"), val = tensor([1, 1])]; + tensor var_12527 = const()[name = tensor("op_12527"), val = tensor([1, 1])]; + tensor hidden_states_499_pad_type_0 = const()[name = tensor("hidden_states_499_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_499_pad_0 = const()[name = tensor("hidden_states_499_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(1827696128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1838755392))), 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(1838755584)))]; + tensor hidden_states_499_cast_fp16 = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_12527, groups = var_6895, pad = hidden_states_499_pad_0, pad_type = hidden_states_499_pad_type_0, strides = var_12525, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized, x = input_721_cast_fp16)[name = tensor("hidden_states_499_cast_fp16")]; + tensor var_12532 = const()[name = tensor("op_12532"), val = tensor(3)]; + tensor var_12548 = const()[name = tensor("op_12548"), val = tensor(1)]; + tensor input_723_interleave_0 = const()[name = tensor("input_723_interleave_0"), val = tensor(false)]; + tensor input_723_cast_fp16 = concat(axis = var_12548, interleave = input_723_interleave_0, values = (hidden_states_499_cast_fp16, res_hidden_states_7_cast_fp16))[name = tensor("input_723_cast_fp16")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([1, 32, 60, 64, 64])]; + tensor reshape_120_cast_fp16 = reshape(shape = reshape_120_shape_0, x = input_723_cast_fp16)[name = tensor("reshape_120_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast_fp16)[name = tensor("reduce_mean_90_cast_fp16")]; + tensor sub_60_cast_fp16 = sub(x = reshape_120_cast_fp16, y = reduce_mean_90_cast_fp16)[name = tensor("sub_60_cast_fp16")]; + tensor square_30_cast_fp16 = square(x = sub_60_cast_fp16)[name = tensor("square_30_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast_fp16)[name = tensor("reduce_mean_92_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_92_cast_fp16, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast_fp16")]; + tensor sqrt_30_cast_fp16 = sqrt(x = add_60_cast_fp16)[name = tensor("sqrt_30_cast_fp16")]; + tensor real_div_30_cast_fp16 = real_div(x = sub_60_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor("real_div_30_cast_fp16")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([1, 1920, 64, 64])]; + tensor reshape_121_cast_fp16 = reshape(shape = reshape_121_shape_0, x = real_div_30_cast_fp16)[name = tensor("reshape_121_cast_fp16")]; + 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(1838758208)))]; + 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(1838762112)))]; + 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_fp16 = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_121_cast_fp16)[name = tensor("add_61_cast_fp16")]; + tensor input_727_cast_fp16 = silu(x = add_61_cast_fp16)[name = tensor("input_727_cast_fp16")]; + tensor var_12577 = const()[name = tensor("op_12577"), val = tensor([1, 1])]; + tensor var_12579 = const()[name = tensor("op_12579"), val = tensor([1, 1])]; + tensor hidden_states_501_pad_type_0 = const()[name = tensor("hidden_states_501_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_501_pad_0 = const()[name = tensor("hidden_states_501_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(1838766016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847060480))), name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1920, 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(1847060672)))]; + tensor hidden_states_501_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_12579, groups = var_12548, pad = hidden_states_501_pad_0, pad_type = hidden_states_501_pad_type_0, strides = var_12577, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_727_cast_fp16)[name = tensor("hidden_states_501_cast_fp16")]; + tensor var_12585 = const()[name = tensor("op_12585"), val = tensor([1, 1])]; + tensor var_12587 = const()[name = tensor("op_12587"), 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_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(1847062016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847676480))), name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 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(1847676672)))]; + tensor temb_23_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_12587, groups = var_12548, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_12585, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_23_cast_fp16")]; + tensor input_731_cast_fp16 = add(x = hidden_states_501_cast_fp16, y = temb_23_cast_fp16)[name = tensor("input_731_cast_fp16")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_124_cast_fp16 = reshape(shape = reshape_124_shape_0, x = input_731_cast_fp16)[name = tensor("reshape_124_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast_fp16)[name = tensor("reduce_mean_93_cast_fp16")]; + tensor sub_62_cast_fp16 = sub(x = reshape_124_cast_fp16, y = reduce_mean_93_cast_fp16)[name = tensor("sub_62_cast_fp16")]; + tensor square_31_cast_fp16 = square(x = sub_62_cast_fp16)[name = tensor("square_31_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast_fp16)[name = tensor("reduce_mean_95_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_95_cast_fp16, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast_fp16")]; + tensor sqrt_31_cast_fp16 = sqrt(x = add_62_cast_fp16)[name = tensor("sqrt_31_cast_fp16")]; + tensor real_div_31_cast_fp16 = real_div(x = sub_62_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor("real_div_31_cast_fp16")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_125_cast_fp16 = reshape(shape = reshape_125_shape_0, x = real_div_31_cast_fp16)[name = tensor("reshape_125_cast_fp16")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847678016)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847679360)))]; + 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_fp16 = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_125_cast_fp16)[name = tensor("add_63_cast_fp16")]; + tensor input_735_cast_fp16 = silu(x = add_63_cast_fp16)[name = tensor("input_735_cast_fp16")]; + tensor var_12597 = const()[name = tensor("op_12597"), val = tensor([1, 1])]; + tensor var_12599 = const()[name = tensor("op_12599"), val = tensor([1, 1])]; + tensor hidden_states_503_pad_type_0 = const()[name = tensor("hidden_states_503_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_503_pad_0 = const()[name = tensor("hidden_states_503_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(1847680704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1850445568))), name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1850445760)))]; + tensor hidden_states_503_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_12599, groups = var_12548, pad = hidden_states_503_pad_0, pad_type = hidden_states_503_pad_type_0, strides = var_12597, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_735_cast_fp16)[name = tensor("hidden_states_503_cast_fp16")]; + tensor var_12604 = const()[name = tensor("op_12604"), val = tensor([1, 1])]; + tensor var_12606 = const()[name = tensor("op_12606"), 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(1850447104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851368768))), name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1920, 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(1851368960)))]; + tensor x_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_12606, groups = var_12548, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_12604, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_723_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor hidden_states_505_cast_fp16 = add(x = x_11_cast_fp16, y = hidden_states_503_cast_fp16)[name = tensor("hidden_states_505_cast_fp16")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_128_cast_fp16 = reshape(shape = reshape_128_shape_0, x = hidden_states_505_cast_fp16)[name = tensor("reshape_128_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast_fp16)[name = tensor("reduce_mean_96_cast_fp16")]; + tensor sub_64_cast_fp16 = sub(x = reshape_128_cast_fp16, y = reduce_mean_96_cast_fp16)[name = tensor("sub_64_cast_fp16")]; + tensor square_32_cast_fp16 = square(x = sub_64_cast_fp16)[name = tensor("square_32_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast_fp16)[name = tensor("reduce_mean_98_cast_fp16")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_64_cast_fp16 = add(x = reduce_mean_98_cast_fp16, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast_fp16")]; + tensor sqrt_32_cast_fp16 = sqrt(x = add_64_cast_fp16)[name = tensor("sqrt_32_cast_fp16")]; + tensor real_div_32_cast_fp16 = real_div(x = sub_64_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor("real_div_32_cast_fp16")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_129_cast_fp16 = reshape(shape = reshape_129_shape_0, x = real_div_32_cast_fp16)[name = tensor("reshape_129_cast_fp16")]; + 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(1851370304)))]; + 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(1851371648)))]; + 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_fp16 = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_129_cast_fp16)[name = tensor("add_65_cast_fp16")]; + tensor var_12628 = const()[name = tensor("op_12628"), val = tensor([1, 1])]; + tensor var_12630 = const()[name = tensor("op_12630"), val = tensor([1, 1])]; + tensor hidden_states_507_pad_type_0 = const()[name = tensor("hidden_states_507_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_507_pad_0 = const()[name = tensor("hidden_states_507_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(1851372992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851680256))), name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1851680448)))]; + tensor hidden_states_507_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_12630, groups = var_12548, pad = hidden_states_507_pad_0, pad_type = hidden_states_507_pad_type_0, strides = var_12628, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_65_cast_fp16)[name = tensor("hidden_states_507_cast_fp16")]; + tensor var_12635 = const()[name = tensor("op_12635"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_385_cast_fp16 = reshape(shape = var_12635, x = hidden_states_507_cast_fp16)[name = tensor("inputs_385_cast_fp16")]; + tensor hidden_states_509_axes_0 = const()[name = tensor("hidden_states_509_axes_0"), val = tensor([1])]; + tensor hidden_states_509_gamma_0_to_fp16 = const()[name = tensor("hidden_states_509_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851681792)))]; + tensor hidden_states_509_beta_0_to_fp16 = const()[name = tensor("hidden_states_509_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851683136)))]; + tensor var_12651_to_fp16 = const()[name = tensor("op_12651_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_509_cast_fp16 = layer_norm(axes = hidden_states_509_axes_0, beta = hidden_states_509_beta_0_to_fp16, epsilon = var_12651_to_fp16, gamma = hidden_states_509_gamma_0_to_fp16, x = inputs_385_cast_fp16)[name = tensor("hidden_states_509_cast_fp16")]; + tensor var_12666 = const()[name = tensor("op_12666"), val = tensor([1, 1])]; + tensor var_12668 = const()[name = tensor("op_12668"), val = tensor([1, 1])]; + tensor q_257_pad_type_0 = const()[name = tensor("q_257_pad_type_0"), val = tensor("custom")]; + tensor q_257_pad_0 = const()[name = tensor("q_257_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(1851684480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851991744))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_257_cast_fp16 = conv(dilations = var_12668, groups = var_12548, pad = q_257_pad_0, pad_type = q_257_pad_type_0, strides = var_12666, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_509_cast_fp16)[name = tensor("q_257_cast_fp16")]; + tensor var_12672 = const()[name = tensor("op_12672"), val = tensor([1, 1])]; + tensor var_12674 = const()[name = tensor("op_12674"), val = tensor([1, 1])]; + tensor k_257_pad_type_0 = const()[name = tensor("k_257_pad_type_0"), val = tensor("custom")]; + tensor k_257_pad_0 = const()[name = tensor("k_257_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(1851991936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852299200))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_257_cast_fp16 = conv(dilations = var_12674, groups = var_12548, pad = k_257_pad_0, pad_type = k_257_pad_type_0, strides = var_12672, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_509_cast_fp16)[name = tensor("k_257_cast_fp16")]; + tensor var_12678 = const()[name = tensor("op_12678"), val = tensor([1, 1])]; + tensor var_12680 = const()[name = tensor("op_12680"), val = tensor([1, 1])]; + tensor v_257_pad_type_0 = const()[name = tensor("v_257_pad_type_0"), val = tensor("custom")]; + tensor v_257_pad_0 = const()[name = tensor("v_257_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(1852299392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852606656))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_257_cast_fp16 = conv(dilations = var_12680, groups = var_12548, pad = v_257_pad_0, pad_type = v_257_pad_type_0, strides = var_12678, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_509_cast_fp16)[name = tensor("v_257_cast_fp16")]; + tensor var_12684 = const()[name = tensor("op_12684"), val = tensor([1, 10, 64, -1])]; + tensor var_12685_cast_fp16 = reshape(shape = var_12684, x = q_257_cast_fp16)[name = tensor("op_12685_cast_fp16")]; + tensor var_12686 = const()[name = tensor("op_12686"), val = tensor([1, 10, 64, -1])]; + tensor var_12687_cast_fp16 = reshape(shape = var_12686, x = k_257_cast_fp16)[name = tensor("op_12687_cast_fp16")]; + tensor var_12688 = const()[name = tensor("op_12688"), val = tensor([1, 10, 64, -1])]; + tensor var_12689_cast_fp16 = reshape(shape = var_12688, x = v_257_cast_fp16)[name = tensor("op_12689_cast_fp16")]; + tensor attn_weights_513_transpose_x_0 = const()[name = tensor("attn_weights_513_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_513_transpose_y_0 = const()[name = tensor("attn_weights_513_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_513_cast_fp16 = matmul(transpose_x = attn_weights_513_transpose_x_0, transpose_y = attn_weights_513_transpose_y_0, x = var_12685_cast_fp16, y = var_12687_cast_fp16)[name = tensor("attn_weights_513_cast_fp16")]; + tensor var_12539_to_fp16 = const()[name = tensor("op_12539_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_515_cast_fp16 = mul(x = attn_weights_513_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_515_cast_fp16")]; + tensor var_12693_cast_fp16 = softmax(axis = var_12532, x = attn_weights_515_cast_fp16)[name = tensor("op_12693_cast_fp16")]; + tensor attn_257_transpose_x_0 = const()[name = tensor("attn_257_transpose_x_0"), val = tensor(false)]; + tensor attn_257_transpose_y_0 = const()[name = tensor("attn_257_transpose_y_0"), val = tensor(true)]; + tensor attn_257_cast_fp16 = matmul(transpose_x = attn_257_transpose_x_0, transpose_y = attn_257_transpose_y_0, x = var_12689_cast_fp16, y = var_12693_cast_fp16)[name = tensor("attn_257_cast_fp16")]; + tensor var_12697 = const()[name = tensor("op_12697"), val = tensor([1, 640, 1, -1])]; + tensor input_739_cast_fp16 = reshape(shape = var_12697, x = attn_257_cast_fp16)[name = tensor("input_739_cast_fp16")]; + tensor var_12702 = const()[name = tensor("op_12702"), val = tensor([1, 1])]; + tensor var_12704 = const()[name = tensor("op_12704"), val = tensor([1, 1])]; + tensor var_12706_pad_type_0 = const()[name = tensor("op_12706_pad_type_0"), val = tensor("custom")]; + tensor var_12706_pad_0 = const()[name = tensor("op_12706_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(1852606848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852914112))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1852914304)))]; + tensor var_12706_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_12704, groups = var_12548, pad = var_12706_pad_0, pad_type = var_12706_pad_type_0, strides = var_12702, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_739_cast_fp16)[name = tensor("op_12706_cast_fp16")]; + tensor inputs_387_cast_fp16 = add(x = var_12706_cast_fp16, y = inputs_385_cast_fp16)[name = tensor("inputs_387_cast_fp16")]; + tensor hidden_states_511_axes_0 = const()[name = tensor("hidden_states_511_axes_0"), val = tensor([1])]; + tensor hidden_states_511_gamma_0_to_fp16 = const()[name = tensor("hidden_states_511_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852915648)))]; + tensor hidden_states_511_beta_0_to_fp16 = const()[name = tensor("hidden_states_511_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852916992)))]; + tensor var_12716_to_fp16 = const()[name = tensor("op_12716_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_511_cast_fp16 = layer_norm(axes = hidden_states_511_axes_0, beta = hidden_states_511_beta_0_to_fp16, epsilon = var_12716_to_fp16, gamma = hidden_states_511_gamma_0_to_fp16, x = inputs_387_cast_fp16)[name = tensor("hidden_states_511_cast_fp16")]; + tensor var_12731 = const()[name = tensor("op_12731"), val = tensor([1, 1])]; + tensor var_12733 = const()[name = tensor("op_12733"), val = tensor([1, 1])]; + tensor q_259_pad_type_0 = const()[name = tensor("q_259_pad_type_0"), val = tensor("custom")]; + tensor q_259_pad_0 = const()[name = tensor("q_259_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(1852918336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1853225600))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_259_cast_fp16 = conv(dilations = var_12733, groups = var_12548, pad = q_259_pad_0, pad_type = q_259_pad_type_0, strides = var_12731, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_511_cast_fp16)[name = tensor("q_259_cast_fp16")]; + tensor var_12737 = const()[name = tensor("op_12737"), val = tensor([1, 1])]; + tensor var_12739 = const()[name = tensor("op_12739"), val = tensor([1, 1])]; + tensor k_259_pad_type_0 = const()[name = tensor("k_259_pad_type_0"), val = tensor("custom")]; + tensor k_259_pad_0 = const()[name = tensor("k_259_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(1853225792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1854208896))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_259_cast_fp16 = conv(dilations = var_12739, groups = var_12548, pad = k_259_pad_0, pad_type = k_259_pad_type_0, strides = var_12737, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_259_cast_fp16")]; + tensor var_12743 = const()[name = tensor("op_12743"), val = tensor([1, 1])]; + tensor var_12745 = const()[name = tensor("op_12745"), val = tensor([1, 1])]; + tensor v_259_pad_type_0 = const()[name = tensor("v_259_pad_type_0"), val = tensor("custom")]; + tensor v_259_pad_0 = const()[name = tensor("v_259_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(1854209088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855192192))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_259_cast_fp16 = conv(dilations = var_12745, groups = var_12548, pad = v_259_pad_0, pad_type = v_259_pad_type_0, strides = var_12743, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_259_cast_fp16")]; + tensor var_12749 = const()[name = tensor("op_12749"), val = tensor([1, 10, 64, -1])]; + tensor var_12750_cast_fp16 = reshape(shape = var_12749, x = q_259_cast_fp16)[name = tensor("op_12750_cast_fp16")]; + tensor var_12751 = const()[name = tensor("op_12751"), val = tensor([1, 10, 64, -1])]; + tensor var_12752_cast_fp16 = reshape(shape = var_12751, x = k_259_cast_fp16)[name = tensor("op_12752_cast_fp16")]; + tensor var_12753 = const()[name = tensor("op_12753"), val = tensor([1, 10, 64, -1])]; + tensor var_12754_cast_fp16 = reshape(shape = var_12753, x = v_259_cast_fp16)[name = tensor("op_12754_cast_fp16")]; + tensor attn_weights_517_transpose_x_0 = const()[name = tensor("attn_weights_517_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_517_transpose_y_0 = const()[name = tensor("attn_weights_517_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_517_cast_fp16 = matmul(transpose_x = attn_weights_517_transpose_x_0, transpose_y = attn_weights_517_transpose_y_0, x = var_12750_cast_fp16, y = var_12752_cast_fp16)[name = tensor("attn_weights_517_cast_fp16")]; + tensor attn_weights_519_cast_fp16 = mul(x = attn_weights_517_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_519_cast_fp16")]; + tensor var_12758_cast_fp16 = softmax(axis = var_12532, x = attn_weights_519_cast_fp16)[name = tensor("op_12758_cast_fp16")]; + tensor attn_259_transpose_x_0 = const()[name = tensor("attn_259_transpose_x_0"), val = tensor(false)]; + tensor attn_259_transpose_y_0 = const()[name = tensor("attn_259_transpose_y_0"), val = tensor(true)]; + tensor attn_259_cast_fp16 = matmul(transpose_x = attn_259_transpose_x_0, transpose_y = attn_259_transpose_y_0, x = var_12754_cast_fp16, y = var_12758_cast_fp16)[name = tensor("attn_259_cast_fp16")]; + tensor var_12762 = const()[name = tensor("op_12762"), val = tensor([1, 640, 1, -1])]; + tensor input_741_cast_fp16 = reshape(shape = var_12762, x = attn_259_cast_fp16)[name = tensor("input_741_cast_fp16")]; + tensor var_12767 = const()[name = tensor("op_12767"), val = tensor([1, 1])]; + tensor var_12769 = const()[name = tensor("op_12769"), val = tensor([1, 1])]; + tensor var_12771_pad_type_0 = const()[name = tensor("op_12771_pad_type_0"), val = tensor("custom")]; + tensor var_12771_pad_0 = const()[name = tensor("op_12771_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(1855192384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855499648))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1855499840)))]; + tensor var_12771_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_12769, groups = var_12548, pad = var_12771_pad_0, pad_type = var_12771_pad_type_0, strides = var_12767, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_741_cast_fp16)[name = tensor("op_12771_cast_fp16")]; + tensor inputs_389_cast_fp16 = add(x = var_12771_cast_fp16, y = inputs_387_cast_fp16)[name = tensor("inputs_389_cast_fp16")]; + tensor input_743_axes_0 = const()[name = tensor("input_743_axes_0"), val = tensor([1])]; + tensor input_743_gamma_0_to_fp16 = const()[name = tensor("input_743_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855501184)))]; + tensor input_743_beta_0_to_fp16 = const()[name = tensor("input_743_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855502528)))]; + tensor var_12781_to_fp16 = const()[name = tensor("op_12781_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_743_cast_fp16 = layer_norm(axes = input_743_axes_0, beta = input_743_beta_0_to_fp16, epsilon = var_12781_to_fp16, gamma = input_743_gamma_0_to_fp16, x = inputs_389_cast_fp16)[name = tensor("input_743_cast_fp16")]; + tensor var_12797 = const()[name = tensor("op_12797"), val = tensor([1, 1])]; + tensor var_12799 = const()[name = tensor("op_12799"), val = tensor([1, 1])]; + tensor var_12801_pad_type_0 = const()[name = tensor("op_12801_pad_type_0"), val = tensor("custom")]; + tensor var_12801_pad_0 = const()[name = tensor("op_12801_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(1855503872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1857961536))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 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(1857961728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1857965632))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_12801_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12799, groups = var_12548, pad = var_12801_pad_0, pad_type = var_12801_pad_type_0, strides = var_12797, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_743_cast_fp16)[name = tensor("op_12801_cast_fp16")]; + tensor var_12802_split_sizes_0 = const()[name = tensor("op_12802_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12802_axis_0 = const()[name = tensor("op_12802_axis_0"), val = tensor(1)]; + tensor var_12802_cast_fp16_0, tensor var_12802_cast_fp16_1 = split(axis = var_12802_axis_0, split_sizes = var_12802_split_sizes_0, x = var_12801_cast_fp16)[name = tensor("op_12802_cast_fp16")]; + tensor var_12804_mode_0 = const()[name = tensor("op_12804_mode_0"), val = tensor("EXACT")]; + tensor var_12804_cast_fp16 = gelu(mode = var_12804_mode_0, x = var_12802_cast_fp16_1)[name = tensor("op_12804_cast_fp16")]; + tensor input_745_cast_fp16 = mul(x = var_12802_cast_fp16_0, y = var_12804_cast_fp16)[name = tensor("input_745_cast_fp16")]; + tensor var_12808 = const()[name = tensor("op_12808"), val = tensor([1, 1])]; + tensor var_12810 = const()[name = tensor("op_12810"), val = tensor([1, 1])]; + tensor var_12812_pad_type_0 = const()[name = tensor("op_12812_pad_type_0"), val = tensor("custom")]; + tensor var_12812_pad_0 = const()[name = tensor("op_12812_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(1857965824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859194688))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 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(1859194880)))]; + tensor var_12812_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_12810, groups = var_12548, pad = var_12812_pad_0, pad_type = var_12812_pad_type_0, strides = var_12808, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_745_cast_fp16)[name = tensor("op_12812_cast_fp16")]; + tensor inputs_391_cast_fp16 = add(x = var_12812_cast_fp16, y = inputs_389_cast_fp16)[name = tensor("inputs_391_cast_fp16")]; + tensor hidden_states_515_axes_0 = const()[name = tensor("hidden_states_515_axes_0"), val = tensor([1])]; + tensor hidden_states_515_gamma_0_to_fp16 = const()[name = tensor("hidden_states_515_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859196224)))]; + tensor hidden_states_515_beta_0_to_fp16 = const()[name = tensor("hidden_states_515_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859197568)))]; + tensor var_12828_to_fp16 = const()[name = tensor("op_12828_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_515_cast_fp16 = layer_norm(axes = hidden_states_515_axes_0, beta = hidden_states_515_beta_0_to_fp16, epsilon = var_12828_to_fp16, gamma = hidden_states_515_gamma_0_to_fp16, x = inputs_391_cast_fp16)[name = tensor("hidden_states_515_cast_fp16")]; + tensor var_12843 = const()[name = tensor("op_12843"), val = tensor([1, 1])]; + tensor var_12845 = const()[name = tensor("op_12845"), val = tensor([1, 1])]; + tensor q_261_pad_type_0 = const()[name = tensor("q_261_pad_type_0"), val = tensor("custom")]; + tensor q_261_pad_0 = const()[name = tensor("q_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859198912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859506176))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_261_cast_fp16 = conv(dilations = var_12845, groups = var_12548, pad = q_261_pad_0, pad_type = q_261_pad_type_0, strides = var_12843, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_515_cast_fp16)[name = tensor("q_261_cast_fp16")]; + tensor var_12849 = const()[name = tensor("op_12849"), val = tensor([1, 1])]; + tensor var_12851 = const()[name = tensor("op_12851"), val = tensor([1, 1])]; + tensor k_261_pad_type_0 = const()[name = tensor("k_261_pad_type_0"), val = tensor("custom")]; + tensor k_261_pad_0 = const()[name = tensor("k_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859506368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859813632))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_261_cast_fp16 = conv(dilations = var_12851, groups = var_12548, pad = k_261_pad_0, pad_type = k_261_pad_type_0, strides = var_12849, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_515_cast_fp16)[name = tensor("k_261_cast_fp16")]; + tensor var_12855 = const()[name = tensor("op_12855"), val = tensor([1, 1])]; + tensor var_12857 = const()[name = tensor("op_12857"), val = tensor([1, 1])]; + tensor v_261_pad_type_0 = const()[name = tensor("v_261_pad_type_0"), val = tensor("custom")]; + tensor v_261_pad_0 = const()[name = tensor("v_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859813824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860121088))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_261_cast_fp16 = conv(dilations = var_12857, groups = var_12548, pad = v_261_pad_0, pad_type = v_261_pad_type_0, strides = var_12855, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_515_cast_fp16)[name = tensor("v_261_cast_fp16")]; + tensor var_12861 = const()[name = tensor("op_12861"), val = tensor([1, 10, 64, -1])]; + tensor var_12862_cast_fp16 = reshape(shape = var_12861, x = q_261_cast_fp16)[name = tensor("op_12862_cast_fp16")]; + tensor var_12863 = const()[name = tensor("op_12863"), val = tensor([1, 10, 64, -1])]; + tensor var_12864_cast_fp16 = reshape(shape = var_12863, x = k_261_cast_fp16)[name = tensor("op_12864_cast_fp16")]; + tensor var_12865 = const()[name = tensor("op_12865"), val = tensor([1, 10, 64, -1])]; + tensor var_12866_cast_fp16 = reshape(shape = var_12865, x = v_261_cast_fp16)[name = tensor("op_12866_cast_fp16")]; + tensor attn_weights_521_transpose_x_0 = const()[name = tensor("attn_weights_521_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_521_transpose_y_0 = const()[name = tensor("attn_weights_521_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_521_cast_fp16 = matmul(transpose_x = attn_weights_521_transpose_x_0, transpose_y = attn_weights_521_transpose_y_0, x = var_12862_cast_fp16, y = var_12864_cast_fp16)[name = tensor("attn_weights_521_cast_fp16")]; + tensor attn_weights_523_cast_fp16 = mul(x = attn_weights_521_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_523_cast_fp16")]; + tensor var_12870_cast_fp16 = softmax(axis = var_12532, x = attn_weights_523_cast_fp16)[name = tensor("op_12870_cast_fp16")]; + tensor attn_261_transpose_x_0 = const()[name = tensor("attn_261_transpose_x_0"), val = tensor(false)]; + tensor attn_261_transpose_y_0 = const()[name = tensor("attn_261_transpose_y_0"), val = tensor(true)]; + tensor attn_261_cast_fp16 = matmul(transpose_x = attn_261_transpose_x_0, transpose_y = attn_261_transpose_y_0, x = var_12866_cast_fp16, y = var_12870_cast_fp16)[name = tensor("attn_261_cast_fp16")]; + tensor var_12874 = const()[name = tensor("op_12874"), val = tensor([1, 640, 1, -1])]; + tensor input_747_cast_fp16 = reshape(shape = var_12874, x = attn_261_cast_fp16)[name = tensor("input_747_cast_fp16")]; + tensor var_12879 = const()[name = tensor("op_12879"), val = tensor([1, 1])]; + tensor var_12881 = const()[name = tensor("op_12881"), val = tensor([1, 1])]; + tensor var_12883_pad_type_0 = const()[name = tensor("op_12883_pad_type_0"), val = tensor("custom")]; + tensor var_12883_pad_0 = const()[name = tensor("op_12883_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860121280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860428544))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860428736)))]; + tensor var_12883_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_12881, groups = var_12548, pad = var_12883_pad_0, pad_type = var_12883_pad_type_0, strides = var_12879, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_747_cast_fp16)[name = tensor("op_12883_cast_fp16")]; + tensor inputs_393_cast_fp16 = add(x = var_12883_cast_fp16, y = inputs_391_cast_fp16)[name = tensor("inputs_393_cast_fp16")]; + tensor hidden_states_517_axes_0 = const()[name = tensor("hidden_states_517_axes_0"), val = tensor([1])]; + tensor hidden_states_517_gamma_0_to_fp16 = const()[name = tensor("hidden_states_517_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860430080)))]; + tensor hidden_states_517_beta_0_to_fp16 = const()[name = tensor("hidden_states_517_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860431424)))]; + tensor var_12893_to_fp16 = const()[name = tensor("op_12893_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_517_cast_fp16 = layer_norm(axes = hidden_states_517_axes_0, beta = hidden_states_517_beta_0_to_fp16, epsilon = var_12893_to_fp16, gamma = hidden_states_517_gamma_0_to_fp16, x = inputs_393_cast_fp16)[name = tensor("hidden_states_517_cast_fp16")]; + tensor var_12908 = const()[name = tensor("op_12908"), val = tensor([1, 1])]; + tensor var_12910 = const()[name = tensor("op_12910"), val = tensor([1, 1])]; + tensor q_263_pad_type_0 = const()[name = tensor("q_263_pad_type_0"), val = tensor("custom")]; + tensor q_263_pad_0 = const()[name = tensor("q_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860432768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860740032))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_263_cast_fp16 = conv(dilations = var_12910, groups = var_12548, pad = q_263_pad_0, pad_type = q_263_pad_type_0, strides = var_12908, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_517_cast_fp16)[name = tensor("q_263_cast_fp16")]; + tensor var_12914 = const()[name = tensor("op_12914"), val = tensor([1, 1])]; + tensor var_12916 = const()[name = tensor("op_12916"), val = tensor([1, 1])]; + tensor k_263_pad_type_0 = const()[name = tensor("k_263_pad_type_0"), val = tensor("custom")]; + tensor k_263_pad_0 = const()[name = tensor("k_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860740224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1861723328))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_263_cast_fp16 = conv(dilations = var_12916, groups = var_12548, pad = k_263_pad_0, pad_type = k_263_pad_type_0, strides = var_12914, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_263_cast_fp16")]; + tensor var_12920 = const()[name = tensor("op_12920"), val = tensor([1, 1])]; + tensor var_12922 = const()[name = tensor("op_12922"), val = tensor([1, 1])]; + tensor v_263_pad_type_0 = const()[name = tensor("v_263_pad_type_0"), val = tensor("custom")]; + tensor v_263_pad_0 = const()[name = tensor("v_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1861723520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1862706624))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_263_cast_fp16 = conv(dilations = var_12922, groups = var_12548, pad = v_263_pad_0, pad_type = v_263_pad_type_0, strides = var_12920, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_263_cast_fp16")]; + tensor var_12926 = const()[name = tensor("op_12926"), val = tensor([1, 10, 64, -1])]; + tensor var_12927_cast_fp16 = reshape(shape = var_12926, x = q_263_cast_fp16)[name = tensor("op_12927_cast_fp16")]; + tensor var_12928 = const()[name = tensor("op_12928"), val = tensor([1, 10, 64, -1])]; + tensor var_12929_cast_fp16 = reshape(shape = var_12928, x = k_263_cast_fp16)[name = tensor("op_12929_cast_fp16")]; + tensor var_12930 = const()[name = tensor("op_12930"), val = tensor([1, 10, 64, -1])]; + tensor var_12931_cast_fp16 = reshape(shape = var_12930, x = v_263_cast_fp16)[name = tensor("op_12931_cast_fp16")]; + tensor attn_weights_525_transpose_x_0 = const()[name = tensor("attn_weights_525_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_525_transpose_y_0 = const()[name = tensor("attn_weights_525_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_525_cast_fp16 = matmul(transpose_x = attn_weights_525_transpose_x_0, transpose_y = attn_weights_525_transpose_y_0, x = var_12927_cast_fp16, y = var_12929_cast_fp16)[name = tensor("attn_weights_525_cast_fp16")]; + tensor attn_weights_527_cast_fp16 = mul(x = attn_weights_525_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_527_cast_fp16")]; + tensor var_12935_cast_fp16 = softmax(axis = var_12532, x = attn_weights_527_cast_fp16)[name = tensor("op_12935_cast_fp16")]; + tensor attn_263_transpose_x_0 = const()[name = tensor("attn_263_transpose_x_0"), val = tensor(false)]; + tensor attn_263_transpose_y_0 = const()[name = tensor("attn_263_transpose_y_0"), val = tensor(true)]; + tensor attn_263_cast_fp16 = matmul(transpose_x = attn_263_transpose_x_0, transpose_y = attn_263_transpose_y_0, x = var_12931_cast_fp16, y = var_12935_cast_fp16)[name = tensor("attn_263_cast_fp16")]; + tensor var_12939 = const()[name = tensor("op_12939"), val = tensor([1, 640, 1, -1])]; + tensor input_749_cast_fp16 = reshape(shape = var_12939, x = attn_263_cast_fp16)[name = tensor("input_749_cast_fp16")]; + tensor var_12944 = const()[name = tensor("op_12944"), val = tensor([1, 1])]; + tensor var_12946 = const()[name = tensor("op_12946"), val = tensor([1, 1])]; + tensor var_12948_pad_type_0 = const()[name = tensor("op_12948_pad_type_0"), val = tensor("custom")]; + tensor var_12948_pad_0 = const()[name = tensor("op_12948_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1862706816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863014080))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863014272)))]; + tensor var_12948_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_12946, groups = var_12548, pad = var_12948_pad_0, pad_type = var_12948_pad_type_0, strides = var_12944, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_749_cast_fp16)[name = tensor("op_12948_cast_fp16")]; + tensor inputs_395_cast_fp16 = add(x = var_12948_cast_fp16, y = inputs_393_cast_fp16)[name = tensor("inputs_395_cast_fp16")]; + tensor input_751_axes_0 = const()[name = tensor("input_751_axes_0"), val = tensor([1])]; + tensor input_751_gamma_0_to_fp16 = const()[name = tensor("input_751_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863015616)))]; + tensor input_751_beta_0_to_fp16 = const()[name = tensor("input_751_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863016960)))]; + tensor var_12958_to_fp16 = const()[name = tensor("op_12958_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_751_cast_fp16 = layer_norm(axes = input_751_axes_0, beta = input_751_beta_0_to_fp16, epsilon = var_12958_to_fp16, gamma = input_751_gamma_0_to_fp16, x = inputs_395_cast_fp16)[name = tensor("input_751_cast_fp16")]; + tensor var_12974 = const()[name = tensor("op_12974"), val = tensor([1, 1])]; + tensor var_12976 = const()[name = tensor("op_12976"), val = tensor([1, 1])]; + tensor var_12978_pad_type_0 = const()[name = tensor("op_12978_pad_type_0"), val = tensor("custom")]; + tensor var_12978_pad_0 = const()[name = tensor("op_12978_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863018304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865475968))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865476160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865480064))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_12978_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12976, groups = var_12548, pad = var_12978_pad_0, pad_type = var_12978_pad_type_0, strides = var_12974, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_751_cast_fp16)[name = tensor("op_12978_cast_fp16")]; + tensor var_12979_split_sizes_0 = const()[name = tensor("op_12979_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12979_axis_0 = const()[name = tensor("op_12979_axis_0"), val = tensor(1)]; + tensor var_12979_cast_fp16_0, tensor var_12979_cast_fp16_1 = split(axis = var_12979_axis_0, split_sizes = var_12979_split_sizes_0, x = var_12978_cast_fp16)[name = tensor("op_12979_cast_fp16")]; + tensor var_12981_mode_0 = const()[name = tensor("op_12981_mode_0"), val = tensor("EXACT")]; + tensor var_12981_cast_fp16 = gelu(mode = var_12981_mode_0, x = var_12979_cast_fp16_1)[name = tensor("op_12981_cast_fp16")]; + tensor input_753_cast_fp16 = mul(x = var_12979_cast_fp16_0, y = var_12981_cast_fp16)[name = tensor("input_753_cast_fp16")]; + tensor var_12985 = const()[name = tensor("op_12985"), val = tensor([1, 1])]; + tensor var_12987 = const()[name = tensor("op_12987"), val = tensor([1, 1])]; + tensor var_12989_pad_type_0 = const()[name = tensor("op_12989_pad_type_0"), val = tensor("custom")]; + tensor var_12989_pad_0 = const()[name = tensor("op_12989_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865480256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1866709120))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1866709312)))]; + tensor var_12989_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_12987, groups = var_12548, pad = var_12989_pad_0, pad_type = var_12989_pad_type_0, strides = var_12985, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_753_cast_fp16)[name = tensor("op_12989_cast_fp16")]; + tensor hidden_states_521_cast_fp16 = add(x = var_12989_cast_fp16, y = inputs_395_cast_fp16)[name = tensor("hidden_states_521_cast_fp16")]; + tensor var_12991 = const()[name = tensor("op_12991"), val = tensor([1, 640, 64, 64])]; + tensor input_755_cast_fp16 = reshape(shape = var_12991, x = hidden_states_521_cast_fp16)[name = tensor("input_755_cast_fp16")]; + tensor var_12995 = const()[name = tensor("op_12995"), val = tensor([1, 1])]; + tensor var_12997 = const()[name = tensor("op_12997"), val = tensor([1, 1])]; + tensor hidden_states_523_pad_type_0 = const()[name = tensor("hidden_states_523_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_523_pad_0 = const()[name = tensor("hidden_states_523_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(1866710656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867017920))), name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1867018112)))]; + tensor hidden_states_523_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_12997, groups = var_12548, pad = hidden_states_523_pad_0, pad_type = hidden_states_523_pad_type_0, strides = var_12995, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_755_cast_fp16)[name = tensor("hidden_states_523_cast_fp16")]; + tensor hidden_states_525_cast_fp16 = add(x = hidden_states_523_cast_fp16, y = hidden_states_505_cast_fp16)[name = tensor("hidden_states_525_cast_fp16")]; + tensor input_757_interleave_0 = const()[name = tensor("input_757_interleave_0"), val = tensor(false)]; + tensor input_757_cast_fp16 = concat(axis = var_12548, interleave = input_757_interleave_0, values = (hidden_states_525_cast_fp16, res_hidden_states_9_cast_fp16))[name = tensor("input_757_cast_fp16")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([1, 32, 40, 64, 64])]; + tensor reshape_132_cast_fp16 = reshape(shape = reshape_132_shape_0, x = input_757_cast_fp16)[name = tensor("reshape_132_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast_fp16)[name = tensor("reduce_mean_99_cast_fp16")]; + tensor sub_66_cast_fp16 = sub(x = reshape_132_cast_fp16, y = reduce_mean_99_cast_fp16)[name = tensor("sub_66_cast_fp16")]; + tensor square_33_cast_fp16 = square(x = sub_66_cast_fp16)[name = tensor("square_33_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast_fp16)[name = tensor("reduce_mean_101_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_101_cast_fp16, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast_fp16")]; + tensor sqrt_33_cast_fp16 = sqrt(x = add_66_cast_fp16)[name = tensor("sqrt_33_cast_fp16")]; + tensor real_div_33_cast_fp16 = real_div(x = sub_66_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor("real_div_33_cast_fp16")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([1, 1280, 64, 64])]; + tensor reshape_133_cast_fp16 = reshape(shape = reshape_133_shape_0, x = real_div_33_cast_fp16)[name = tensor("reshape_133_cast_fp16")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867019456)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867022080)))]; + 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_fp16 = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_133_cast_fp16)[name = tensor("add_67_cast_fp16")]; + tensor input_761_cast_fp16 = silu(x = add_67_cast_fp16)[name = tensor("input_761_cast_fp16")]; + tensor var_13015 = const()[name = tensor("op_13015"), val = tensor([1, 1])]; + tensor var_13017 = const()[name = tensor("op_13017"), val = tensor([1, 1])]; + tensor hidden_states_527_pad_type_0 = const()[name = tensor("hidden_states_527_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_527_pad_0 = const()[name = tensor("hidden_states_527_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(1867024704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1872554368))), name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1280, 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(1872554560)))]; + tensor hidden_states_527_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_13017, groups = var_12548, pad = hidden_states_527_pad_0, pad_type = hidden_states_527_pad_type_0, strides = var_13015, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_761_cast_fp16)[name = tensor("hidden_states_527_cast_fp16")]; + tensor var_13023 = const()[name = tensor("op_13023"), val = tensor([1, 1])]; + tensor var_13025 = const()[name = tensor("op_13025"), 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_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(1872555904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1873170368))), name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 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(1873170560)))]; + tensor temb_25_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_13025, groups = var_12548, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_13023, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_25_cast_fp16")]; + tensor input_765_cast_fp16 = add(x = hidden_states_527_cast_fp16, y = temb_25_cast_fp16)[name = tensor("input_765_cast_fp16")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_136_cast_fp16 = reshape(shape = reshape_136_shape_0, x = input_765_cast_fp16)[name = tensor("reshape_136_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast_fp16)[name = tensor("reduce_mean_102_cast_fp16")]; + tensor sub_68_cast_fp16 = sub(x = reshape_136_cast_fp16, y = reduce_mean_102_cast_fp16)[name = tensor("sub_68_cast_fp16")]; + tensor square_34_cast_fp16 = square(x = sub_68_cast_fp16)[name = tensor("square_34_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast_fp16)[name = tensor("reduce_mean_104_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_104_cast_fp16, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast_fp16")]; + tensor sqrt_34_cast_fp16 = sqrt(x = add_68_cast_fp16)[name = tensor("sqrt_34_cast_fp16")]; + tensor real_div_34_cast_fp16 = real_div(x = sub_68_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor("real_div_34_cast_fp16")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_137_cast_fp16 = reshape(shape = reshape_137_shape_0, x = real_div_34_cast_fp16)[name = tensor("reshape_137_cast_fp16")]; + 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(1873171904)))]; + 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(1873173248)))]; + 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_fp16 = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_137_cast_fp16)[name = tensor("add_69_cast_fp16")]; + tensor input_769_cast_fp16 = silu(x = add_69_cast_fp16)[name = tensor("input_769_cast_fp16")]; + tensor var_13035 = const()[name = tensor("op_13035"), val = tensor([1, 1])]; + tensor var_13037 = const()[name = tensor("op_13037"), val = tensor([1, 1])]; + tensor hidden_states_529_pad_type_0 = const()[name = tensor("hidden_states_529_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_529_pad_0 = const()[name = tensor("hidden_states_529_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(1873174592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1875939456))), name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1875939648)))]; + tensor hidden_states_529_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_13037, groups = var_12548, pad = hidden_states_529_pad_0, pad_type = hidden_states_529_pad_type_0, strides = var_13035, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_769_cast_fp16)[name = tensor("hidden_states_529_cast_fp16")]; + tensor var_13042 = const()[name = tensor("op_13042"), val = tensor([1, 1])]; + tensor var_13044 = const()[name = tensor("op_13044"), 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(1875940992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876555456))), name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1280, 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(1876555648)))]; + tensor x_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13044, groups = var_12548, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_13042, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_757_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor hidden_states_531_cast_fp16 = add(x = x_13_cast_fp16, y = hidden_states_529_cast_fp16)[name = tensor("hidden_states_531_cast_fp16")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_140_cast_fp16 = reshape(shape = reshape_140_shape_0, x = hidden_states_531_cast_fp16)[name = tensor("reshape_140_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast_fp16)[name = tensor("reduce_mean_105_cast_fp16")]; + tensor sub_70_cast_fp16 = sub(x = reshape_140_cast_fp16, y = reduce_mean_105_cast_fp16)[name = tensor("sub_70_cast_fp16")]; + tensor square_35_cast_fp16 = square(x = sub_70_cast_fp16)[name = tensor("square_35_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast_fp16)[name = tensor("reduce_mean_107_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_107_cast_fp16, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast_fp16")]; + tensor sqrt_35_cast_fp16 = sqrt(x = add_70_cast_fp16)[name = tensor("sqrt_35_cast_fp16")]; + tensor real_div_35_cast_fp16 = real_div(x = sub_70_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor("real_div_35_cast_fp16")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_141_cast_fp16 = reshape(shape = reshape_141_shape_0, x = real_div_35_cast_fp16)[name = tensor("reshape_141_cast_fp16")]; + 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(1876556992)))]; + 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(1876558336)))]; + 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_fp16 = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_141_cast_fp16)[name = tensor("add_71_cast_fp16")]; + tensor var_13066 = const()[name = tensor("op_13066"), val = tensor([1, 1])]; + tensor var_13068 = const()[name = tensor("op_13068"), val = tensor([1, 1])]; + tensor hidden_states_533_pad_type_0 = const()[name = tensor("hidden_states_533_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_533_pad_0 = const()[name = tensor("hidden_states_533_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(1876559680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876866944))), name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1876867136)))]; + tensor hidden_states_533_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_13068, groups = var_12548, pad = hidden_states_533_pad_0, pad_type = hidden_states_533_pad_type_0, strides = var_13066, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_71_cast_fp16)[name = tensor("hidden_states_533_cast_fp16")]; + tensor var_13073 = const()[name = tensor("op_13073"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_397_cast_fp16 = reshape(shape = var_13073, x = hidden_states_533_cast_fp16)[name = tensor("inputs_397_cast_fp16")]; + tensor hidden_states_535_axes_0 = const()[name = tensor("hidden_states_535_axes_0"), val = tensor([1])]; + tensor hidden_states_535_gamma_0_to_fp16 = const()[name = tensor("hidden_states_535_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876868480)))]; + tensor hidden_states_535_beta_0_to_fp16 = const()[name = tensor("hidden_states_535_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876869824)))]; + tensor var_13089_to_fp16 = const()[name = tensor("op_13089_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_535_cast_fp16 = layer_norm(axes = hidden_states_535_axes_0, beta = hidden_states_535_beta_0_to_fp16, epsilon = var_13089_to_fp16, gamma = hidden_states_535_gamma_0_to_fp16, x = inputs_397_cast_fp16)[name = tensor("hidden_states_535_cast_fp16")]; + tensor var_13104 = const()[name = tensor("op_13104"), val = tensor([1, 1])]; + tensor var_13106 = const()[name = tensor("op_13106"), val = tensor([1, 1])]; + tensor q_265_pad_type_0 = const()[name = tensor("q_265_pad_type_0"), val = tensor("custom")]; + tensor q_265_pad_0 = const()[name = tensor("q_265_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(1876871168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877178432))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_265_cast_fp16 = conv(dilations = var_13106, groups = var_12548, pad = q_265_pad_0, pad_type = q_265_pad_type_0, strides = var_13104, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_535_cast_fp16)[name = tensor("q_265_cast_fp16")]; + tensor var_13110 = const()[name = tensor("op_13110"), val = tensor([1, 1])]; + tensor var_13112 = const()[name = tensor("op_13112"), val = tensor([1, 1])]; + tensor k_265_pad_type_0 = const()[name = tensor("k_265_pad_type_0"), val = tensor("custom")]; + tensor k_265_pad_0 = const()[name = tensor("k_265_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(1877178624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877485888))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_265_cast_fp16 = conv(dilations = var_13112, groups = var_12548, pad = k_265_pad_0, pad_type = k_265_pad_type_0, strides = var_13110, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_535_cast_fp16)[name = tensor("k_265_cast_fp16")]; + tensor var_13116 = const()[name = tensor("op_13116"), val = tensor([1, 1])]; + tensor var_13118 = const()[name = tensor("op_13118"), val = tensor([1, 1])]; + tensor v_265_pad_type_0 = const()[name = tensor("v_265_pad_type_0"), val = tensor("custom")]; + tensor v_265_pad_0 = const()[name = tensor("v_265_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(1877486080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877793344))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_265_cast_fp16 = conv(dilations = var_13118, groups = var_12548, pad = v_265_pad_0, pad_type = v_265_pad_type_0, strides = var_13116, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_535_cast_fp16)[name = tensor("v_265_cast_fp16")]; + tensor var_13122 = const()[name = tensor("op_13122"), val = tensor([1, 10, 64, -1])]; + tensor var_13123_cast_fp16 = reshape(shape = var_13122, x = q_265_cast_fp16)[name = tensor("op_13123_cast_fp16")]; + tensor var_13124 = const()[name = tensor("op_13124"), val = tensor([1, 10, 64, -1])]; + tensor var_13125_cast_fp16 = reshape(shape = var_13124, x = k_265_cast_fp16)[name = tensor("op_13125_cast_fp16")]; + tensor var_13126 = const()[name = tensor("op_13126"), val = tensor([1, 10, 64, -1])]; + tensor var_13127_cast_fp16 = reshape(shape = var_13126, x = v_265_cast_fp16)[name = tensor("op_13127_cast_fp16")]; + tensor attn_weights_529_transpose_x_0 = const()[name = tensor("attn_weights_529_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_529_transpose_y_0 = const()[name = tensor("attn_weights_529_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_529_cast_fp16 = matmul(transpose_x = attn_weights_529_transpose_x_0, transpose_y = attn_weights_529_transpose_y_0, x = var_13123_cast_fp16, y = var_13125_cast_fp16)[name = tensor("attn_weights_529_cast_fp16")]; + tensor attn_weights_531_cast_fp16 = mul(x = attn_weights_529_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_531_cast_fp16")]; + tensor var_13131_cast_fp16 = softmax(axis = var_12532, x = attn_weights_531_cast_fp16)[name = tensor("op_13131_cast_fp16")]; + tensor attn_265_transpose_x_0 = const()[name = tensor("attn_265_transpose_x_0"), val = tensor(false)]; + tensor attn_265_transpose_y_0 = const()[name = tensor("attn_265_transpose_y_0"), val = tensor(true)]; + tensor attn_265_cast_fp16 = matmul(transpose_x = attn_265_transpose_x_0, transpose_y = attn_265_transpose_y_0, x = var_13127_cast_fp16, y = var_13131_cast_fp16)[name = tensor("attn_265_cast_fp16")]; + tensor var_13135 = const()[name = tensor("op_13135"), val = tensor([1, 640, 1, -1])]; + tensor input_773_cast_fp16 = reshape(shape = var_13135, x = attn_265_cast_fp16)[name = tensor("input_773_cast_fp16")]; + tensor var_13140 = const()[name = tensor("op_13140"), val = tensor([1, 1])]; + tensor var_13142 = const()[name = tensor("op_13142"), val = tensor([1, 1])]; + tensor var_13144_pad_type_0 = const()[name = tensor("op_13144_pad_type_0"), val = tensor("custom")]; + tensor var_13144_pad_0 = const()[name = tensor("op_13144_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(1877793536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878100800))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1878100992)))]; + tensor var_13144_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13142, groups = var_12548, pad = var_13144_pad_0, pad_type = var_13144_pad_type_0, strides = var_13140, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_773_cast_fp16)[name = tensor("op_13144_cast_fp16")]; + tensor inputs_399_cast_fp16 = add(x = var_13144_cast_fp16, y = inputs_397_cast_fp16)[name = tensor("inputs_399_cast_fp16")]; + tensor hidden_states_537_axes_0 = const()[name = tensor("hidden_states_537_axes_0"), val = tensor([1])]; + tensor hidden_states_537_gamma_0_to_fp16 = const()[name = tensor("hidden_states_537_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878102336)))]; + tensor hidden_states_537_beta_0_to_fp16 = const()[name = tensor("hidden_states_537_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878103680)))]; + tensor var_13154_to_fp16 = const()[name = tensor("op_13154_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_537_cast_fp16 = layer_norm(axes = hidden_states_537_axes_0, beta = hidden_states_537_beta_0_to_fp16, epsilon = var_13154_to_fp16, gamma = hidden_states_537_gamma_0_to_fp16, x = inputs_399_cast_fp16)[name = tensor("hidden_states_537_cast_fp16")]; + tensor var_13169 = const()[name = tensor("op_13169"), val = tensor([1, 1])]; + tensor var_13171 = const()[name = tensor("op_13171"), val = tensor([1, 1])]; + tensor q_267_pad_type_0 = const()[name = tensor("q_267_pad_type_0"), val = tensor("custom")]; + tensor q_267_pad_0 = const()[name = tensor("q_267_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(1878105024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878412288))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_267_cast_fp16 = conv(dilations = var_13171, groups = var_12548, pad = q_267_pad_0, pad_type = q_267_pad_type_0, strides = var_13169, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_537_cast_fp16)[name = tensor("q_267_cast_fp16")]; + tensor var_13175 = const()[name = tensor("op_13175"), val = tensor([1, 1])]; + tensor var_13177 = const()[name = tensor("op_13177"), val = tensor([1, 1])]; + tensor k_267_pad_type_0 = const()[name = tensor("k_267_pad_type_0"), val = tensor("custom")]; + tensor k_267_pad_0 = const()[name = tensor("k_267_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(1878412480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1879395584))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_267_cast_fp16 = conv(dilations = var_13177, groups = var_12548, pad = k_267_pad_0, pad_type = k_267_pad_type_0, strides = var_13175, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_267_cast_fp16")]; + tensor var_13181 = const()[name = tensor("op_13181"), val = tensor([1, 1])]; + tensor var_13183 = const()[name = tensor("op_13183"), val = tensor([1, 1])]; + tensor v_267_pad_type_0 = const()[name = tensor("v_267_pad_type_0"), val = tensor("custom")]; + tensor v_267_pad_0 = const()[name = tensor("v_267_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(1879395776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880378880))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_267_cast_fp16 = conv(dilations = var_13183, groups = var_12548, pad = v_267_pad_0, pad_type = v_267_pad_type_0, strides = var_13181, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_267_cast_fp16")]; + tensor var_13187 = const()[name = tensor("op_13187"), val = tensor([1, 10, 64, -1])]; + tensor var_13188_cast_fp16 = reshape(shape = var_13187, x = q_267_cast_fp16)[name = tensor("op_13188_cast_fp16")]; + tensor var_13189 = const()[name = tensor("op_13189"), val = tensor([1, 10, 64, -1])]; + tensor var_13190_cast_fp16 = reshape(shape = var_13189, x = k_267_cast_fp16)[name = tensor("op_13190_cast_fp16")]; + tensor var_13191 = const()[name = tensor("op_13191"), val = tensor([1, 10, 64, -1])]; + tensor var_13192_cast_fp16 = reshape(shape = var_13191, x = v_267_cast_fp16)[name = tensor("op_13192_cast_fp16")]; + tensor attn_weights_533_transpose_x_0 = const()[name = tensor("attn_weights_533_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_533_transpose_y_0 = const()[name = tensor("attn_weights_533_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_533_cast_fp16 = matmul(transpose_x = attn_weights_533_transpose_x_0, transpose_y = attn_weights_533_transpose_y_0, x = var_13188_cast_fp16, y = var_13190_cast_fp16)[name = tensor("attn_weights_533_cast_fp16")]; + tensor attn_weights_535_cast_fp16 = mul(x = attn_weights_533_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_535_cast_fp16")]; + tensor var_13196_cast_fp16 = softmax(axis = var_12532, x = attn_weights_535_cast_fp16)[name = tensor("op_13196_cast_fp16")]; + tensor attn_267_transpose_x_0 = const()[name = tensor("attn_267_transpose_x_0"), val = tensor(false)]; + tensor attn_267_transpose_y_0 = const()[name = tensor("attn_267_transpose_y_0"), val = tensor(true)]; + tensor attn_267_cast_fp16 = matmul(transpose_x = attn_267_transpose_x_0, transpose_y = attn_267_transpose_y_0, x = var_13192_cast_fp16, y = var_13196_cast_fp16)[name = tensor("attn_267_cast_fp16")]; + tensor var_13200 = const()[name = tensor("op_13200"), val = tensor([1, 640, 1, -1])]; + tensor input_775_cast_fp16 = reshape(shape = var_13200, x = attn_267_cast_fp16)[name = tensor("input_775_cast_fp16")]; + tensor var_13205 = const()[name = tensor("op_13205"), val = tensor([1, 1])]; + tensor var_13207 = const()[name = tensor("op_13207"), val = tensor([1, 1])]; + tensor var_13209_pad_type_0 = const()[name = tensor("op_13209_pad_type_0"), val = tensor("custom")]; + tensor var_13209_pad_0 = const()[name = tensor("op_13209_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(1880379072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880686336))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1880686528)))]; + tensor var_13209_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13207, groups = var_12548, pad = var_13209_pad_0, pad_type = var_13209_pad_type_0, strides = var_13205, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_775_cast_fp16)[name = tensor("op_13209_cast_fp16")]; + tensor inputs_401_cast_fp16 = add(x = var_13209_cast_fp16, y = inputs_399_cast_fp16)[name = tensor("inputs_401_cast_fp16")]; + tensor input_777_axes_0 = const()[name = tensor("input_777_axes_0"), val = tensor([1])]; + tensor input_777_gamma_0_to_fp16 = const()[name = tensor("input_777_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880687872)))]; + tensor input_777_beta_0_to_fp16 = const()[name = tensor("input_777_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880689216)))]; + tensor var_13219_to_fp16 = const()[name = tensor("op_13219_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_777_cast_fp16 = layer_norm(axes = input_777_axes_0, beta = input_777_beta_0_to_fp16, epsilon = var_13219_to_fp16, gamma = input_777_gamma_0_to_fp16, x = inputs_401_cast_fp16)[name = tensor("input_777_cast_fp16")]; + tensor var_13235 = const()[name = tensor("op_13235"), val = tensor([1, 1])]; + tensor var_13237 = const()[name = tensor("op_13237"), val = tensor([1, 1])]; + tensor var_13239_pad_type_0 = const()[name = tensor("op_13239_pad_type_0"), val = tensor("custom")]; + tensor var_13239_pad_0 = const()[name = tensor("op_13239_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(1880690560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1883148224))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 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(1883148416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1883152320))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13239_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13237, groups = var_12548, pad = var_13239_pad_0, pad_type = var_13239_pad_type_0, strides = var_13235, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_777_cast_fp16)[name = tensor("op_13239_cast_fp16")]; + tensor var_13240_split_sizes_0 = const()[name = tensor("op_13240_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13240_axis_0 = const()[name = tensor("op_13240_axis_0"), val = tensor(1)]; + tensor var_13240_cast_fp16_0, tensor var_13240_cast_fp16_1 = split(axis = var_13240_axis_0, split_sizes = var_13240_split_sizes_0, x = var_13239_cast_fp16)[name = tensor("op_13240_cast_fp16")]; + tensor var_13242_mode_0 = const()[name = tensor("op_13242_mode_0"), val = tensor("EXACT")]; + tensor var_13242_cast_fp16 = gelu(mode = var_13242_mode_0, x = var_13240_cast_fp16_1)[name = tensor("op_13242_cast_fp16")]; + tensor input_779_cast_fp16 = mul(x = var_13240_cast_fp16_0, y = var_13242_cast_fp16)[name = tensor("input_779_cast_fp16")]; + tensor var_13246 = const()[name = tensor("op_13246"), val = tensor([1, 1])]; + tensor var_13248 = const()[name = tensor("op_13248"), val = tensor([1, 1])]; + tensor var_13250_pad_type_0 = const()[name = tensor("op_13250_pad_type_0"), val = tensor("custom")]; + tensor var_13250_pad_0 = const()[name = tensor("op_13250_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(1883152512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884381376))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 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(1884381568)))]; + tensor var_13250_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13248, groups = var_12548, pad = var_13250_pad_0, pad_type = var_13250_pad_type_0, strides = var_13246, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_779_cast_fp16)[name = tensor("op_13250_cast_fp16")]; + tensor inputs_403_cast_fp16 = add(x = var_13250_cast_fp16, y = inputs_401_cast_fp16)[name = tensor("inputs_403_cast_fp16")]; + tensor hidden_states_541_axes_0 = const()[name = tensor("hidden_states_541_axes_0"), val = tensor([1])]; + tensor hidden_states_541_gamma_0_to_fp16 = const()[name = tensor("hidden_states_541_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884382912)))]; + tensor hidden_states_541_beta_0_to_fp16 = const()[name = tensor("hidden_states_541_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884384256)))]; + tensor var_13266_to_fp16 = const()[name = tensor("op_13266_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_541_cast_fp16 = layer_norm(axes = hidden_states_541_axes_0, beta = hidden_states_541_beta_0_to_fp16, epsilon = var_13266_to_fp16, gamma = hidden_states_541_gamma_0_to_fp16, x = inputs_403_cast_fp16)[name = tensor("hidden_states_541_cast_fp16")]; + tensor var_13281 = const()[name = tensor("op_13281"), val = tensor([1, 1])]; + tensor var_13283 = const()[name = tensor("op_13283"), val = tensor([1, 1])]; + tensor q_269_pad_type_0 = const()[name = tensor("q_269_pad_type_0"), val = tensor("custom")]; + tensor q_269_pad_0 = const()[name = tensor("q_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884385600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884692864))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_269_cast_fp16 = conv(dilations = var_13283, groups = var_12548, pad = q_269_pad_0, pad_type = q_269_pad_type_0, strides = var_13281, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_541_cast_fp16)[name = tensor("q_269_cast_fp16")]; + tensor var_13287 = const()[name = tensor("op_13287"), val = tensor([1, 1])]; + tensor var_13289 = const()[name = tensor("op_13289"), val = tensor([1, 1])]; + tensor k_269_pad_type_0 = const()[name = tensor("k_269_pad_type_0"), val = tensor("custom")]; + tensor k_269_pad_0 = const()[name = tensor("k_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884693056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885000320))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_269_cast_fp16 = conv(dilations = var_13289, groups = var_12548, pad = k_269_pad_0, pad_type = k_269_pad_type_0, strides = var_13287, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_541_cast_fp16)[name = tensor("k_269_cast_fp16")]; + tensor var_13293 = const()[name = tensor("op_13293"), val = tensor([1, 1])]; + tensor var_13295 = const()[name = tensor("op_13295"), val = tensor([1, 1])]; + tensor v_269_pad_type_0 = const()[name = tensor("v_269_pad_type_0"), val = tensor("custom")]; + tensor v_269_pad_0 = const()[name = tensor("v_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885000512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885307776))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_269_cast_fp16 = conv(dilations = var_13295, groups = var_12548, pad = v_269_pad_0, pad_type = v_269_pad_type_0, strides = var_13293, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_541_cast_fp16)[name = tensor("v_269_cast_fp16")]; + tensor var_13299 = const()[name = tensor("op_13299"), val = tensor([1, 10, 64, -1])]; + tensor var_13300_cast_fp16 = reshape(shape = var_13299, x = q_269_cast_fp16)[name = tensor("op_13300_cast_fp16")]; + tensor var_13301 = const()[name = tensor("op_13301"), val = tensor([1, 10, 64, -1])]; + tensor var_13302_cast_fp16 = reshape(shape = var_13301, x = k_269_cast_fp16)[name = tensor("op_13302_cast_fp16")]; + tensor var_13303 = const()[name = tensor("op_13303"), val = tensor([1, 10, 64, -1])]; + tensor var_13304_cast_fp16 = reshape(shape = var_13303, x = v_269_cast_fp16)[name = tensor("op_13304_cast_fp16")]; + tensor attn_weights_537_transpose_x_0 = const()[name = tensor("attn_weights_537_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_537_transpose_y_0 = const()[name = tensor("attn_weights_537_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_537_cast_fp16 = matmul(transpose_x = attn_weights_537_transpose_x_0, transpose_y = attn_weights_537_transpose_y_0, x = var_13300_cast_fp16, y = var_13302_cast_fp16)[name = tensor("attn_weights_537_cast_fp16")]; + tensor attn_weights_539_cast_fp16 = mul(x = attn_weights_537_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_539_cast_fp16")]; + tensor var_13308_cast_fp16 = softmax(axis = var_12532, x = attn_weights_539_cast_fp16)[name = tensor("op_13308_cast_fp16")]; + tensor attn_269_transpose_x_0 = const()[name = tensor("attn_269_transpose_x_0"), val = tensor(false)]; + tensor attn_269_transpose_y_0 = const()[name = tensor("attn_269_transpose_y_0"), val = tensor(true)]; + tensor attn_269_cast_fp16 = matmul(transpose_x = attn_269_transpose_x_0, transpose_y = attn_269_transpose_y_0, x = var_13304_cast_fp16, y = var_13308_cast_fp16)[name = tensor("attn_269_cast_fp16")]; + tensor var_13312 = const()[name = tensor("op_13312"), val = tensor([1, 640, 1, -1])]; + tensor input_781_cast_fp16 = reshape(shape = var_13312, x = attn_269_cast_fp16)[name = tensor("input_781_cast_fp16")]; + tensor var_13317 = const()[name = tensor("op_13317"), val = tensor([1, 1])]; + tensor var_13319 = const()[name = tensor("op_13319"), val = tensor([1, 1])]; + tensor var_13321_pad_type_0 = const()[name = tensor("op_13321_pad_type_0"), val = tensor("custom")]; + tensor var_13321_pad_0 = const()[name = tensor("op_13321_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885307968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885615232))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885615424)))]; + tensor var_13321_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13319, groups = var_12548, pad = var_13321_pad_0, pad_type = var_13321_pad_type_0, strides = var_13317, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_781_cast_fp16)[name = tensor("op_13321_cast_fp16")]; + tensor inputs_405_cast_fp16 = add(x = var_13321_cast_fp16, y = inputs_403_cast_fp16)[name = tensor("inputs_405_cast_fp16")]; + tensor hidden_states_543_axes_0 = const()[name = tensor("hidden_states_543_axes_0"), val = tensor([1])]; + tensor hidden_states_543_gamma_0_to_fp16 = const()[name = tensor("hidden_states_543_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885616768)))]; + tensor hidden_states_543_beta_0_to_fp16 = const()[name = tensor("hidden_states_543_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885618112)))]; + tensor var_13331_to_fp16 = const()[name = tensor("op_13331_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_543_cast_fp16 = layer_norm(axes = hidden_states_543_axes_0, beta = hidden_states_543_beta_0_to_fp16, epsilon = var_13331_to_fp16, gamma = hidden_states_543_gamma_0_to_fp16, x = inputs_405_cast_fp16)[name = tensor("hidden_states_543_cast_fp16")]; + tensor var_13346 = const()[name = tensor("op_13346"), val = tensor([1, 1])]; + tensor var_13348 = const()[name = tensor("op_13348"), val = tensor([1, 1])]; + tensor q_271_pad_type_0 = const()[name = tensor("q_271_pad_type_0"), val = tensor("custom")]; + tensor q_271_pad_0 = const()[name = tensor("q_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885619456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885926720))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_271_cast_fp16 = conv(dilations = var_13348, groups = var_12548, pad = q_271_pad_0, pad_type = q_271_pad_type_0, strides = var_13346, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_543_cast_fp16)[name = tensor("q_271_cast_fp16")]; + tensor var_13352 = const()[name = tensor("op_13352"), val = tensor([1, 1])]; + tensor var_13354 = const()[name = tensor("op_13354"), val = tensor([1, 1])]; + tensor k_271_pad_type_0 = const()[name = tensor("k_271_pad_type_0"), val = tensor("custom")]; + tensor k_271_pad_0 = const()[name = tensor("k_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885926912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1886910016))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_271_cast_fp16 = conv(dilations = var_13354, groups = var_12548, pad = k_271_pad_0, pad_type = k_271_pad_type_0, strides = var_13352, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_271_cast_fp16")]; + tensor var_13358 = const()[name = tensor("op_13358"), val = tensor([1, 1])]; + tensor var_13360 = const()[name = tensor("op_13360"), val = tensor([1, 1])]; + tensor v_271_pad_type_0 = const()[name = tensor("v_271_pad_type_0"), val = tensor("custom")]; + tensor v_271_pad_0 = const()[name = tensor("v_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1886910208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1887893312))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_271_cast_fp16 = conv(dilations = var_13360, groups = var_12548, pad = v_271_pad_0, pad_type = v_271_pad_type_0, strides = var_13358, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_271_cast_fp16")]; + tensor var_13364 = const()[name = tensor("op_13364"), val = tensor([1, 10, 64, -1])]; + tensor var_13365_cast_fp16 = reshape(shape = var_13364, x = q_271_cast_fp16)[name = tensor("op_13365_cast_fp16")]; + tensor var_13366 = const()[name = tensor("op_13366"), val = tensor([1, 10, 64, -1])]; + tensor var_13367_cast_fp16 = reshape(shape = var_13366, x = k_271_cast_fp16)[name = tensor("op_13367_cast_fp16")]; + tensor var_13368 = const()[name = tensor("op_13368"), val = tensor([1, 10, 64, -1])]; + tensor var_13369_cast_fp16 = reshape(shape = var_13368, x = v_271_cast_fp16)[name = tensor("op_13369_cast_fp16")]; + tensor attn_weights_541_transpose_x_0 = const()[name = tensor("attn_weights_541_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_541_transpose_y_0 = const()[name = tensor("attn_weights_541_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_541_cast_fp16 = matmul(transpose_x = attn_weights_541_transpose_x_0, transpose_y = attn_weights_541_transpose_y_0, x = var_13365_cast_fp16, y = var_13367_cast_fp16)[name = tensor("attn_weights_541_cast_fp16")]; + tensor attn_weights_543_cast_fp16 = mul(x = attn_weights_541_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_543_cast_fp16")]; + tensor var_13373_cast_fp16 = softmax(axis = var_12532, x = attn_weights_543_cast_fp16)[name = tensor("op_13373_cast_fp16")]; + tensor attn_271_transpose_x_0 = const()[name = tensor("attn_271_transpose_x_0"), val = tensor(false)]; + tensor attn_271_transpose_y_0 = const()[name = tensor("attn_271_transpose_y_0"), val = tensor(true)]; + tensor attn_271_cast_fp16 = matmul(transpose_x = attn_271_transpose_x_0, transpose_y = attn_271_transpose_y_0, x = var_13369_cast_fp16, y = var_13373_cast_fp16)[name = tensor("attn_271_cast_fp16")]; + tensor var_13377 = const()[name = tensor("op_13377"), val = tensor([1, 640, 1, -1])]; + tensor input_783_cast_fp16 = reshape(shape = var_13377, x = attn_271_cast_fp16)[name = tensor("input_783_cast_fp16")]; + tensor var_13382 = const()[name = tensor("op_13382"), val = tensor([1, 1])]; + tensor var_13384 = const()[name = tensor("op_13384"), val = tensor([1, 1])]; + tensor var_13386_pad_type_0 = const()[name = tensor("op_13386_pad_type_0"), val = tensor("custom")]; + tensor var_13386_pad_0 = const()[name = tensor("op_13386_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1887893504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888200768))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888200960)))]; + tensor var_13386_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13384, groups = var_12548, pad = var_13386_pad_0, pad_type = var_13386_pad_type_0, strides = var_13382, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_783_cast_fp16)[name = tensor("op_13386_cast_fp16")]; + tensor inputs_407_cast_fp16 = add(x = var_13386_cast_fp16, y = inputs_405_cast_fp16)[name = tensor("inputs_407_cast_fp16")]; + tensor input_785_axes_0 = const()[name = tensor("input_785_axes_0"), val = tensor([1])]; + tensor input_785_gamma_0_to_fp16 = const()[name = tensor("input_785_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888202304)))]; + tensor input_785_beta_0_to_fp16 = const()[name = tensor("input_785_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888203648)))]; + tensor var_13396_to_fp16 = const()[name = tensor("op_13396_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_785_cast_fp16 = layer_norm(axes = input_785_axes_0, beta = input_785_beta_0_to_fp16, epsilon = var_13396_to_fp16, gamma = input_785_gamma_0_to_fp16, x = inputs_407_cast_fp16)[name = tensor("input_785_cast_fp16")]; + tensor var_13412 = const()[name = tensor("op_13412"), val = tensor([1, 1])]; + tensor var_13414 = const()[name = tensor("op_13414"), val = tensor([1, 1])]; + tensor var_13416_pad_type_0 = const()[name = tensor("op_13416_pad_type_0"), val = tensor("custom")]; + tensor var_13416_pad_0 = const()[name = tensor("op_13416_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888204992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890662656))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890662848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890666752))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13416_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13414, groups = var_12548, pad = var_13416_pad_0, pad_type = var_13416_pad_type_0, strides = var_13412, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_785_cast_fp16)[name = tensor("op_13416_cast_fp16")]; + tensor var_13417_split_sizes_0 = const()[name = tensor("op_13417_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13417_axis_0 = const()[name = tensor("op_13417_axis_0"), val = tensor(1)]; + tensor var_13417_cast_fp16_0, tensor var_13417_cast_fp16_1 = split(axis = var_13417_axis_0, split_sizes = var_13417_split_sizes_0, x = var_13416_cast_fp16)[name = tensor("op_13417_cast_fp16")]; + tensor var_13419_mode_0 = const()[name = tensor("op_13419_mode_0"), val = tensor("EXACT")]; + tensor var_13419_cast_fp16 = gelu(mode = var_13419_mode_0, x = var_13417_cast_fp16_1)[name = tensor("op_13419_cast_fp16")]; + tensor input_787_cast_fp16 = mul(x = var_13417_cast_fp16_0, y = var_13419_cast_fp16)[name = tensor("input_787_cast_fp16")]; + tensor var_13423 = const()[name = tensor("op_13423"), val = tensor([1, 1])]; + tensor var_13425 = const()[name = tensor("op_13425"), val = tensor([1, 1])]; + tensor var_13427_pad_type_0 = const()[name = tensor("op_13427_pad_type_0"), val = tensor("custom")]; + tensor var_13427_pad_0 = const()[name = tensor("op_13427_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890666944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1891895808))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1891896000)))]; + tensor var_13427_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13425, groups = var_12548, pad = var_13427_pad_0, pad_type = var_13427_pad_type_0, strides = var_13423, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_787_cast_fp16)[name = tensor("op_13427_cast_fp16")]; + tensor hidden_states_547_cast_fp16 = add(x = var_13427_cast_fp16, y = inputs_407_cast_fp16)[name = tensor("hidden_states_547_cast_fp16")]; + tensor var_13429 = const()[name = tensor("op_13429"), val = tensor([1, 640, 64, 64])]; + tensor input_789_cast_fp16 = reshape(shape = var_13429, x = hidden_states_547_cast_fp16)[name = tensor("input_789_cast_fp16")]; + tensor var_13433 = const()[name = tensor("op_13433"), val = tensor([1, 1])]; + tensor var_13435 = const()[name = tensor("op_13435"), val = tensor([1, 1])]; + tensor hidden_states_549_pad_type_0 = const()[name = tensor("hidden_states_549_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_549_pad_0 = const()[name = tensor("hidden_states_549_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(1891897344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892204608))), name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1892204800)))]; + tensor hidden_states_549_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_13435, groups = var_12548, pad = hidden_states_549_pad_0, pad_type = hidden_states_549_pad_type_0, strides = var_13433, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_789_cast_fp16)[name = tensor("hidden_states_549_cast_fp16")]; + tensor hidden_states_551_cast_fp16 = add(x = hidden_states_549_cast_fp16, y = hidden_states_531_cast_fp16)[name = tensor("hidden_states_551_cast_fp16")]; + tensor input_791_interleave_0 = const()[name = tensor("input_791_interleave_0"), val = tensor(false)]; + tensor input_791_cast_fp16 = concat(axis = var_12548, interleave = input_791_interleave_0, values = (hidden_states_551_cast_fp16, res_hidden_states_11_cast_fp16))[name = tensor("input_791_cast_fp16")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([1, 32, 30, 64, 64])]; + tensor reshape_144_cast_fp16 = reshape(shape = reshape_144_shape_0, x = input_791_cast_fp16)[name = tensor("reshape_144_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast_fp16)[name = tensor("reduce_mean_108_cast_fp16")]; + tensor sub_72_cast_fp16 = sub(x = reshape_144_cast_fp16, y = reduce_mean_108_cast_fp16)[name = tensor("sub_72_cast_fp16")]; + tensor square_36_cast_fp16 = square(x = sub_72_cast_fp16)[name = tensor("square_36_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast_fp16)[name = tensor("reduce_mean_110_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_110_cast_fp16, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast_fp16")]; + tensor sqrt_36_cast_fp16 = sqrt(x = add_72_cast_fp16)[name = tensor("sqrt_36_cast_fp16")]; + tensor real_div_36_cast_fp16 = real_div(x = sub_72_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor("real_div_36_cast_fp16")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([1, 960, 64, 64])]; + tensor reshape_145_cast_fp16 = reshape(shape = reshape_145_shape_0, x = real_div_36_cast_fp16)[name = tensor("reshape_145_cast_fp16")]; + tensor add_73_mean_0_to_fp16 = const()[name = tensor("add_73_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892206144)))]; + tensor add_73_variance_0_to_fp16 = const()[name = tensor("add_73_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892208128)))]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892210112)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892212096)))]; + 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_fp16 = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_145_cast_fp16)[name = tensor("add_73_cast_fp16")]; + tensor input_795_cast_fp16 = silu(x = add_73_cast_fp16)[name = tensor("input_795_cast_fp16")]; + tensor var_13453 = const()[name = tensor("op_13453"), val = tensor([1, 1])]; + tensor var_13455 = const()[name = tensor("op_13455"), val = tensor([1, 1])]; + tensor hidden_states_553_pad_type_0 = const()[name = tensor("hidden_states_553_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_553_pad_0 = const()[name = tensor("hidden_states_553_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(1892214080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896361344))), name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([640, 960, 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(1896361536)))]; + tensor hidden_states_553_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_13455, groups = var_12548, pad = hidden_states_553_pad_0, pad_type = hidden_states_553_pad_type_0, strides = var_13453, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized, x = input_795_cast_fp16)[name = tensor("hidden_states_553_cast_fp16")]; + tensor var_13461 = const()[name = tensor("op_13461"), val = tensor([1, 1])]; + tensor var_13463 = const()[name = tensor("op_13463"), 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_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896362880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896977344))), name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 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(1896977536)))]; + tensor temb_27_cast_fp16 = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13463, groups = var_12548, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_13461, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_27_cast_fp16")]; + tensor input_799_cast_fp16 = add(x = hidden_states_553_cast_fp16, y = temb_27_cast_fp16)[name = tensor("input_799_cast_fp16")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_148_cast_fp16 = reshape(shape = reshape_148_shape_0, x = input_799_cast_fp16)[name = tensor("reshape_148_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast_fp16)[name = tensor("reduce_mean_111_cast_fp16")]; + tensor sub_74_cast_fp16 = sub(x = reshape_148_cast_fp16, y = reduce_mean_111_cast_fp16)[name = tensor("sub_74_cast_fp16")]; + tensor square_37_cast_fp16 = square(x = sub_74_cast_fp16)[name = tensor("square_37_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast_fp16)[name = tensor("reduce_mean_113_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_113_cast_fp16, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast_fp16")]; + tensor sqrt_37_cast_fp16 = sqrt(x = add_74_cast_fp16)[name = tensor("sqrt_37_cast_fp16")]; + tensor real_div_37_cast_fp16 = real_div(x = sub_74_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor("real_div_37_cast_fp16")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_149_cast_fp16 = reshape(shape = reshape_149_shape_0, x = real_div_37_cast_fp16)[name = tensor("reshape_149_cast_fp16")]; + 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(1896978880)))]; + 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(1896980224)))]; + 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_fp16 = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_149_cast_fp16)[name = tensor("add_75_cast_fp16")]; + tensor input_803_cast_fp16 = silu(x = add_75_cast_fp16)[name = tensor("input_803_cast_fp16")]; + tensor var_13473 = const()[name = tensor("op_13473"), val = tensor([1, 1])]; + tensor var_13475 = const()[name = tensor("op_13475"), val = tensor([1, 1])]; + tensor hidden_states_555_pad_type_0 = const()[name = tensor("hidden_states_555_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_555_pad_0 = const()[name = tensor("hidden_states_555_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(1896981568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1899746432))), name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1899746624)))]; + tensor hidden_states_555_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_13475, groups = var_12548, pad = hidden_states_555_pad_0, pad_type = hidden_states_555_pad_type_0, strides = var_13473, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized, x = input_803_cast_fp16)[name = tensor("hidden_states_555_cast_fp16")]; + tensor var_13480 = const()[name = tensor("op_13480"), val = tensor([1, 1])]; + tensor var_13482 = const()[name = tensor("op_13482"), 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(1899747968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900208832))), name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 960, 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(1900209024)))]; + tensor x_15_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_13482, groups = var_12548, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_13480, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_791_cast_fp16)[name = tensor("x_15_cast_fp16")]; + tensor hidden_states_557_cast_fp16 = add(x = x_15_cast_fp16, y = hidden_states_555_cast_fp16)[name = tensor("hidden_states_557_cast_fp16")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_152_cast_fp16 = reshape(shape = reshape_152_shape_0, x = hidden_states_557_cast_fp16)[name = tensor("reshape_152_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast_fp16)[name = tensor("reduce_mean_114_cast_fp16")]; + tensor sub_76_cast_fp16 = sub(x = reshape_152_cast_fp16, y = reduce_mean_114_cast_fp16)[name = tensor("sub_76_cast_fp16")]; + tensor square_38_cast_fp16 = square(x = sub_76_cast_fp16)[name = tensor("square_38_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast_fp16)[name = tensor("reduce_mean_116_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_116_cast_fp16, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast_fp16")]; + tensor sqrt_38_cast_fp16 = sqrt(x = add_76_cast_fp16)[name = tensor("sqrt_38_cast_fp16")]; + tensor real_div_38_cast_fp16 = real_div(x = sub_76_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor("real_div_38_cast_fp16")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_153_cast_fp16 = reshape(shape = reshape_153_shape_0, x = real_div_38_cast_fp16)[name = tensor("reshape_153_cast_fp16")]; + 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(1900210368)))]; + 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(1900211712)))]; + 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_fp16 = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_153_cast_fp16)[name = tensor("add_77_cast_fp16")]; + tensor var_13504 = const()[name = tensor("op_13504"), val = tensor([1, 1])]; + tensor var_13506 = const()[name = tensor("op_13506"), val = tensor([1, 1])]; + tensor hidden_states_559_pad_type_0 = const()[name = tensor("hidden_states_559_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_559_pad_0 = const()[name = tensor("hidden_states_559_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(1900213056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900520320))), name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1900520512)))]; + tensor hidden_states_559_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_13506, groups = var_12548, pad = hidden_states_559_pad_0, pad_type = hidden_states_559_pad_type_0, strides = var_13504, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized, x = add_77_cast_fp16)[name = tensor("hidden_states_559_cast_fp16")]; + tensor var_13511 = const()[name = tensor("op_13511"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_409_cast_fp16 = reshape(shape = var_13511, x = hidden_states_559_cast_fp16)[name = tensor("inputs_409_cast_fp16")]; + tensor hidden_states_561_axes_0 = const()[name = tensor("hidden_states_561_axes_0"), val = tensor([1])]; + tensor hidden_states_561_gamma_0_to_fp16 = const()[name = tensor("hidden_states_561_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900521856)))]; + tensor hidden_states_561_beta_0_to_fp16 = const()[name = tensor("hidden_states_561_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900523200)))]; + tensor var_13527_to_fp16 = const()[name = tensor("op_13527_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_561_cast_fp16 = layer_norm(axes = hidden_states_561_axes_0, beta = hidden_states_561_beta_0_to_fp16, epsilon = var_13527_to_fp16, gamma = hidden_states_561_gamma_0_to_fp16, x = inputs_409_cast_fp16)[name = tensor("hidden_states_561_cast_fp16")]; + tensor var_13542 = const()[name = tensor("op_13542"), val = tensor([1, 1])]; + tensor var_13544 = const()[name = tensor("op_13544"), val = tensor([1, 1])]; + tensor q_273_pad_type_0 = const()[name = tensor("q_273_pad_type_0"), val = tensor("custom")]; + tensor q_273_pad_0 = const()[name = tensor("q_273_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(1900524544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900831808))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_273_cast_fp16 = conv(dilations = var_13544, groups = var_12548, pad = q_273_pad_0, pad_type = q_273_pad_type_0, strides = var_13542, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_561_cast_fp16)[name = tensor("q_273_cast_fp16")]; + tensor var_13548 = const()[name = tensor("op_13548"), val = tensor([1, 1])]; + tensor var_13550 = const()[name = tensor("op_13550"), val = tensor([1, 1])]; + tensor k_273_pad_type_0 = const()[name = tensor("k_273_pad_type_0"), val = tensor("custom")]; + tensor k_273_pad_0 = const()[name = tensor("k_273_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(1900832000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901139264))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_273_cast_fp16 = conv(dilations = var_13550, groups = var_12548, pad = k_273_pad_0, pad_type = k_273_pad_type_0, strides = var_13548, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_561_cast_fp16)[name = tensor("k_273_cast_fp16")]; + tensor var_13554 = const()[name = tensor("op_13554"), val = tensor([1, 1])]; + tensor var_13556 = const()[name = tensor("op_13556"), val = tensor([1, 1])]; + tensor v_273_pad_type_0 = const()[name = tensor("v_273_pad_type_0"), val = tensor("custom")]; + tensor v_273_pad_0 = const()[name = tensor("v_273_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(1901139456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901446720))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_273_cast_fp16 = conv(dilations = var_13556, groups = var_12548, pad = v_273_pad_0, pad_type = v_273_pad_type_0, strides = var_13554, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_561_cast_fp16)[name = tensor("v_273_cast_fp16")]; + tensor var_13560 = const()[name = tensor("op_13560"), val = tensor([1, 10, 64, -1])]; + tensor var_13561_cast_fp16 = reshape(shape = var_13560, x = q_273_cast_fp16)[name = tensor("op_13561_cast_fp16")]; + tensor var_13562 = const()[name = tensor("op_13562"), val = tensor([1, 10, 64, -1])]; + tensor var_13563_cast_fp16 = reshape(shape = var_13562, x = k_273_cast_fp16)[name = tensor("op_13563_cast_fp16")]; + tensor var_13564 = const()[name = tensor("op_13564"), val = tensor([1, 10, 64, -1])]; + tensor var_13565_cast_fp16 = reshape(shape = var_13564, x = v_273_cast_fp16)[name = tensor("op_13565_cast_fp16")]; + tensor attn_weights_545_transpose_x_0 = const()[name = tensor("attn_weights_545_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_545_transpose_y_0 = const()[name = tensor("attn_weights_545_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_545_cast_fp16 = matmul(transpose_x = attn_weights_545_transpose_x_0, transpose_y = attn_weights_545_transpose_y_0, x = var_13561_cast_fp16, y = var_13563_cast_fp16)[name = tensor("attn_weights_545_cast_fp16")]; + tensor attn_weights_547_cast_fp16 = mul(x = attn_weights_545_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_547_cast_fp16")]; + tensor var_13569_cast_fp16 = softmax(axis = var_12532, x = attn_weights_547_cast_fp16)[name = tensor("op_13569_cast_fp16")]; + tensor attn_273_transpose_x_0 = const()[name = tensor("attn_273_transpose_x_0"), val = tensor(false)]; + tensor attn_273_transpose_y_0 = const()[name = tensor("attn_273_transpose_y_0"), val = tensor(true)]; + tensor attn_273_cast_fp16 = matmul(transpose_x = attn_273_transpose_x_0, transpose_y = attn_273_transpose_y_0, x = var_13565_cast_fp16, y = var_13569_cast_fp16)[name = tensor("attn_273_cast_fp16")]; + tensor var_13573 = const()[name = tensor("op_13573"), val = tensor([1, 640, 1, -1])]; + tensor input_807_cast_fp16 = reshape(shape = var_13573, x = attn_273_cast_fp16)[name = tensor("input_807_cast_fp16")]; + tensor var_13578 = const()[name = tensor("op_13578"), val = tensor([1, 1])]; + tensor var_13580 = const()[name = tensor("op_13580"), val = tensor([1, 1])]; + tensor var_13582_pad_type_0 = const()[name = tensor("op_13582_pad_type_0"), val = tensor("custom")]; + tensor var_13582_pad_0 = const()[name = tensor("op_13582_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(1901446912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901754176))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1901754368)))]; + tensor var_13582_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13580, groups = var_12548, pad = var_13582_pad_0, pad_type = var_13582_pad_type_0, strides = var_13578, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_807_cast_fp16)[name = tensor("op_13582_cast_fp16")]; + tensor inputs_411_cast_fp16 = add(x = var_13582_cast_fp16, y = inputs_409_cast_fp16)[name = tensor("inputs_411_cast_fp16")]; + tensor hidden_states_563_axes_0 = const()[name = tensor("hidden_states_563_axes_0"), val = tensor([1])]; + tensor hidden_states_563_gamma_0_to_fp16 = const()[name = tensor("hidden_states_563_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901755712)))]; + tensor hidden_states_563_beta_0_to_fp16 = const()[name = tensor("hidden_states_563_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901757056)))]; + tensor var_13592_to_fp16 = const()[name = tensor("op_13592_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_563_cast_fp16 = layer_norm(axes = hidden_states_563_axes_0, beta = hidden_states_563_beta_0_to_fp16, epsilon = var_13592_to_fp16, gamma = hidden_states_563_gamma_0_to_fp16, x = inputs_411_cast_fp16)[name = tensor("hidden_states_563_cast_fp16")]; + tensor var_13607 = const()[name = tensor("op_13607"), val = tensor([1, 1])]; + tensor var_13609 = const()[name = tensor("op_13609"), val = tensor([1, 1])]; + tensor q_275_pad_type_0 = const()[name = tensor("q_275_pad_type_0"), val = tensor("custom")]; + tensor q_275_pad_0 = const()[name = tensor("q_275_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(1901758400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1902065664))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_275_cast_fp16 = conv(dilations = var_13609, groups = var_12548, pad = q_275_pad_0, pad_type = q_275_pad_type_0, strides = var_13607, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_563_cast_fp16)[name = tensor("q_275_cast_fp16")]; + tensor var_13613 = const()[name = tensor("op_13613"), val = tensor([1, 1])]; + tensor var_13615 = const()[name = tensor("op_13615"), val = tensor([1, 1])]; + tensor k_275_pad_type_0 = const()[name = tensor("k_275_pad_type_0"), val = tensor("custom")]; + tensor k_275_pad_0 = const()[name = tensor("k_275_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(1902065856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1903048960))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_275_cast_fp16 = conv(dilations = var_13615, groups = var_12548, pad = k_275_pad_0, pad_type = k_275_pad_type_0, strides = var_13613, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_275_cast_fp16")]; + tensor var_13619 = const()[name = tensor("op_13619"), val = tensor([1, 1])]; + tensor var_13621 = const()[name = tensor("op_13621"), val = tensor([1, 1])]; + tensor v_275_pad_type_0 = const()[name = tensor("v_275_pad_type_0"), val = tensor("custom")]; + tensor v_275_pad_0 = const()[name = tensor("v_275_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(1903049152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904032256))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_275_cast_fp16 = conv(dilations = var_13621, groups = var_12548, pad = v_275_pad_0, pad_type = v_275_pad_type_0, strides = var_13619, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_275_cast_fp16")]; + tensor var_13625 = const()[name = tensor("op_13625"), val = tensor([1, 10, 64, -1])]; + tensor var_13626_cast_fp16 = reshape(shape = var_13625, x = q_275_cast_fp16)[name = tensor("op_13626_cast_fp16")]; + tensor var_13627 = const()[name = tensor("op_13627"), val = tensor([1, 10, 64, -1])]; + tensor var_13628_cast_fp16 = reshape(shape = var_13627, x = k_275_cast_fp16)[name = tensor("op_13628_cast_fp16")]; + tensor var_13629 = const()[name = tensor("op_13629"), val = tensor([1, 10, 64, -1])]; + tensor var_13630_cast_fp16 = reshape(shape = var_13629, x = v_275_cast_fp16)[name = tensor("op_13630_cast_fp16")]; + tensor attn_weights_549_transpose_x_0 = const()[name = tensor("attn_weights_549_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_549_transpose_y_0 = const()[name = tensor("attn_weights_549_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_549_cast_fp16 = matmul(transpose_x = attn_weights_549_transpose_x_0, transpose_y = attn_weights_549_transpose_y_0, x = var_13626_cast_fp16, y = var_13628_cast_fp16)[name = tensor("attn_weights_549_cast_fp16")]; + tensor attn_weights_551_cast_fp16 = mul(x = attn_weights_549_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_551_cast_fp16")]; + tensor var_13634_cast_fp16 = softmax(axis = var_12532, x = attn_weights_551_cast_fp16)[name = tensor("op_13634_cast_fp16")]; + tensor attn_275_transpose_x_0 = const()[name = tensor("attn_275_transpose_x_0"), val = tensor(false)]; + tensor attn_275_transpose_y_0 = const()[name = tensor("attn_275_transpose_y_0"), val = tensor(true)]; + tensor attn_275_cast_fp16 = matmul(transpose_x = attn_275_transpose_x_0, transpose_y = attn_275_transpose_y_0, x = var_13630_cast_fp16, y = var_13634_cast_fp16)[name = tensor("attn_275_cast_fp16")]; + tensor var_13638 = const()[name = tensor("op_13638"), val = tensor([1, 640, 1, -1])]; + tensor input_809_cast_fp16 = reshape(shape = var_13638, x = attn_275_cast_fp16)[name = tensor("input_809_cast_fp16")]; + tensor var_13643 = const()[name = tensor("op_13643"), val = tensor([1, 1])]; + tensor var_13645 = const()[name = tensor("op_13645"), val = tensor([1, 1])]; + tensor var_13647_pad_type_0 = const()[name = tensor("op_13647_pad_type_0"), val = tensor("custom")]; + tensor var_13647_pad_0 = const()[name = tensor("op_13647_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(1904032448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904339712))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1904339904)))]; + tensor var_13647_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13645, groups = var_12548, pad = var_13647_pad_0, pad_type = var_13647_pad_type_0, strides = var_13643, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_809_cast_fp16)[name = tensor("op_13647_cast_fp16")]; + tensor inputs_413_cast_fp16 = add(x = var_13647_cast_fp16, y = inputs_411_cast_fp16)[name = tensor("inputs_413_cast_fp16")]; + tensor input_811_axes_0 = const()[name = tensor("input_811_axes_0"), val = tensor([1])]; + tensor input_811_gamma_0_to_fp16 = const()[name = tensor("input_811_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904341248)))]; + tensor input_811_beta_0_to_fp16 = const()[name = tensor("input_811_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904342592)))]; + tensor var_13657_to_fp16 = const()[name = tensor("op_13657_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_811_cast_fp16 = layer_norm(axes = input_811_axes_0, beta = input_811_beta_0_to_fp16, epsilon = var_13657_to_fp16, gamma = input_811_gamma_0_to_fp16, x = inputs_413_cast_fp16)[name = tensor("input_811_cast_fp16")]; + tensor var_13673 = const()[name = tensor("op_13673"), val = tensor([1, 1])]; + tensor var_13675 = const()[name = tensor("op_13675"), val = tensor([1, 1])]; + tensor var_13677_pad_type_0 = const()[name = tensor("op_13677_pad_type_0"), val = tensor("custom")]; + tensor var_13677_pad_0 = const()[name = tensor("op_13677_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(1904343936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1906801600))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 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(1906801792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1906805696))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13677_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13675, groups = var_12548, pad = var_13677_pad_0, pad_type = var_13677_pad_type_0, strides = var_13673, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_811_cast_fp16)[name = tensor("op_13677_cast_fp16")]; + tensor var_13678_split_sizes_0 = const()[name = tensor("op_13678_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13678_axis_0 = const()[name = tensor("op_13678_axis_0"), val = tensor(1)]; + tensor var_13678_cast_fp16_0, tensor var_13678_cast_fp16_1 = split(axis = var_13678_axis_0, split_sizes = var_13678_split_sizes_0, x = var_13677_cast_fp16)[name = tensor("op_13678_cast_fp16")]; + tensor var_13680_mode_0 = const()[name = tensor("op_13680_mode_0"), val = tensor("EXACT")]; + tensor var_13680_cast_fp16 = gelu(mode = var_13680_mode_0, x = var_13678_cast_fp16_1)[name = tensor("op_13680_cast_fp16")]; + tensor input_813_cast_fp16 = mul(x = var_13678_cast_fp16_0, y = var_13680_cast_fp16)[name = tensor("input_813_cast_fp16")]; + tensor var_13684 = const()[name = tensor("op_13684"), val = tensor([1, 1])]; + tensor var_13686 = const()[name = tensor("op_13686"), val = tensor([1, 1])]; + tensor var_13688_pad_type_0 = const()[name = tensor("op_13688_pad_type_0"), val = tensor("custom")]; + tensor var_13688_pad_0 = const()[name = tensor("op_13688_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(1906805888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908034752))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 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(1908034944)))]; + tensor var_13688_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13686, groups = var_12548, pad = var_13688_pad_0, pad_type = var_13688_pad_type_0, strides = var_13684, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_813_cast_fp16)[name = tensor("op_13688_cast_fp16")]; + tensor inputs_415_cast_fp16 = add(x = var_13688_cast_fp16, y = inputs_413_cast_fp16)[name = tensor("inputs_415_cast_fp16")]; + tensor hidden_states_567_axes_0 = const()[name = tensor("hidden_states_567_axes_0"), val = tensor([1])]; + tensor hidden_states_567_gamma_0_to_fp16 = const()[name = tensor("hidden_states_567_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908036288)))]; + tensor hidden_states_567_beta_0_to_fp16 = const()[name = tensor("hidden_states_567_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908037632)))]; + tensor var_13704_to_fp16 = const()[name = tensor("op_13704_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_567_cast_fp16 = layer_norm(axes = hidden_states_567_axes_0, beta = hidden_states_567_beta_0_to_fp16, epsilon = var_13704_to_fp16, gamma = hidden_states_567_gamma_0_to_fp16, x = inputs_415_cast_fp16)[name = tensor("hidden_states_567_cast_fp16")]; + tensor var_13719 = const()[name = tensor("op_13719"), val = tensor([1, 1])]; + tensor var_13721 = const()[name = tensor("op_13721"), val = tensor([1, 1])]; + tensor q_277_pad_type_0 = const()[name = tensor("q_277_pad_type_0"), val = tensor("custom")]; + tensor q_277_pad_0 = const()[name = tensor("q_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908038976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908346240))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_277_cast_fp16 = conv(dilations = var_13721, groups = var_12548, pad = q_277_pad_0, pad_type = q_277_pad_type_0, strides = var_13719, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_567_cast_fp16)[name = tensor("q_277_cast_fp16")]; + tensor var_13725 = const()[name = tensor("op_13725"), val = tensor([1, 1])]; + tensor var_13727 = const()[name = tensor("op_13727"), val = tensor([1, 1])]; + tensor k_277_pad_type_0 = const()[name = tensor("k_277_pad_type_0"), val = tensor("custom")]; + tensor k_277_pad_0 = const()[name = tensor("k_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908346432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908653696))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_277_cast_fp16 = conv(dilations = var_13727, groups = var_12548, pad = k_277_pad_0, pad_type = k_277_pad_type_0, strides = var_13725, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_567_cast_fp16)[name = tensor("k_277_cast_fp16")]; + tensor var_13731 = const()[name = tensor("op_13731"), val = tensor([1, 1])]; + tensor var_13733 = const()[name = tensor("op_13733"), val = tensor([1, 1])]; + tensor v_277_pad_type_0 = const()[name = tensor("v_277_pad_type_0"), val = tensor("custom")]; + tensor v_277_pad_0 = const()[name = tensor("v_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908653888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908961152))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_277_cast_fp16 = conv(dilations = var_13733, groups = var_12548, pad = v_277_pad_0, pad_type = v_277_pad_type_0, strides = var_13731, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_567_cast_fp16)[name = tensor("v_277_cast_fp16")]; + tensor var_13737 = const()[name = tensor("op_13737"), val = tensor([1, 10, 64, -1])]; + tensor var_13738_cast_fp16 = reshape(shape = var_13737, x = q_277_cast_fp16)[name = tensor("op_13738_cast_fp16")]; + tensor var_13739 = const()[name = tensor("op_13739"), val = tensor([1, 10, 64, -1])]; + tensor var_13740_cast_fp16 = reshape(shape = var_13739, x = k_277_cast_fp16)[name = tensor("op_13740_cast_fp16")]; + tensor var_13741 = const()[name = tensor("op_13741"), val = tensor([1, 10, 64, -1])]; + tensor var_13742_cast_fp16 = reshape(shape = var_13741, x = v_277_cast_fp16)[name = tensor("op_13742_cast_fp16")]; + tensor attn_weights_553_transpose_x_0 = const()[name = tensor("attn_weights_553_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_553_transpose_y_0 = const()[name = tensor("attn_weights_553_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_553_cast_fp16 = matmul(transpose_x = attn_weights_553_transpose_x_0, transpose_y = attn_weights_553_transpose_y_0, x = var_13738_cast_fp16, y = var_13740_cast_fp16)[name = tensor("attn_weights_553_cast_fp16")]; + tensor attn_weights_555_cast_fp16 = mul(x = attn_weights_553_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_555_cast_fp16")]; + tensor var_13746_cast_fp16 = softmax(axis = var_12532, x = attn_weights_555_cast_fp16)[name = tensor("op_13746_cast_fp16")]; + tensor attn_277_transpose_x_0 = const()[name = tensor("attn_277_transpose_x_0"), val = tensor(false)]; + tensor attn_277_transpose_y_0 = const()[name = tensor("attn_277_transpose_y_0"), val = tensor(true)]; + tensor attn_277_cast_fp16 = matmul(transpose_x = attn_277_transpose_x_0, transpose_y = attn_277_transpose_y_0, x = var_13742_cast_fp16, y = var_13746_cast_fp16)[name = tensor("attn_277_cast_fp16")]; + tensor var_13750 = const()[name = tensor("op_13750"), val = tensor([1, 640, 1, -1])]; + tensor input_815_cast_fp16 = reshape(shape = var_13750, x = attn_277_cast_fp16)[name = tensor("input_815_cast_fp16")]; + tensor var_13755 = const()[name = tensor("op_13755"), val = tensor([1, 1])]; + tensor var_13757 = const()[name = tensor("op_13757"), val = tensor([1, 1])]; + tensor var_13759_pad_type_0 = const()[name = tensor("op_13759_pad_type_0"), val = tensor("custom")]; + tensor var_13759_pad_0 = const()[name = tensor("op_13759_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908961344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909268608))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909268800)))]; + tensor var_13759_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13757, groups = var_12548, pad = var_13759_pad_0, pad_type = var_13759_pad_type_0, strides = var_13755, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_815_cast_fp16)[name = tensor("op_13759_cast_fp16")]; + tensor inputs_417_cast_fp16 = add(x = var_13759_cast_fp16, y = inputs_415_cast_fp16)[name = tensor("inputs_417_cast_fp16")]; + tensor hidden_states_569_axes_0 = const()[name = tensor("hidden_states_569_axes_0"), val = tensor([1])]; + tensor hidden_states_569_gamma_0_to_fp16 = const()[name = tensor("hidden_states_569_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909270144)))]; + tensor hidden_states_569_beta_0_to_fp16 = const()[name = tensor("hidden_states_569_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909271488)))]; + tensor var_13769_to_fp16 = const()[name = tensor("op_13769_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_569_cast_fp16 = layer_norm(axes = hidden_states_569_axes_0, beta = hidden_states_569_beta_0_to_fp16, epsilon = var_13769_to_fp16, gamma = hidden_states_569_gamma_0_to_fp16, x = inputs_417_cast_fp16)[name = tensor("hidden_states_569_cast_fp16")]; + tensor var_13784 = const()[name = tensor("op_13784"), val = tensor([1, 1])]; + tensor var_13786 = const()[name = tensor("op_13786"), 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_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909272832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909580096))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_cast_fp16 = conv(dilations = var_13786, groups = var_12548, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_13784, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_569_cast_fp16)[name = tensor("q_cast_fp16")]; + tensor var_13790 = const()[name = tensor("op_13790"), val = tensor([1, 1])]; + tensor var_13792 = const()[name = tensor("op_13792"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909580288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1910563392))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_cast_fp16 = conv(dilations = var_13792, groups = var_12548, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_13790, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_cast_fp16")]; + tensor var_13796 = const()[name = tensor("op_13796"), val = tensor([1, 1])]; + tensor var_13798 = const()[name = tensor("op_13798"), 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_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1910563584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911546688))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_cast_fp16 = conv(dilations = var_13798, groups = var_12548, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_13796, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_cast_fp16")]; + tensor var_13802 = const()[name = tensor("op_13802"), val = tensor([1, 10, 64, -1])]; + tensor var_13803_cast_fp16 = reshape(shape = var_13802, x = q_cast_fp16)[name = tensor("op_13803_cast_fp16")]; + tensor var_13804 = const()[name = tensor("op_13804"), val = tensor([1, 10, 64, -1])]; + tensor var_13805_cast_fp16 = reshape(shape = var_13804, x = k_cast_fp16)[name = tensor("op_13805_cast_fp16")]; + tensor var_13806 = const()[name = tensor("op_13806"), val = tensor([1, 10, 64, -1])]; + tensor var_13807_cast_fp16 = reshape(shape = var_13806, x = v_cast_fp16)[name = tensor("op_13807_cast_fp16")]; + tensor attn_weights_557_transpose_x_0 = const()[name = tensor("attn_weights_557_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_557_transpose_y_0 = const()[name = tensor("attn_weights_557_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_557_cast_fp16 = matmul(transpose_x = attn_weights_557_transpose_x_0, transpose_y = attn_weights_557_transpose_y_0, x = var_13803_cast_fp16, y = var_13805_cast_fp16)[name = tensor("attn_weights_557_cast_fp16")]; + tensor attn_weights_cast_fp16 = mul(x = attn_weights_557_cast_fp16, y = var_12539_to_fp16)[name = tensor("attn_weights_cast_fp16")]; + tensor var_13811_cast_fp16 = softmax(axis = var_12532, x = attn_weights_cast_fp16)[name = tensor("op_13811_cast_fp16")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_13807_cast_fp16, y = var_13811_cast_fp16)[name = tensor("attn_cast_fp16")]; + tensor var_13815 = const()[name = tensor("op_13815"), val = tensor([1, 640, 1, -1])]; + tensor input_817_cast_fp16 = reshape(shape = var_13815, x = attn_cast_fp16)[name = tensor("input_817_cast_fp16")]; + tensor var_13820 = const()[name = tensor("op_13820"), val = tensor([1, 1])]; + tensor var_13822 = const()[name = tensor("op_13822"), val = tensor([1, 1])]; + tensor var_13824_pad_type_0 = const()[name = tensor("op_13824_pad_type_0"), val = tensor("custom")]; + tensor var_13824_pad_0 = const()[name = tensor("op_13824_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911546880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911854144))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911854336)))]; + tensor var_13824_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13822, groups = var_12548, pad = var_13824_pad_0, pad_type = var_13824_pad_type_0, strides = var_13820, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_817_cast_fp16)[name = tensor("op_13824_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = var_13824_cast_fp16, y = inputs_417_cast_fp16)[name = tensor("inputs_cast_fp16")]; + tensor input_819_axes_0 = const()[name = tensor("input_819_axes_0"), val = tensor([1])]; + tensor input_819_gamma_0_to_fp16 = const()[name = tensor("input_819_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911855680)))]; + tensor input_819_beta_0_to_fp16 = const()[name = tensor("input_819_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911857024)))]; + tensor var_13834_to_fp16 = const()[name = tensor("op_13834_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_819_cast_fp16 = layer_norm(axes = input_819_axes_0, beta = input_819_beta_0_to_fp16, epsilon = var_13834_to_fp16, gamma = input_819_gamma_0_to_fp16, x = inputs_cast_fp16)[name = tensor("input_819_cast_fp16")]; + tensor var_13850 = const()[name = tensor("op_13850"), val = tensor([1, 1])]; + tensor var_13852 = const()[name = tensor("op_13852"), val = tensor([1, 1])]; + tensor var_13854_pad_type_0 = const()[name = tensor("op_13854_pad_type_0"), val = tensor("custom")]; + tensor var_13854_pad_0 = const()[name = tensor("op_13854_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911858368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914316032))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914316224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914320128))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13854_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13852, groups = var_12548, pad = var_13854_pad_0, pad_type = var_13854_pad_type_0, strides = var_13850, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = tensor("op_13854_cast_fp16")]; + tensor var_13855_split_sizes_0 = const()[name = tensor("op_13855_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13855_axis_0 = const()[name = tensor("op_13855_axis_0"), val = tensor(1)]; + tensor var_13855_cast_fp16_0, tensor var_13855_cast_fp16_1 = split(axis = var_13855_axis_0, split_sizes = var_13855_split_sizes_0, x = var_13854_cast_fp16)[name = tensor("op_13855_cast_fp16")]; + tensor var_13857_mode_0 = const()[name = tensor("op_13857_mode_0"), val = tensor("EXACT")]; + tensor var_13857_cast_fp16 = gelu(mode = var_13857_mode_0, x = var_13855_cast_fp16_1)[name = tensor("op_13857_cast_fp16")]; + tensor input_821_cast_fp16 = mul(x = var_13855_cast_fp16_0, y = var_13857_cast_fp16)[name = tensor("input_821_cast_fp16")]; + tensor var_13861 = const()[name = tensor("op_13861"), val = tensor([1, 1])]; + tensor var_13863 = const()[name = tensor("op_13863"), val = tensor([1, 1])]; + tensor var_13865_pad_type_0 = const()[name = tensor("op_13865_pad_type_0"), val = tensor("custom")]; + tensor var_13865_pad_0 = const()[name = tensor("op_13865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914320320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915549184))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915549376)))]; + tensor var_13865_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13863, groups = var_12548, pad = var_13865_pad_0, pad_type = var_13865_pad_type_0, strides = var_13861, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_821_cast_fp16)[name = tensor("op_13865_cast_fp16")]; + tensor hidden_states_573_cast_fp16 = add(x = var_13865_cast_fp16, y = inputs_cast_fp16)[name = tensor("hidden_states_573_cast_fp16")]; + tensor var_13867 = const()[name = tensor("op_13867"), val = tensor([1, 640, 64, 64])]; + tensor input_823_cast_fp16 = reshape(shape = var_13867, x = hidden_states_573_cast_fp16)[name = tensor("input_823_cast_fp16")]; + tensor var_13871 = const()[name = tensor("op_13871"), val = tensor([1, 1])]; + tensor var_13873 = const()[name = tensor("op_13873"), val = tensor([1, 1])]; + tensor hidden_states_575_pad_type_0 = const()[name = tensor("hidden_states_575_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_575_pad_0 = const()[name = tensor("hidden_states_575_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(1915550720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915857984))), name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1915858176)))]; + tensor hidden_states_575_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_13873, groups = var_12548, pad = hidden_states_575_pad_0, pad_type = hidden_states_575_pad_type_0, strides = var_13871, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized, x = input_823_cast_fp16)[name = tensor("hidden_states_575_cast_fp16")]; + tensor input_825_cast_fp16 = add(x = hidden_states_575_cast_fp16, y = hidden_states_557_cast_fp16)[name = tensor("input_825_cast_fp16")]; + tensor input_827_scale_factor_height_0 = const()[name = tensor("input_827_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_827_scale_factor_width_0 = const()[name = tensor("input_827_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_827_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_827_scale_factor_height_0, scale_factor_width = input_827_scale_factor_width_0, x = input_825_cast_fp16)[name = tensor("input_827_cast_fp16")]; + tensor var_13882 = const()[name = tensor("op_13882"), val = tensor([1, 1])]; + tensor var_13884 = const()[name = tensor("op_13884"), val = tensor([1, 1])]; + tensor hidden_states_577_pad_type_0 = const()[name = tensor("hidden_states_577_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_577_pad_0 = const()[name = tensor("hidden_states_577_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(1915859520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1918624384))), name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 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(1918624576)))]; + tensor hidden_states_577_cast_fp16 = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_13884, groups = var_12548, pad = hidden_states_577_pad_0, pad_type = hidden_states_577_pad_type_0, strides = var_13882, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized, x = input_827_cast_fp16)[name = tensor("hidden_states_577_cast_fp16")]; + tensor var_13892 = const()[name = tensor("op_13892"), val = tensor(1)]; + tensor input_829_interleave_0 = const()[name = tensor("input_829_interleave_0"), val = tensor(false)]; + tensor input_829_cast_fp16 = concat(axis = var_13892, interleave = input_829_interleave_0, values = (hidden_states_577_cast_fp16, res_hidden_states_13_cast_fp16))[name = tensor("input_829_cast_fp16")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([1, 32, 30, 128, 128])]; + tensor reshape_156_cast_fp16 = reshape(shape = reshape_156_shape_0, x = input_829_cast_fp16)[name = tensor("reshape_156_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast_fp16)[name = tensor("reduce_mean_117_cast_fp16")]; + tensor sub_78_cast_fp16 = sub(x = reshape_156_cast_fp16, y = reduce_mean_117_cast_fp16)[name = tensor("sub_78_cast_fp16")]; + tensor square_39_cast_fp16 = square(x = sub_78_cast_fp16)[name = tensor("square_39_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast_fp16)[name = tensor("reduce_mean_119_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_119_cast_fp16, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast_fp16")]; + tensor sqrt_39_cast_fp16 = sqrt(x = add_78_cast_fp16)[name = tensor("sqrt_39_cast_fp16")]; + tensor real_div_39_cast_fp16 = real_div(x = sub_78_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor("real_div_39_cast_fp16")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([1, 960, 128, 128])]; + tensor reshape_157_cast_fp16 = reshape(shape = reshape_157_shape_0, x = real_div_39_cast_fp16)[name = tensor("reshape_157_cast_fp16")]; + 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(1918625920)))]; + 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(1918627904)))]; + 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_fp16 = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_157_cast_fp16)[name = tensor("add_79_cast_fp16")]; + tensor input_833_cast_fp16 = silu(x = add_79_cast_fp16)[name = tensor("input_833_cast_fp16")]; + tensor var_13913 = const()[name = tensor("op_13913"), val = tensor([1, 1])]; + tensor var_13915 = const()[name = tensor("op_13915"), val = tensor([1, 1])]; + tensor hidden_states_579_pad_type_0 = const()[name = tensor("hidden_states_579_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_579_pad_0 = const()[name = tensor("hidden_states_579_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(1918629888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1920703552))), name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 960, 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(1920703744)))]; + tensor hidden_states_579_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_13915, groups = var_13892, pad = hidden_states_579_pad_0, pad_type = hidden_states_579_pad_type_0, strides = var_13913, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_833_cast_fp16)[name = tensor("hidden_states_579_cast_fp16")]; + tensor var_13921 = const()[name = tensor("op_13921"), val = tensor([1, 1])]; + tensor var_13923 = const()[name = tensor("op_13923"), 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_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(1920704448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921011712))), name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 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(1921011904)))]; + tensor temb_29_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_13923, groups = var_13892, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_13921, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_29_cast_fp16")]; + tensor input_837_cast_fp16 = add(x = hidden_states_579_cast_fp16, y = temb_29_cast_fp16)[name = tensor("input_837_cast_fp16")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_160_cast_fp16 = reshape(shape = reshape_160_shape_0, x = input_837_cast_fp16)[name = tensor("reshape_160_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast_fp16)[name = tensor("reduce_mean_120_cast_fp16")]; + tensor sub_80_cast_fp16 = sub(x = reshape_160_cast_fp16, y = reduce_mean_120_cast_fp16)[name = tensor("sub_80_cast_fp16")]; + tensor square_40_cast_fp16 = square(x = sub_80_cast_fp16)[name = tensor("square_40_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast_fp16)[name = tensor("reduce_mean_122_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_122_cast_fp16, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast_fp16")]; + tensor sqrt_40_cast_fp16 = sqrt(x = add_80_cast_fp16)[name = tensor("sqrt_40_cast_fp16")]; + tensor real_div_40_cast_fp16 = real_div(x = sub_80_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor("real_div_40_cast_fp16")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_161_cast_fp16 = reshape(shape = reshape_161_shape_0, x = real_div_40_cast_fp16)[name = tensor("reshape_161_cast_fp16")]; + 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(1921012608)))]; + 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(1921013312)))]; + 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_fp16 = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_161_cast_fp16)[name = tensor("add_81_cast_fp16")]; + tensor input_841_cast_fp16 = silu(x = add_81_cast_fp16)[name = tensor("input_841_cast_fp16")]; + tensor var_13933 = const()[name = tensor("op_13933"), val = tensor([1, 1])]; + tensor var_13935 = const()[name = tensor("op_13935"), val = tensor([1, 1])]; + tensor hidden_states_581_pad_type_0 = const()[name = tensor("hidden_states_581_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_581_pad_0 = const()[name = tensor("hidden_states_581_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(1921014016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921705280))), name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 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(1921705472)))]; + tensor hidden_states_581_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_13935, groups = var_13892, pad = hidden_states_581_pad_0, pad_type = hidden_states_581_pad_type_0, strides = var_13933, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_841_cast_fp16)[name = tensor("hidden_states_581_cast_fp16")]; + tensor var_13940 = const()[name = tensor("op_13940"), val = tensor([1, 1])]; + tensor var_13942 = const()[name = tensor("op_13942"), 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(1921706176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921936640))), name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 960, 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(1921936832)))]; + tensor x_17_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_13942, groups = var_13892, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_13940, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_829_cast_fp16)[name = tensor("x_17_cast_fp16")]; + tensor hidden_states_583_cast_fp16 = add(x = x_17_cast_fp16, y = hidden_states_581_cast_fp16)[name = tensor("hidden_states_583_cast_fp16")]; + tensor input_843_interleave_0 = const()[name = tensor("input_843_interleave_0"), val = tensor(false)]; + tensor input_843_cast_fp16 = concat(axis = var_13892, interleave = input_843_interleave_0, values = (hidden_states_583_cast_fp16, res_hidden_states_15_cast_fp16))[name = tensor("input_843_cast_fp16")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([1, 32, 20, 128, 128])]; + tensor reshape_164_cast_fp16 = reshape(shape = reshape_164_shape_0, x = input_843_cast_fp16)[name = tensor("reshape_164_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast_fp16)[name = tensor("reduce_mean_123_cast_fp16")]; + tensor sub_82_cast_fp16 = sub(x = reshape_164_cast_fp16, y = reduce_mean_123_cast_fp16)[name = tensor("sub_82_cast_fp16")]; + tensor square_41_cast_fp16 = square(x = sub_82_cast_fp16)[name = tensor("square_41_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast_fp16)[name = tensor("reduce_mean_125_cast_fp16")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_82_cast_fp16 = add(x = reduce_mean_125_cast_fp16, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast_fp16")]; + tensor sqrt_41_cast_fp16 = sqrt(x = add_82_cast_fp16)[name = tensor("sqrt_41_cast_fp16")]; + tensor real_div_41_cast_fp16 = real_div(x = sub_82_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor("real_div_41_cast_fp16")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([1, 640, 128, 128])]; + tensor reshape_165_cast_fp16 = reshape(shape = reshape_165_shape_0, x = real_div_41_cast_fp16)[name = tensor("reshape_165_cast_fp16")]; + 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(1921937536)))]; + 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(1921938880)))]; + 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_fp16 = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_165_cast_fp16)[name = tensor("add_83_cast_fp16")]; + tensor input_847_cast_fp16 = silu(x = add_83_cast_fp16)[name = tensor("input_847_cast_fp16")]; + tensor var_13960 = const()[name = tensor("op_13960"), val = tensor([1, 1])]; + tensor var_13962 = const()[name = tensor("op_13962"), val = tensor([1, 1])]; + tensor hidden_states_585_pad_type_0 = const()[name = tensor("hidden_states_585_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_585_pad_0 = const()[name = tensor("hidden_states_585_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(1921940224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923322688))), name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 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(1923322880)))]; + tensor hidden_states_585_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_13962, groups = var_13892, pad = hidden_states_585_pad_0, pad_type = hidden_states_585_pad_type_0, strides = var_13960, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_847_cast_fp16)[name = tensor("hidden_states_585_cast_fp16")]; + tensor var_13968 = const()[name = tensor("op_13968"), val = tensor([1, 1])]; + tensor var_13970 = const()[name = tensor("op_13970"), 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_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(1923323584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923630848))), name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 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(1923631040)))]; + tensor temb_31_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_13970, groups = var_13892, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_13968, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_31_cast_fp16")]; + tensor input_851_cast_fp16 = add(x = hidden_states_585_cast_fp16, y = temb_31_cast_fp16)[name = tensor("input_851_cast_fp16")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_168_cast_fp16 = reshape(shape = reshape_168_shape_0, x = input_851_cast_fp16)[name = tensor("reshape_168_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast_fp16)[name = tensor("reduce_mean_126_cast_fp16")]; + tensor sub_84_cast_fp16 = sub(x = reshape_168_cast_fp16, y = reduce_mean_126_cast_fp16)[name = tensor("sub_84_cast_fp16")]; + tensor square_42_cast_fp16 = square(x = sub_84_cast_fp16)[name = tensor("square_42_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast_fp16)[name = tensor("reduce_mean_128_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_128_cast_fp16, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast_fp16")]; + tensor sqrt_42_cast_fp16 = sqrt(x = add_84_cast_fp16)[name = tensor("sqrt_42_cast_fp16")]; + tensor real_div_42_cast_fp16 = real_div(x = sub_84_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor("real_div_42_cast_fp16")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_169_cast_fp16 = reshape(shape = reshape_169_shape_0, x = real_div_42_cast_fp16)[name = tensor("reshape_169_cast_fp16")]; + 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(1923631744)))]; + 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(1923632448)))]; + 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_fp16 = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_169_cast_fp16)[name = tensor("add_85_cast_fp16")]; + tensor input_855_cast_fp16 = silu(x = add_85_cast_fp16)[name = tensor("input_855_cast_fp16")]; + tensor var_13980 = const()[name = tensor("op_13980"), val = tensor([1, 1])]; + tensor var_13982 = const()[name = tensor("op_13982"), val = tensor([1, 1])]; + tensor hidden_states_587_pad_type_0 = const()[name = tensor("hidden_states_587_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_587_pad_0 = const()[name = tensor("hidden_states_587_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(1923633152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924324416))), name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 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(1924324608)))]; + tensor hidden_states_587_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_13982, groups = var_13892, pad = hidden_states_587_pad_0, pad_type = hidden_states_587_pad_type_0, strides = var_13980, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_855_cast_fp16)[name = tensor("hidden_states_587_cast_fp16")]; + tensor var_13987 = const()[name = tensor("op_13987"), val = tensor([1, 1])]; + tensor var_13989 = const()[name = tensor("op_13989"), 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(1924325312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924478976))), name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 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(1924479168)))]; + tensor x_19_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13989, groups = var_13892, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_13987, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_843_cast_fp16)[name = tensor("x_19_cast_fp16")]; + tensor hidden_states_589_cast_fp16 = add(x = x_19_cast_fp16, y = hidden_states_587_cast_fp16)[name = tensor("hidden_states_589_cast_fp16")]; + tensor input_857_interleave_0 = const()[name = tensor("input_857_interleave_0"), val = tensor(false)]; + tensor input_857_cast_fp16 = concat(axis = var_13892, interleave = input_857_interleave_0, values = (hidden_states_589_cast_fp16, res_hidden_states_cast_fp16))[name = tensor("input_857_cast_fp16")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([1, 32, 20, 128, 128])]; + tensor reshape_172_cast_fp16 = reshape(shape = reshape_172_shape_0, x = input_857_cast_fp16)[name = tensor("reshape_172_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast_fp16)[name = tensor("reduce_mean_129_cast_fp16")]; + tensor sub_86_cast_fp16 = sub(x = reshape_172_cast_fp16, y = reduce_mean_129_cast_fp16)[name = tensor("sub_86_cast_fp16")]; + tensor square_43_cast_fp16 = square(x = sub_86_cast_fp16)[name = tensor("square_43_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast_fp16)[name = tensor("reduce_mean_131_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_131_cast_fp16, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast_fp16")]; + tensor sqrt_43_cast_fp16 = sqrt(x = add_86_cast_fp16)[name = tensor("sqrt_43_cast_fp16")]; + tensor real_div_43_cast_fp16 = real_div(x = sub_86_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor("real_div_43_cast_fp16")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([1, 640, 128, 128])]; + tensor reshape_173_cast_fp16 = reshape(shape = reshape_173_shape_0, x = real_div_43_cast_fp16)[name = tensor("reshape_173_cast_fp16")]; + 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(1924479872)))]; + 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(1924481216)))]; + 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_fp16 = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_173_cast_fp16)[name = tensor("add_87_cast_fp16")]; + tensor input_861_cast_fp16 = silu(x = add_87_cast_fp16)[name = tensor("input_861_cast_fp16")]; + tensor var_14007 = const()[name = tensor("op_14007"), val = tensor([1, 1])]; + tensor var_14009 = const()[name = tensor("op_14009"), val = tensor([1, 1])]; + tensor hidden_states_591_pad_type_0 = const()[name = tensor("hidden_states_591_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_591_pad_0 = const()[name = tensor("hidden_states_591_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(1924482560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1925865024))), name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 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(1925865216)))]; + tensor hidden_states_591_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_14009, groups = var_13892, pad = hidden_states_591_pad_0, pad_type = hidden_states_591_pad_type_0, strides = var_14007, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized, x = input_861_cast_fp16)[name = tensor("hidden_states_591_cast_fp16")]; + tensor var_14015 = const()[name = tensor("op_14015"), val = tensor([1, 1])]; + tensor var_14017 = const()[name = tensor("op_14017"), 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_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(1925865920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926173184))), name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 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(1926173376)))]; + tensor temb_cast_fp16 = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_14017, groups = var_13892, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_14015, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor("temb_cast_fp16")]; + tensor input_865_cast_fp16 = add(x = hidden_states_591_cast_fp16, y = temb_cast_fp16)[name = tensor("input_865_cast_fp16")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_176_cast_fp16 = reshape(shape = reshape_176_shape_0, x = input_865_cast_fp16)[name = tensor("reshape_176_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast_fp16)[name = tensor("reduce_mean_132_cast_fp16")]; + tensor sub_88_cast_fp16 = sub(x = reshape_176_cast_fp16, y = reduce_mean_132_cast_fp16)[name = tensor("sub_88_cast_fp16")]; + tensor square_44_cast_fp16 = square(x = sub_88_cast_fp16)[name = tensor("square_44_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast_fp16)[name = tensor("reduce_mean_134_cast_fp16")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_88_cast_fp16 = add(x = reduce_mean_134_cast_fp16, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast_fp16")]; + tensor sqrt_44_cast_fp16 = sqrt(x = add_88_cast_fp16)[name = tensor("sqrt_44_cast_fp16")]; + tensor real_div_44_cast_fp16 = real_div(x = sub_88_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor("real_div_44_cast_fp16")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_177_cast_fp16 = reshape(shape = reshape_177_shape_0, x = real_div_44_cast_fp16)[name = tensor("reshape_177_cast_fp16")]; + 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(1926174080)))]; + 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(1926174784)))]; + 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_fp16 = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_177_cast_fp16)[name = tensor("add_89_cast_fp16")]; + tensor input_869_cast_fp16 = silu(x = add_89_cast_fp16)[name = tensor("input_869_cast_fp16")]; + tensor var_14027 = const()[name = tensor("op_14027"), val = tensor([1, 1])]; + tensor var_14029 = const()[name = tensor("op_14029"), 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([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(1926175488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926866752))), name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 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(1926866944)))]; + tensor hidden_states_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_14029, groups = var_13892, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_14027, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized, x = input_869_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor var_14034 = const()[name = tensor("op_14034"), val = tensor([1, 1])]; + tensor var_14036 = const()[name = tensor("op_14036"), 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_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(1926867648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927021312))), name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 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(1927021504)))]; + tensor x_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_14036, groups = var_13892, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_14034, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_857_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor input_871_cast_fp16 = add(x = x_cast_fp16, y = hidden_states_cast_fp16)[name = tensor("input_871_cast_fp16")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_180_cast_fp16 = reshape(shape = reshape_180_shape_0, x = input_871_cast_fp16)[name = tensor("reshape_180_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast_fp16)[name = tensor("reduce_mean_135_cast_fp16")]; + tensor sub_90_cast_fp16 = sub(x = reshape_180_cast_fp16, y = reduce_mean_135_cast_fp16)[name = tensor("sub_90_cast_fp16")]; + tensor square_45_cast_fp16 = square(x = sub_90_cast_fp16)[name = tensor("square_45_cast_fp16")]; + 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_fp16 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast_fp16)[name = tensor("reduce_mean_137_cast_fp16")]; + 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_fp16 = add(x = reduce_mean_137_cast_fp16, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast_fp16")]; + tensor sqrt_45_cast_fp16 = sqrt(x = add_90_cast_fp16)[name = tensor("sqrt_45_cast_fp16")]; + tensor real_div_45_cast_fp16 = real_div(x = sub_90_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor("real_div_45_cast_fp16")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_181_cast_fp16 = reshape(shape = reshape_181_shape_0, x = real_div_45_cast_fp16)[name = tensor("reshape_181_cast_fp16")]; + 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(1927022208)))]; + 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(1927022912)))]; + 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_fp16 = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_181_cast_fp16)[name = tensor("add_91_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_91_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_14050 = const()[name = tensor("op_14050"), val = tensor(1)]; + tensor var_14053 = const()[name = tensor("op_14053"), val = tensor([1, 1])]; + tensor var_14055 = const()[name = tensor("op_14055"), val = tensor([1, 1])]; + tensor var_14057_pad_type_0 = const()[name = tensor("op_14057_pad_type_0"), val = tensor("custom")]; + tensor var_14057_pad_0 = const()[name = tensor("op_14057_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(1927023616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927032320))), 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.c08p-9, -0x1.754p-10, 0x1.06p-9, -0x1.9d4p-9])]; + tensor var_14057_cast_fp16 = conv(bias = conv_out_bias_to_fp16, dilations = var_14055, groups = var_14050, pad = var_14057_pad_0, pad_type = var_14057_pad_type_0, strides = var_14053, weight = conv_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = tensor("op_14057_cast_fp16")]; + tensor var_14057_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_14057_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_14057_cast_fp16_to_fp32_dtype_0, x = var_14057_cast_fp16)[name = tensor("cast_0")]; + } -> (noise_pred); +} \ No newline at end of file